In my recent article on Exploring Artificial Intelligence, I covered several dimensions of how I think about the direction of AI, including how models will evolve from general-purpose and broad-based to be more value-focused and end consumer-specific as the above diagram is intended to illustrate.
The purpose of this article is to dive a little deeper into a mental model for how I believe the technology could become more relevant and valuable in an end-user (or end consumer) specific context.
Before that, a few assertions related to the technology and end user application of the technology:
The more we can passively collect data in the interest of simplifying end-user tasks and informing models, the better. People can be both inconsistent and unreliable in how they capture data. In reality, our cell phones are collecting massive amounts of data on an ongoing basis that is used to drive targeted advertising and other capabilities to us without our involvement. In a business context, however, the concept of doing so can be met with significant privacy and other concerns and it’s a shame because, while there is data being collected on our devices regardless, we aren’t able to benefit from it in the context of doing our work
Moving from a broad- or persona-based means of delivering technology capabilities to a consumer-specific approach is a potentially significant advancement in enabling productivity and effectiveness. This would be difficult or impossible to achieve without leveraging an adaptive approach that synthesizes various technologies (personalization, customization, dynamic code generation, role-based access control, AI/ML models, LLMs, content management, and so forth) to create a more cohesive and personalized user experience
While I am largely focusing on end-user application of the technology, I would argue that the same concepts and approach could be leveraged for the next generation of intelligent devices and digital equipment, such as robotics in factory automation scenarios
To make the technology both performant and relevant, part of the design challenge is to continually reduce and refine to level of “model” information that is needed at the next layer of processing so as not to overload the end computing device (presumably a cell phone or tablet) with a volume of data that isn’t required to enable effective action on behalf of the data consumer.
The rest of this article will focus on providing a mental model for how to think about the relationship across the various kinds of models that may make up the future state of AI.
Starting with a “Real World” example
Having spent a good portion of my time off traveling across the U.S., while I had a printed road atlas in my car, I was reminded of the trust I place in Google Maps more than once, particularly when driving through an “open range” gravel road with cattle roaming about in northwest Nebraska on my way to South Dakota. In many ways, navigation software represents a good starting point for where I believe intelligent applications will eventually go in the business environment.
Maps is useful as a tool because it synthesizes what data is has on roads and navigation options with specific information like my chosen destination, location, speed traps, delays, and accident information that is specific to my potential routes, allowing for a level of customization if I prefer to take routes that avoid tolls and so on. From an end-user perspective, it provides a next recommended action, remaining contextually relevant to where I am and what I need to do, along with how long it will be both until that action needs to be taken as well as the distance remaining and time I should arrive at my final destination.
In a connected setting, navigation software pulls pieces of its overall model and applies data on where I am and where I’m going, to (ideally) help me get where I’m going as efficiently as possible. The application is useful because it is specific to me, to my destination, and to my preferred route, and is different than what would be delivered to a car immediately behind me, despite leveraging the same application and infrastructure. This is the direction I believe we need to go with intelligent applications, to drive individual productivity and effectiveness.
Introducing the “Tree of Knowledge” concept
The Overall Model
The visual above is meant to represent the relationship of general-purpose and foundational models to what ultimately are delivered to an end-user (or piece of digital equipment) in a distributed fashion.
Conceptually, I think of the relationship across data sets as if it were a tree.
The general-purpose model (e.g., LLM) provides the trunk that establishes a foundation for downstream analytics
Domain-specific models (e.g., RAG) act as the branches that rely on the base model (i.e., the trunk) to provide process- or function-specific capabilities that can span a number of end-user applications, but have specific, targeted outcomes in mind
A “micro”-model is created when specific branches of the tree are deployed to an end-user based on their profile. This represents the subset that is relevant to that data consumer given their role, permissions, experience level, etc.
The data available at the end point (e.g., mobile device) then provides the leaves that populate the branches of the “micro”-models that have been deployed to create an adaptive model used to inform the end user and drive meaningful and productive action.
The adaptive model should also take into account user preferences (via customization options) and personalization to tune their experience as closely as possible to what they need and how they work.
In this way, the progression of models moves from general to very specific, end-user focused solutions that are contextualized with real-time data much the same as the navigation example above.
It is also worth noting that, in addition to delivering these capabilities, the mobile device (or endpoint) may collect and send data back to further inform and train the knowledge models by domain (e.g., process performance data) and potentially develop additional branches based on gaps that may surface in execution.
Applying the Model
Having set context on the overall approach, there are some notable differences from how these capabilities could create a different experience and level of productivity than today, namely:
Rather than delivering content and transactional capabilities based on an end-user’s role and persona(s), those capabilities would be deployed to a user’s device (the branches of the “micro”-model), but synthesized with other information (the “leaves”) like the user’s experience level, preferences, location, training needs, equipment information (in a manufacturing-type context), to generate an interface specific to them that continually evolves to optimize their individual productivity
As new capabilities (i.e., “branches”) are developed centrally, they could be deployed to targeted users and their individual experiences would adapt to incorporate in ways that work best for them and their given configuration, without having to relearn the underlying application(s)
Going Back to Navigation
On the last point above, a parallel example would be the introduction of weather information into navigation.
At least in Google Maps, while there are real-time elements like speed traps, traffic delays, and accidents factored into the application, there is currently no mechanism to recognize or warn end users about significant weather events that also may surface along the route. In practice, where severe weather is involved, this could represent safety risk to the traveler and, in the event that the model was adapted to include a “branch” for this kind of data, one would hope that the application would behave the same from an end-user standpoint, but with the additional capability integrated into the application.
Wrapping Up
Understanding that we’re still early in the exploration of how AI will change the way we work, I believe that defining a framework for how various types of models can integrate and work across purposes would enable significant value and productivity if designed effectively.
I hope the ideas were worth considering. Thanks for spending the time to read them. Feedback is welcome as always.
It’s impossible to scroll through a news feed and miss the energy surrounding AI and its potential to transform. The investment in technology as a strategic differentiator is encouraging to see, particularly as a person who thrives on change and innovation. It is, however, also concerning that the ways in which it is often described are reminiscent of other technology advances of the past… CRM, BigData, .com… where there was an immediate surge in spending without a clear set of outcomes in mind, operating approach, or business architecture established for how to leverage it effectively. Consequently, while a level of experimentation is always good in the interest of learning and exploring, a lot of money and time can be wasted (and technical debt created) without necessarily creating any meaningful business value through the process.
For the purposes of this article, I’m going to focus on five dimensions of AI and how I’m thinking about them:
Framing the Problem – Thinking about how AI will be used in practice
The Role of Compute – Considering the needs for processing moving forward
Revisiting Data Strategy – Putting AI in the context of the broader data landscape
Simplifying “Intelligence” – Exploring the end user impact of AI
Thinking About Multi-Cloud – Contemplating how to approach AI in a distributed environment
This topic is very extensive, so I’ll try to keep the thoughts at a relatively high-level to start and dive into more specifics in future articles as appropriate.
Framing the Problem
Considering the Range of Opportunities
While a lot of the attention surrounding Generative AI over the last year has been focused on content generation for research, communication, software development, and other purposes, I believe the focus for how AI can create business value will shift substantially to be more outcome-driven and directed at specific business problems. In this environment, smaller, more focused data sets (e.g., incorporating process, market, equipment, end user, and environmental data) will be analyzed to understand causal relationships in the interest of producing desired business outcomes (e.g., optimizing process efficiency, improving risk management, increasing safety) and content (e.g., just in time training, adaptive user experiences). Retrieval-Augmented Generation (RAG) models are an example of this today, with a purpose-built model leveraging a foundational large language model to establish context for a more problem-specific solution.
This is not to suggest that general purpose models will decline in utility, but rather that I believe those applications will be better understood, mature, and become integrated where they create the most value (in relatively short order). The focus will then shift towards areas where more direct business value can be obtained through an evolution of these technologies.
For that to occur, the fundamentals of business process analysis need to regain some momentum to overcome the ‘silver bullet’ mentality that seems largely prevalent with these technologies today. It is, once again, a rush towards “the cool versus the useful” towards my opening remark about how current AI discussions feel a lot like conversations at the start of the .com era, and the sooner we shift towards a disciplined approach to leveraging these technology advancements, the better.
The opportunity will be to look at how we can leverage what these models provide, in terms of understanding multi-dimensional relationships across large sets of data, but then extending the concept to become more deterministic in terms of what decisions under a given set of conditions are most likely to bring about desired outcomes (i.e., causal models). This is not where we are today, but is where I believe these technologies are meant to go in the near future. Ultimately, we don’t just want to produce content, we want to influence processes and business results with support from artificial intelligence.
As purpose-built models evolve, I believe there will be a base set of business insights that are made available across communities of end users, and then an emergence of secondary insights that are developed in a derivative fashion. In this way, rather than try to summit Mount Everest in a direct ascent, we will establish one of more layers of outcomes (analogous to having multiple base camps) that facilitate the eventual goal.
Takeaways
General purpose AI and large language models (LLMs) will continue to be important and become integrated with how we work and consume technology, but reach a plateau of usefulness fairly rapidly in the next year or so
Focus will shift towards integrating transactional, contextual, and process data with the intention of predicting business outcomes (causal AI) in a much more targeted way
The overall mindset will pivot from models that do everything to ones that do something much more specific, with a desired outcome in mind up front
The Role of Compute
Considering the Spectrum of Needs
Having set the context of general versus purpose-built AI and the desire to move from content to more outcome-focused intelligence, the question is what this means for computing. There is a significant amount of attention going to specialized processors (e.g., GPUs) at the moment, with the presumption that there are significant computing requirements to generate models based on large sets of data in a reasonable amount of time. That being said, while content-focused outcomes may be based on a large volume of data and only need to be refreshed on a periodic basis, the more we want AI to assist in the performance of day-to-day tasks, the more we need the insights to be produced on a near-real time basis and available on a mobile device or embedded on a piece of digital equipment.
Said differently, as our focus shifts to smaller and more specific business problems, so should the data sets involved, making it possible to develop purpose-built models on more “standard” computing platforms that are commercially available, where the models are refreshed on a more frequent basis, taking into account relevant environmental conditions, whether that’s production plans or equipment status in manufacturing, market conditions in financial services, or weather patterns or other risk factors in insurance.
The Argument for a “Micro-Model”
Assuming a purpose-built model can be developed with a particular business outcome or process in mind, where things could take an interesting leap forward would be extending those models into edge computing environments, like a digital worker in a manufacturing facility, where the specific end user knowledge and skills, geo-location, environmental conditions, equipment status could be fed into a purpose-built model and then extended to create a more adaptive model that provides a user-specific set of insights and instructions to drive productivity, safety, and effectiveness.
Ultimately, AI needs to be focused on the individual and run on something as accessible as a mobile device to truly realize it’s potential. The same would also be true for extending models that could be embedded within a piece of industrial equipment to run as part of a digital facility. That is beyond anything we can do today, it makes insights personalized and specific to an individual, and that concept holds a significant amount more business value than targeting a specific user group or persona from an application development standpoint. Said differently, the concept is similar to integrating personalization, workflow, presentation, and insights into one integrated technology.
With this in mind, perhaps the answer will ultimately still result in highly specialized computing, but before rushing in the direction of quantum computing and buying a significant number of GPUs, I’d definitely consider the ultimate outcome we want, which is to put the power of insights in the hands of end users in day-to-day activities, but being much more effective in what they are able to do. That is not a once-a-month refresh of a massive amount of data. It is a constantly evolving model that is based on learnings from the past, but the current realities and conditions of the moment and the specific individual taking action on those things.
Takeaways
Computing requirements will shift from centralized processing of large data volumes to smaller, curated data sets that are refreshed more often and targeted to specific business goals
Ultimately, the goal should be to enable end users with a highly personalized model that is focused on them, the tasks they need to accomplish, and the current conditions under which they are operating
Processing for artificial intelligence will therefore be distributed across a spectrum of environments from large scale centralized methods to distributed edge appliances and mobile devices
Revisiting Data Strategy
Business Implications of AI
The largest risk with artificial intelligence that I see today is no different than anything else in regards to data: the value of new technology is only as good as the underlying data quality, and that’s a business issue (for the most part).
Said differently, in the case of AI, if the underlying data sets upon which models are developed has data quality issues and there is a lack of data management and data governance in place, the inferences drawn will likely be of limited value.
Ultimately, the more we move from general purpose to purpose-built solutions, the ability to identify relevant and necessary data to be incorporated into a model can be a significant accelerator of value. This is because the “give me all the data” approach would likely both increase time to develop and produce models as well as introduce significant overhead in ensuring data quality and governance to confirm the usefulness of the resulting models.
If, as an example, I wanted to use AI to ingest all the training materials developed across a set of manufacturing facilities in the interest of synthesizing, standardizing, and optimizing them across an enterprise, the underlying quality of those materials becomes critically important in deriving the right outcomes. There may be out of date procedures, unique steps specific to a location, quality issues in some of the source content, etc. The technology itself doesn’t solve these issues. Arguably, a level of data wrangling or quality tools could be helpful in identifying and surfacing these issues, but the point is that data governance and curation are required before the infrastructure would produce the desired business outcomes.
Technology Implications of AI
As the diagram intends to indicate, whether AI lives as a set of intelligent agents that run as separate stove pipes in parallel with existing applications and data solutions, the direction for how things will evolve is an important element of data strategy to consider, particularly in a multi-cloud environment (something I’ll address in the final section).
As discussed in The Intelligent Enterprise, I believe that the eventual direction for AI (as we already see somewhat evidenced with Copilot in Microsoft Office 365), is to move from separate agents and data apps (“intelligent agents”) to having those capabilities integrated into the workflow of applications themselves (making them “intelligent applications”), where they can create the most overall value.
What this suggests to me is that transaction data from applications will make its way into models, and be exposed back into consuming applications via AI services. Whether the data ultimately moves into a common repository that can handle both the graph and relational data within the same data solution remains to be seen, but having personally developed an integrated object- and relational-database for a commercial software package thirty years ago at the start of my career, I can foresee that there may be benefits in thinking through the value of that kind of solution.
Where things get more complicated on an enterprise level is when you scale these concepts out. I will address the end user and multi-cloud aspects of this in the next two sections, but it’s critically important in data strategy to consider how too many point solutions in the AI domain could significantly increase cost and complexity (not to mention have negative quality consequences). As data sets and insights are meant to extend outside an individual application to cross-application and cross-ecosystem levels, the ways in which that data is stored, accessed, and exposed likely will become significant. My article on Perspective on Impact-Driven Analytics attempted to establish a layered approach to how to think about the data landscape, from production to consumption, that may provide a starting point for evaluating alternatives in this regard.
Takeaways
While AI provides new technology capabilities, business ownership / stewardship of data and the processes surrounding data quality, data management, and data governance are extremely critical in an AI-enabled world
As AI capabilities move within applications, the need to look across applications for additional insights and optimization opportunities will emerge. To the extent that can be designed and architected in a consistent approach, it will be significantly more cost-effective and create more value over time at an enterprise level
Experimentation is appropriate in the AI domain for the foreseeable future, but it is important to consider how these capabilities will ultimately become integrated with the application and data ecosystems in medium to larger organizations in the interest of getting the most long-term value from the investments
Simplifying “Intelligence”
Avoiding the Pitfalls of Introducing New Technologies to End Users
The diagram above is meant to help conceptualize what could ultimately occur if AI capabilities are introduced as various data apps or intelligent agents, running separate from applications versus becoming an integrated part of the way intelligent applications behave over time.
At an overall level, expecting users to arbitrate new capabilities without integrating them thoughtfully into the workflow and footprint that exists creates the conditions for significant change management and productivity issues. This is always true when introducing change, but the expectations associated with the disruptive potential of AI (at the moment) are quite high, and that could set the stage for disappointment if there isn’t a thoughtful design in place for how the solutions are meant to make the consumer more effective on a workflow and task level.
Takeaways
Intelligence capabilities will move inside applications rather than be adjacent to them, providing more of a “guided path” approach to end users
To the degree that “micro-models” are eventually in place, that could include making the presentation layer of applications personalized to the individual user based on their profile, experience level, role, and operating conditions
The role of “Intelligent Agents” will take on a higher-level, cross-application focus, which could be (as an example) optimizing notifications and alerts coming from various applications to a more thoughtful set of prioritized actions intended to maximize individual performance
Thinking About Multi-Cloud
Working Across Environments
With the introduction of AI capabilities at an enterprise level, the challenge becomes how to leverage and integrate these technologies, particularly given that data may exist across a number of hosted and cloud-based environments. For simplicity’s sake, I’m going to assume that any cloud capability required for data management and AI services can be extended to the edge (via containers), though that may not be fully true today.
At an overall level, as it becomes desirable to extend models to include data resident both in something like Microsoft Office 365 (running on Azure) and corporate transactional data (largely running in AWS if you look at market share today), the considerations and costs for moving data between platforms could be significant if not architected in a purposeful manner.
To that end, my suggestion is to look at business needs in one of three ways:
Those that can be addressed via a single cloud platform, in which case it would likely be appropriate to design and deliver solutions leveraging the AI capabilities available natively on that platform
To the extent a solution extends across multiple providers, it may be possible to look at layering the solutions such that each cloud platform performs a subset of the analysis, resulting in pre-processed data that could then be published to a centralized, enterprise cloud environment where the various data sets are pulled into a single enterprise model that is used to address the overall need
If a partitioning approach isn’t possible, then some level of cost, capability, and performance analysis would likely make sense to determine where data should reside to enable the necessary integrated models to be developed
Again, the point is to step back from individual solutions and projects to consider the enterprise strategy for how data will be managed, models will be developed, and deployed overall. The alternative approach of deploying too many point solutions could lead to considerable cost and complexity (i.e., technical debt) over time.
Takeaways
AI capabilities are already available on all the major cloud platforms. I believe they will reach relative parity from a capability standpoint in the foreseeable future, to the point that they shouldn’t be a primary consideration in how data and models are managed and deployed
The more the environment can be designed with standards in mind, modularity, integration, interoperability, and a level of composability, the better. Technology solutions will continue to be introduced that an organization will want to leverage without having to abandon or migrate everything that is already in place
It is extremely probably that AI models will be deployed across cloud platforms, so having a deliberate strategy for how to manage and facilitate this should be given consideration
A lack of overall multi-cloud strategy will likely create complexity and cost that may be difficult to unwind over time
Wrapping Up
If you’ve made it this far, thank you for taking the time, hopefully some of the concepts were thought provoking. In Excellence by Design, I talk about ‘Relentless Innovation’…
Admittedly, there is so much movement in this space, that it’s very possible some of what I’ve written is obsolete, obvious, far-fetched, or some combination of all of the above, but that’s also part of the point of sharing the ideas: to encourage the dialogue. My experience in technology over the last thirty-two years, especially with emerging capabilities like artificial intelligence, is that we can lose perspective on value creation in the rush to adopt something new and the tool becomes a proverbial hammer in search of a nail.
What would be far better is to envision a desired end state, identify what we’d really like to be able to do from a business capability standpoint, and then endeavor to make that happen with advanced technology. I do believe there is significant power in these capabilities for the organizations that leverage them effectively.
I hope the ideas were worth considering. Thanks for spending the time to read them. Feedback is welcome as always.
Having covered a couple future-oriented topics on Transforming Manufacturing and The Future of IT, I thought it would good to come back to where we are with Enterprise Architecture as a critical function for promoting excellence in IT.
Overall, there is a critical balance to be struck in technology strategy today: technology-driven capabilities are advancing faster than any organization can reasonably adopt and integrate them (as is the exposure in cyber security), even if you could, the change management issues you’d cause on end users would be highly disruptive, and thereby undermine your desired business outcomes, and, in practice rapidly evolving, sustainable change is the goal, not any one particular “implementation” of the latest thing. This is what Relentless Innovation is about, referenced in my article on Excellence by Design.
Connecting Architecture back to Strategy
In the article, Creating Value Through Strategy, I laid out a framework for thinking about IT strategy at an overall level that can be used to create some focal points for enterprise architecture efforts in practice, namely:
Innovate – leveraging technology advancements in ways that promote competitive advantage
Accelerate – increasing speed to market/value to be more responsive to changing needs
Optimize – improving the value/cost ratio to drive return on technology investments overall
Inspire – creating a workplace that promotes retention and enables the above objectives
Perform – ensuring reliability, security, and performance in the production environment
The remainder of this article will focus on how enterprise architecture (EA) plays a role in enabling each of these dimensions given the pace of change today.
Breaking it Down
Innovate
Adopting new technologies for maximum business advantage is certainly the desired end game in this dimension, but unless there is a very unique, one-off situation, the role of EA is fairly critical in making these advancements leverageable, scalable, and sustainable. It’s worth noting, by the way, that I’m specifically referring to “enterprise architecture” here, not “solution architecture”, which I would consider to be the architecture and design of a specific business solution. One should not exist without the other and, to the degree that solution architecture is emphasized without a governing enterprise architecture framework in place, the probability of significant technical debt, delivery issues, lack of reliability, and a host of other issues will skyrocket.
Where EA plays a role in promoting innovation is minimally in exploring market trends and looking for enabling technologies that can promote competitive advantage, but also, and very critically in establishing the standards and guidelines by which new technologies should be introduced and integrated into the existing environment.
Using a “modern” example, I’ve seen a number of articles of late on the role of GenAI in “replacing” or “disrupting” application development, from the low-code/no code type solutions to the SaaS/package software domain, to everywhere. While this sounds great in theory, it shouldn’t take long for the enterprise architecture questions to surface:
How do I integrate that accumulated set of “point solutions” in any standard way?
How do I meaningfully run analytics on the data associated with these applications?
How do I secure these applications in a way that I’m not exposed to vulnerabilities that I would with any open-source technology (i.e., they are generated by an engine that may have inherent security gaps)?
How do I manage the interoperability between these internally-developed/generated solutions and standard packages (ERP, CRM, etc.) that are likely a core part of any sizeable IT environment?
In the above example, even if I find way to replace existing low-code/no code solutions with a new technology, it doesn’t mean that I don’t have the same challenges as exist with leveraging those technologies today.
In the case of innovation, the highest priorities for EA are therefore: looking for new disruptive technologies in the market, defining standards to enable their effective introduction and use, and then governing that delivery process to ensure standards are followed in practice.
Accelerate
Speed to market is a pressing reality in any environment I’ve seen, though it can lead to negative consequences as I discussed in Fast and Cheap… Isn’t Good. Certainly, one of the largest barriers to speed is complexity, and complexity can come in many forms depending on the makeup of the overall IT landscape, the standards, processes, and governance in place related to delivery, and the diversity in solutions, tools, and technologies that are involved in the ecosystem as a whole.
While I talk about standards, reuse, and governance in the broader article on IT strategy, I would argue that the largest priority for EA in terms of accelerating delivery is in rationalization of solutions, tools, and technologies in use overall.
The more diverse the enterprise ecosystem is, the more difficult it becomes to add, replace, or integrate new solutions over time, and ultimately this will slow delivery efforts down to a snail’s pace (not to mention making them much more expensive and higher risk over time).
Using an example of a company that has performed many acquisitions over time, looking for opportunities to simplify and standardize core systems (e.g., moving to a single ERP versus having multiple instances and running consolidations through a separate tool) can lead to significant reduction in complexity over time, not to mention making it possible to redeploy resources to new capability development versus being spread across multiple redundant production solutions.
Optimize
In the case of increasing the value/cost ratio, the ability to rationalize tools and solutions should definitely lead to reduced cost of ownership (beyond the delivery benefit mentioned above), but the largest priority should be in identifying ways to modernize on a continual basis.
Again, in my experience, modernization is difficult to prioritize and fund until there is an end-of-life or end-of-support scenario, at which point it becomes a “must do” priority, and causes a significant amount of delivery disruption in the process.
What I believe is a much better and healthier approach to modernization is a more disciplined, thoughtful approach that is akin to “urban renewal”, where there is an annual allocation of work directed at modernization on a prioritized basis (the criteria for which should be established through EA, given an understanding of other business demand), such that significant “events” are mitigated and it becomes a way of working on a sustained basis. In this way, the delineation between “keep the lights on” (KTLO) support, maintenance (which is where modernization efforts belong), and enhancement/ build-related work is important. In my experience, that second maintenance bucket is too often lumped into KTLO work, it is underserved/underfunded, and ultimately that creates periodic crises in IT to remediate things that should’ve been addressed far sooner (as a much lower cost) if a more disciplined portfolio management strategy was in place.
Inspire
In the interest of supporting the above objectives, having the right culture and skills to support ongoing evolution is imperative. To that end, the role of EA should be in helping to inform and guide the core skills needed to “lean forward” into advanced technology, while maintaining the right level of competency to support the footprint in place.
Again, this is where having a focus on modernization can help, as it creates a means to sunset legacy tools and technologies, to enable that continuous evolution of the skills the organization needs to operate (whether internally or externally sourced).
Perform
Finally, the role of EA in the production setting could be more or less difficult depending on how well the above capabilities are defined and supported in an enterprise. To the degree standards, rationalization, modernization, and the right culture and skills are in place, the role of EA would be helping to “tune” the environment to perform better and at a lower cost to operate.
Where there is a priority need for EA is ensuring there is an integrated approach to cyber security that aligns to development processes (e.g., DevSecOps) and a comprehensive, integrated strategy to monitor and manage performance in the production environment so that production incidents (using ITIL-speak) can be minimized and mitigated to the maximum degree possible.
Wrapping Up
Looking back on the various dimensions and priorities outlined above in relation to the role of EA, perhaps there isn’t much that I can argue is very different than what the role entailed five or ten years ago… establish standards, simplify / rationalize, modernize, retool, govern… that being said, the pace at which these things need to be accomplished and the criticality of doing them well is more important than ever with the increasing role technology plays in the digital enterprise. Like other dimensions required to establish excellence in IT, courageous leadership is where this needs to start, because it takes discipline to do things “right” while still doing them at a pace and with an agility that discerns the things that matter to an enterprise versus those that are simply ivory tower thinking.
I hope the ideas were worth considering. Thanks for spending the time to read them. Feedback is welcome as always.
I’ve been thinking about writing this article for a while, with the premise of “what does IT look like in the future?” In a digital economy, the role of technology in The Intelligent Enterprise will certainly continue to be creating value and competitive business advantage. That being said, one can reasonably assume a few things that are true today for medium to large organizations will continue to be part of that reality as well, namely:
The technology footprint will be complex and heterogenous in its makeup. To the degree that there is a history of acquisitions, even more so
Cost will always be a concern, especially to the degree it exceeds value delivered (this is explored in my article on Optimizing the Value of IT)
Agility will be important in adopting and integrating new capabilities rapidly, especially given the rate of technology advancement only appears to be accelerating over time
Talent management will be complex given the variety of technologies present will be highly diverse (something I’ve started to address in my Workforce and Sourcing Strategy Overview article)
My hope is to provide some perspective in this article on where I believe things will ultimately move in technology, in the underlying makeup of the footprint itself, how we apply capabilities against it, and how to think about moving from our current reality to that environment. Certainly, all of the five dimensions of what I outlined in my article on Creating Value Through Strategy will continue to apply at an overall strategy level (four of which are referenced in the bullet points above).
A Note on My Selfish Bias…
Before diving further into the topic at hand, I want to acknowledge that I am coming from a place where I love software development and the process surrounding it. I taught myself to program in the third grade (in Apple Basic), got my degree in Computer Science, started as a software engineer, and taught myself Java and .Net for fun years after I stopped writing code as part of my “day job”. I love the creative process for conceptualizing a problem, taking a blank sheet of paper (or white board), designing a solution, pulling up a keyboard, putting on some loud music, shutting out distractions, and ultimately having technology that solves that problem. It is a very fun and rewarding thing to explore those boundaries of what’s possible and balance the creative aspects of conceptual design with the practical realities and physical constraints of technology development.
All that being said, insofar as this article is concerned, when we conceptualize the future of IT, I wanted to put a foundational position statement forward to frame where I’m going from here, which is:
Just because something is cool and I can do it, doesn’t mean I should.
That is a very difficult thing to internalize for those of us who live and breathe technology professionally. Pride of authorship is a real thing and, if we’re to embrace the possibilities of a more capable future, we need to apply our energies in the right way to maximize the value we want to create in what we do.
The Producer/Consumer Model
Where the Challenge Exists Today
The fundamental problem I see in technology as a whole today (I realize I’m generalizing here) is that we tend to want to be good at everything, build too much, customize more than we should, and throw caution to the wind when it comes to things like standards and governance as inconveniences that slow us down in the “deliver now” environment in which we generally operate (see my article Fast and Cheap, Isn’t Good for more on this point).
Where that leaves us is bloated, heavy, expensive, and slow… and it’s not good. For all of our good intentions, IT doesn’t always have the best reputation for understanding, articulating, or delivering value in business terms and, in quite a lot of situations I’ve seen over the years, our delivery story can be marred with issues that don’t create a lot of confidence when the next big idea comes along and we want to capitalize on the opportunity it presents.
I’m being relatively negative on purpose here, but the point is to start with the humility of acknowledging the situation that exists in a lot of medium to large IT environments, because charting a path to the future requires a willingness to accept that reality and to create sustainable change in its place. The good news, from my experience, is there is one thing going for most IT organizations I’ve seen that can be a critical element in pivoting to where we need to be: a strong sense of ownership. That ownership may show up as frustration in the status quo depending on the organization itself, but I’ve rarely seen an IT environment where the practitioners themselves don’t feel ownership for the solutions they build, maintain, and operate or have a latent desire to make them better. There may be a lack of a strategy or commitment to change in many organizations, but the underlying potential to improve is there, and that’s a very good thing if capitalized upon.
Challenging the Status Quo
Pivoting to the future state has to start with a few critical questions:
Where does IT create value for the organization?
Which of those capabilities are available through commercially available solutions?
To what degree are “differentiated” capabilities or features truly creating value? Are they exceptions or the norm?
Using an example from the past, a delivery team was charged with solving a set of business problems that they routinely addressed through custom solutions, even though the same capabilities could be accomplished through integration of one or more commercially available technologies. From an internal standpoint, the team promoted the idea that they had a rapid delivery process, were highly responsive to the business needs they were meant to address, etc. The problem is that the custom approach actually cost more money to develop, maintain, and support, was considerably more difficult to scale. Given solutions were also continually developed with a lack of standards, their ability to adopt or integrate any new technologies available on the market was non-existent. Those situations inevitably led to new custom solutions and the costs of ownership skyrocketed over time.
This situation begs the question: if it’s possible to deliver equivalent business capability without building anything “in house”, why not do just that?
In the proverbial “buy versus build” argument, these are the reasons I believe it is valid to ultimately build a solution:
There is nothing commercially available that provides the capability at a reasonable cost
I’m referencing cost here, but it’s critical to understand the TCO implications of building and maintaining a solution over time. They are very often underestimated.
There is a commercially available solution that can provide the capability, but something about privacy, IP, confidentiality, security, or compliance-related concerns makes that solution infeasible in a way that contractual terms can’t address
I mention contracting purposefully here, because I’ve seen viable solutions eliminated from consideration over a lack of willingness to contract effectively, and that seems suboptimal by comparison with the cost of building alternative solutions instead
Ultimately, we create value in business capability enabled through technology, “who” built them doesn’t matter.
Rethinking the Model
My assertion is that we will obtain the most value and acceleration of business capabilities when we shift towards a producer/consumer model in technology as a whole.
What that suggests is that “corporate IT” largely adopts the mindset of the consumer of technologies (specifically services or components) developed by producers focused purely on building configurable, leverageable components that can be integrated in compelling ways into a connected ecosystem (or enterprise) of the future.
What corporate IT “produces” should be limited to differentiated capabilities that are not commercially available, and a limited set of foundational capabilities that will be outlined below. By trying to produce less and thinking more as a consumer, this should shift the focus internally towards how technology can more effectively enable business capability and innovation and externally towards understanding, evaluating, and selecting from the best-of-breed capabilities in the market that help deliver on those business needs.
The implication, of course for those focused on custom development, would be to move towards those differentiated capabilities or entirely towards the producer side (in a product-focused environment), which honestly could be more satisfying than corporate IT can be for those with a strong development inclination.
The cumulative effect of these adjustments should lead to an influx of talent into the product community, an associated expansion of available advanced capabilities in the market, and an accelerated ability to eventually adopt and integrate those components in the corporate environment (assuming the right infrastructure is then in place), creating more business value than is currently possible where everyone tries to do too much and sub-optimizes their collective potential.
Learning from the Evolution of Infrastructure
The Infrastructure Journey
You don’t need to look very far back in time to remember when the role of a CTO was largely focused on managing data centers and infrastructure in an internally hosted environment. Along the way, third parties emerged to provide hosting services and alleviate the need to be concerned with routine maintenance, patching, and upgrades. Then converged infrastructure and the software-defined data center provided opportunities to consolidate and optimize that footprint and manage cost more effectively. With the rapid evolution of public and private cloud offerings, the arguments for managing much of your own infrastructure beyond those related specifically to compliance or legal concerns are very limited and the trajectory of edge computing environments is still evolving fairly rapidly as specialized computing resources and appliances are developed. The learning being: it’s not what you manage in house that matters, it’s the services you provide relative to security, availability, scalability, and performance.
Ok, so what happens when we apply this conceptual model to data and applications? What if we were to become a consumer of services in these domains as well? The good news is that this journey is already underway, the question is how far we should take things in the interest of optimizing the value of IT within an organization.
The Path for Data and Analytics
In the case of data, I think about this area in two primary dimensions:
How we store, manage, and expose data
How we apply capabilities to that data and consume it
In terms of storage, the shift from hosted data to cloud-based solutions is already underway in many organizations. The key levers continue to be ensuring data quality and governance, finding ways to minimize data movement and optimize data sharing (while facilitating near real-time analytics), and establishing means to expose data in standard ways (e.g., virtualization) that enable downstream analytic capabilities and consumption methods to scale and work consistently across an enterprise. Certainly, the cost of ingress and egress of data across environments is a key consideration, especially where SaaS/PaaS solutions are concerned. Another opportunity continues to be the money wasted on building data lakes (beyond archival and unstructured data needs) when viable platform solutions in that space are available. From my perspective, the less time and resources spent on moving and storing data to no business benefit, the more energy that can be applied to exposing, analyzing, and consuming that data in ways that create actual value. Simply said, we don’t create value in how or where we store data, we create value in how consume it.
On the consumption side, having a standards-based environment with a consistent method for exposing data and enabling integration will lend itself well to tapping into the ever-expanding range of analytical tools on the market, as well as swapping out one technology for another as those tools continue to evolve and advance in their capabilities over time. The other major pivot being to minimize the amount of “traditional” analytical reporting and business intelligence solutions to more dynamic data apps that leverage AI to inform meaningful end-user actions, whether that’s for internal or external users of systems. Compliance-related needs aside, at an overall level, the primary goal of analytics should be informed action, not administrivia.
The Shift In Applications
The challenge in the applications environment is arbitrating the balance between monolithic (“all in”) solutions, like ERPs, and a fully distributed component-based environment that requires potentially significant management and coordination from an IT standpoint.
Conceptually, for smaller organizations, where the core applications (like an ERP suite + CRM solution) represent the majority of the overall footprint and there aren’t a significant number of specialized applications that must interoperate with them, it likely would be appropriate and effective to standardize based on those solutions, their data model, and integration technologies.
On the other hand, the more diverse and complex the underlying footprint is for a medium- to large-size organization, there is value in looking at ways to decompose these relatively monolithic environments to provide interoperability across solutions, enable rapid integration of new capabilities into a best-of-breed ecosystem, and facilitate analytics that span multiple platforms in ways that would be difficult, costly, or impossible to do within any one or two given solutions. What that translates to, in my mind, is an eventual decline of the monolithic ERP-centric environment to more of a service-driven ecosystem where individually configured capabilities are orchestrated through data and integration standards with components provided by various producers in the market. That doesn’t necessarily align to the product strategies of individual companies trying to grow through complementary vertical or horizontal solutions, but I would argue those products should create value at an individual component level and be configurable such that swapping out one component of a larger ecosystem should still be feasible without having to abandon the other products in that application suite (that may individually be best-of-breed) as well.
Whether shifting from a highly insourced to a highly outsourced/consumption-based model for data and applications will be feasible remains to be seen, but there was certainly a time not that long ago when hosting a substantial portion of an organization’s infrastructure footprint in the public cloud was a cultural challenge. Moving up the technology stack from the infrastructure layer to data and applications seems like a logical extension of that mindset, placing emphasis on capabilities provided and value delivered versus assets created over time.
Defining Critical Capabilities
Own Only What is Essential
Making an argument to shift to a consumption-oriented mindset in technology doesn’t mean there isn’t value in “owning” anything, rather it’s meant to be a call to evaluate and challenge assumptions related to where IT creates differentiated value and to apply our energies towards those things. What can be leveraged, configured, and orchestrated, I would buy and use. What should be built? Capabilities that are truly unique, create competitive advantage, can’t be sourced in the market overall, and that create a unified experience for end users. On the final point, I believe that shifting to a disaggregated applications environment could create complexity for end users in navigating end-to-end processes in intuitive ways, especially to the degree that data apps and integrated intelligence becomes a common way of working. To that end, building end user experiences that can leverage underlying capabilities provided by third parties feels like a thoughtful balance between a largely outsourced application environment and a highly effective and productive individual consumer of technology.
Recognize Orchestration is King
Workflow and business process management is not a new concept in the integration space, but it’s been elusive (in my experience) for many years for a number of reasons. What is clear at this point is that, with the rapid expansion in technology capabilities continuing to hit the market, our ability to synthesize a connected ecosystem that blends these unique technologies with existing core systems is critical. The more we can do this in consistent ways, the more we shift towards a configurable and dynamic environment that is framework-driven, the more business flexibility and agility we will provide… and that translates to innovation and competitive advantage over time. Orchestration is a critical piece of deciding which processes are critical enough that they shouldn’t be relegated to the internal workings of a platform solution or ERP, but taken in-house, mapped out, and coordinated with the intention of creating differentiated value that can be measured, evaluated, and optimized over time. Clearly the scalability and performance of this component is critical, especially to the degree there is a significant amount of activity being managed through this infrastructure, but I believe the transparency, agility, and control afforded in this kind of environment would greatly outweigh the complexity involved in its implementation.
Put Integration in the Center
In a service-driven environment, clearly the infrastructure for integration, streaming in particular, along with enabling a publish and subscribe model for event-driven processing, will be critical for high-priority enterprise transactions. The challenge in integration conversations in my experience tends to be defining the transactions that “matter”, in terms of facilitating interoperability and reuse, and those that are suitable for point-to-point, one off connections. There is ultimately a cost for reuse when you try to scale, and there is discipline needed to arbitrate those decisions to ensure they are appropriate to business needs.
Reassess Your Applications/Services
With any medium to large organization, there is likely technology sprawl to be addressed, particularly if there is a material level of custom development (because component boundaries likely won’t be well architected) and acquired technology (because of the duplication it can cause in solutions and instances of solutions) in the landscape. Another complicating factor could be the diversity of technologies and architectures in place, depending on whether or not a disciplined modernization effort exists, the level of architecture governance in place, and rate and means by which new technologies are introduced into the environment. All of these factors call for a thoughtful portfolio strategy, to identify critical business capabilities and ensure the technology solutions meant to enable them are modern, configurable, rationalized, and integrated effectively from an enterprise perspective.
Leverage Data and Insights, Then Optimize
With analytics and insights being a critical capability to differentiated business performance, an effective data governance program with business stewardship, selecting the right core, standard data sets to enable purposeful, actionable analytics, and process performance data associated with orchestrated workflows are critical components of any future IT infrastructure. This is not all data, it’s the subset that creates significant business value to justify the investment in making it actionable. As process performance data is gathered through the orchestration approach, analytics can be performed to look for opportunities to evolve processes, configurations, rules, and other characteristics of the environment based on key business metrics to improve performance over time.
Monitor and Manage
With the expansion of technologies and components, internal and external to the enterprise environment, having the ability to monitor and detect issues, proactively take action, and mitigate performance, security, or availability issues will become increasingly important. Today’s tools are too fragmented and siloed to achieve the level of holistic understanding that is needed between hosted and cloud-based environments, including internal and external security threats in the process.
Secure “Everything”
While zero trust and vulnerability management risk is expanding at a rate that exceeds an organization’s ability to mitigate it, treating security as a fundamental requirement of current and future IT environments is a given. The development of a purposeful cyber strategy, prioritizing areas for tooling and governance effectively, and continuing to evolve and adapt that infrastructure will be core to the DNA of operating successfully in any organization. Security is not a nice to have, it’s a requirement.
The Role of Standards and Governance
What makes the framework-driven environment of the future work is ultimately having meaningful standards and governance, particularly for data and integration, but extending into application and data architecture, along with how those environments are constructed and layered to facilitate evolution and change over time. Excellence takes discipline and, while that may require some additional investment in cost and time during the initial and ongoing stages of delivery, it will easily pay itself off in business agility, operating cost/ cost of ownership, and risk/exposure to cyber incidents over time.
The Lending Example
Having spent time a number of years ago understanding and developing strategy in the consumer lending domain, the similarities in process between direct and indirect lending, prime and specialty / sub-prime, from simple products like credit card to more complex ones like mortgage is difficult to ignore. That being said, it isn’t unusual for systems to exist in a fairly siloed manner, from application to booking, from document preparation, into the servicing process itself.
What’s interesting, from my perspective, is where the differentiation actually exists across these product sets: in the rules and workflow being applied across them, while the underlying functions themselves are relatively the same. As an example, one thing that differentiates a lender is their risk management policy, not necessarily the tool they use to assess to implement their underwriting rules or scoring models per se. Similarly, whether pulling a credit score is part of the front end of the process in something like credit card and an intermediate step in education lending, having a configurable workflow engine could enable origination across a diverse product set with essentially the same back-end capabilities and likely at a lower operating cost.
So why does it matter? Well, to the degree that the focus shifts from developing core components that implement relatively commoditized capability to the rules and processes that enable various products to be delivered to end consumers, the speed with which products can be developed, enhanced, modified, and deployed should be significantly improved.
Ok, Sounds Great, But Now What?
It Starts with Culture
At the end of the day, even the best designed solutions come down to culture. As I mentioned above, excellence takes discipline and, at times, patience and thoughtfulness that seems to contradict the speed with which we want to operate from a technology (and business) standpoint. That being said, given the challenges that ultimately arise when you operate without the right standards, discipline, and governance, the outcome is well worth the associated investments. This is why I placed courageous leadership as the first pillar in the five dimensions outlined in my article on Excellence by Design. Leadership is critical and, without it, everything else becomes much more difficult to accomplish.
Exploring the Right Operating Model
Once a strategy is established to define the desired future state and a culture to promote change and evolution is in place, looking at how to organize around managing that change is worth consideration. I don’t necessarily believe in “all in” operating approaches, whether it is a plan/build/run, product-based orientation, or some other relatively established model. I do believe that, given leadership and adaptability are critically needed for transformational change, looking at how the organization is aligned to maintaining and operating the legacy environment versus enabling establishment and transition to the future environment is something to explore. As an example, rather than assuming a pure product-based orientation, which could mushroom into a bloated organization design where not all leaders are well suited to manage change effectively, I’d consider organizing around a defined set of “transformation teams” that operate in a product-oriented/iterative model, but basically take on the scope of pieces of the technology environment, re-orient, optimize, modernize, and align them to the future operating model, then transition those working assets to different leaders that maintain or manage those solutions in the interest of moving to the next set of transformation targets. This should be done in concert with looking for ways to establish “common components” teams (where infrastructure like cloud platform enablement can be a component as well) that are driven to produce core, reusable services or assets that can be consumed in the interest of ultimately accelerating delivery and enabling wider adoption of the future operating model for IT.
Managing Transition
One of the consistent challenges with any kind of transformative change is moving between what is likely a very diverse, heterogenous environment to one that is standards-based, governed, and relatively optimized. While it’s tempting to take on too much scope and ultimately undermine the aspirations of change, I believe there is a balance to be struck in defining and establishing some core delivery capabilities that are part of the future infrastructure, but incrementally migrating individual capabilities into that future environment over time. This is another case where disciplined operations and disciplined delivery come into play so that changes are delivered consistently but also in a way that is sustainable and consistent with the desired future state.
Wrapping Up
While a certain level of evolution is guaranteed as part of working in technology, the primary question is whether we will define and shape that future or be continually reacting and responding to it. My belief is that we can, through a level of thoughtful planning and strategy, influence and shape the future environment to be one that enables rapid evolution as well as accelerated integration of best-of-breed capabilities at a pace and scale that is difficult to deliver today. Whether we’ll truly move to a full producer/consumer type environment that is service based, standardized, governed, orchestrated, fully secured, and optimized is unlikely, but falling short of excellence as an aspiration would still leave us in a considerably better place than where we are today… and it’s a journey worth making in my opinion.
I hope the ideas were worth considering. Thanks for spending the time to read them. Feedback is welcome as always.
My father ran his own business when I was growing up. His business had two components: first, he manufactured pinion wire (steel or brass rods of various diameters with teeth from which gears are cut) and, second, as a producer of specialty gears that were used in various applications (e.g., the timing mechanism of an oil pump). It was something he used to raise and support a large Italian family and it was pretty much a one-man show, with help from his kids as needed, whether that was counting and quality testing gears with mating parts or cutting, packing, and shipping material to various customers across North America. He acquired and learned how to operate screw machines to produce pinion wire but eventually shifted to a distribution business, where he would buy finished material in quantity and then distribute to middle market customers at lower volumes at a markup.
His business was as low tech as you could get, with a little card file he maintained that had every order by customer written out on an index card, tracking the specific item/part, volume, and pricing so he had a way to understand history as new requests for quotes and orders came in and also understand purchase patterns over time. It was largely a relationship business, and he took his customer commitments to heart at a level that dinner conversation could easily deflect into a worry about a disruption in his supply chain (e.g., something not making it to the electroplater on time) and whether he might miss his promised delivery date. Integrity and accountability were things that mattered and it was very clear his customers knew it. He had a note pad on which he’d jot things down to keep some subset of information on customer / prospect follow ups, active orders, pending quotes, and so on, but to say there was a system outside what he kept in his head would be unfairto his mental capacity, which was substantial.
It was a highly manual business and, as a person who taught myself how to write software in the third grade, I was always curious what he could do to make things a little easier, more structured, and less manual, even though that was an inescapable part of running his business on the whole. That isn’t to say he had that issue or concern, given he’d developed a system and way of operating over many years, knew exactly how it worked, and was entirely comfortable with it. There was also a small point of my father being relatively stubborn, but that didn’t necessarily deter me from suggesting things could be improved. I do like a challenge, after all…
As it happened, when I was in high school, we got our first home computer (somewhere in the mid-1980s) and, despite its relatively limited capacity, I thought it would be a good idea to figure out a way to make something about running his business a little easier with technology. To that end, I wrote a piece of software that would take all of his order management off of the paper index cards and put it into an application. The ability to look up customer history, enter new orders, look at pricing differences across customers, etc. were all things I figured would make life a lot easier, not mention reducing the need to maintain this overstuffed card file that seemed highly inefficient to me.
By this point, I suspect it’s clear to the reader what happened… which is that, while my father appreciated the good intentions and concept, there was no interest in changing the way he’d been doing business for decades in favor of using technology he found a lot more confusing and intimidating than what he already knew (and that worked to his level of satisfaction). I ended up relatively disappointed, but learned a valuable lesson, which is that, the first challenge in transformation is changing mindsets… without that, the best vision in the world will fail, no matter what value it may create.
It Starts with Mindset
I wanted to start this article with the above story because, despite nearly forty years having passed since I tried to introduce a little bit of automation to my father’s business, manufacturing in today’s environment can be just as antiquated and resistant to change as it was then, seeing technology as an afterthought, a bolt on, or cost of doing business rather than the means to unlock the potential that exists to transform a digital business in even the most “low tech” of operating environments.
While there is an inevitable and essential dependence on people, equipment, and processes, my belief is that we have a long way to go on understanding the critical role technology plays in unlocking the potential of all of those things to optimize capacity, improve quality, ensure safety, and increase performance in a production setting.
The Criticality of Discipline
Having spent a number of years understanding various approaches to digital manufacturing, one point that I wanted to raise prior to going into more of the particulars is the importance of operating with a holistic vision and striking the balance between agility and long-term value creation. As I addressed in my article Fast and Cheap Isn’t Good, too much speed without quality can lead to complexity, uncontrolled and inflated TCO, and an inability to integrate and scale digital capabilities over time. Wanting something “right now” isn’t an excuse not to do things the right way and eventually there is a price to pay for tactical thinking when solutions don’t scale or produce more than incremental gains.
This is also related to “Framework-Driven Design” that I talk about in my article on Excellence by Design. It is rarely the case that there is an opportunity to start from scratch in modernizing a manufacturing facility, but I do believe there is substantial value in making sure that investments are guided by an overall operating concept, technology strategy, and evolving standards that will, over time, transform the manufacturing environment as a whole and unlock a level of value that isn’t possible where incremental gains are always the goal. Sustainable change takes time.
The remainder of this article will focus on a set of areas that I believe form the core of the future digital manufacturing environment. Given this is a substantial topic, I will focus on the breadth of the subject versus going too deep into any one area. Those can be follow-up articles as appropriate over time.
Leveraging Data Effectively
The Criticality of Standards
It is a foregone conclusion that you can’t optimize what you can’t track, measure, and analyze in real-time. To that end, starting with data and standards is critical in transforming to a digital manufacturing environment. Without standards, the ability to benchmark, correlate, and analyze performance will be severely compromised. This can be a basic as how a camera system, autonomous vehicle, drone, conveyor, or digital sensor is integrated within a facility, to the representation of equipment hierarchies, or how operator roles and processes are tracked across a set of similar facilities. Where standards for these things don’t exist, value will be constrained to a set of individual point solutions, use cases, and one-off successes. Where standards are, however, implemented and scaled over time, the value opportunity will eventually cross over into exponential gains that aren’t otherwise possible, because the technical debt associated with retrofitting and mapping across various standards in place will create a significant maintenance effort that limits focus on true innovation and optimization. This isn’t to suggest that there is a one-size-fits-all way to thinking about standards and that every solution needs to conform for the sake of an ivory tower ideal. The point is that it’s worth slowing down the pace of “progress” at times to understand the value in designing solutions for longer-term value creation.
The Role of Data Governance
It’s impossible to discuss the criticality of standards without also highlighting the need for active, ongoing data governance, both to ensure standards are followed, that data quality at the local and enterprise level is given priority (especially to the degree that analytical insights and AI become core to informed decision making), and also to help identify and surface additional areas of opportunity where standards may be needed to create further insights and value across the operating environment. The upshot of this is that there need to be established roles and accountability for data stewards at the facility and enterprise level if there is an aspiration to drive excellence in manufacturing, no matter what the present level of automation is across facilities.
Modeling Distributed Operations
Applying Distributed Computing
There is a power in distributed computing that enables you to scale execution at a rate that is beyond the capacity you can achieve with a single machine (or processor). The model requires an overall coordinator of activity to distribute work and monitor execution and then the individual processors to churn out calculations as rapidly as they are able. As you increase processors, you increase capacity, so long as the orchestrator can continue to manage and coordinate the parallel activity effectively.
From a manufacturing standpoint, the concept applies well across a set of distributed facilities, where the overall goal is to optimize the performance and utilization of available capacity given varying demand signals, individual operating characteristics of each facility, cost considerations, preventative maintenance windows, etc. It’s a system that can be measured, analyzed, and optimized, with data gathered and measured locally, a subset of which is used to inform and guide the macro-level process.
Striking the Balance
While I will dive into this a little further towards the tail end of this article, the overall premise from an operating standpoint is to have a model that optimizes the coordination of activity between individual operating units (facilities) that are running as autonomously as possible at peak efficiency, while distributing work across them in a way that maximizes production, availability, cost, or whatever other business parameters are most critical.
The key point being that the technology infrastructure for distributing and enabling production across and within facilities should ideally be a matter of business parameters that can be input and adjusted at the macro-level and the entire system of facilities be adjusted in real-time in a seamless, integrated way. Conversely, the system should be a closed loop where a disruption at the facility level can inform a change across the overall ecosystem such that workloads are redistributed (if possible) to minimize the impact on overall production. This could be manifest in one or more micro-level events (e.g., a higher than expected occurrence of unplanned outages) that informs production scheduling and distribution of orders to a major event (e.g., a fire or substantial facility outage) that redirects work across other facilities to minimize end customer impact. Arguably there are elements that exist within ERP systems that can account for some of this today, but the level and degree of customization required to make it a robust and inclusive process would be substantial, given much of the data required to inform the model exists outside the ERP ecosystemitself, in equipment, devices, processes, and execution within individual facilities themselves.
Thinking about Mergers, Acquisitions, and Divestitures
As I mentioned in the previous section on data, establishing standards is critical to enabling a distributed paradigm for operations, the benefit of which is also the speed at which an acquisition could be leveraged effectively in concert with an existing set of facilities. This assumes there is an ability to translate and integrate systems rapidly to make the new facility function as a logical extension of what is already in place, but ultimately a number of those technology-related challenges would have to be worked through in the interest of optimizing individual facility performance regardless. The alternative to having this macro-level dynamic ecosystem functioning would likely be excess cost, inefficiency, and wasted production capacity.
Advancing the Digital Facility
The Role of the Digital Facility
At a time when data and analytics can inform meaningful action in real-time, the starting point for optimizing performance is the individual “processor”, which is a digital facility. While the historical mental model would focus on IT and OT systems and integrating them in a secure way, the emergence of digital equipment, sensors, devices, and connected workers has led to more complex infrastructure and an exponential amount of available data that needs to be thoughtfully integrated to maximize the value it can contribute over time. With this increased reliance on technology, likely some of which runs locally and some in the cloud, the reliability of wired and wireless connectivity has also become a critical imperative of operating and competing as a digital manufacturer.
Thinking About Auto Maintenance
Drawing on a consumer example, I brought my car in for maintenance recently. The first thing the dealer did was plug in and download a set of diagnostic information that was gathered over the course of my road trips over the last year and a half. The data was collected passively, provided the technicians with input on how various engine components were performing, and also some insight on settings that I could adjust given my driving habits that would enable the car to perform better (e.g., be more fuel efficient). These diagnostics and safety systems are part of having a modern car and we take them for granted.
Turning back to a manufacturing facility, a similar mental model should apply for managing data at a local and enterprise level, which is that there should be a passive flow of data to a central repository that is mapped to processes, equipment, and operators in a way that enables ongoing analytics to help troubleshoot problems, identify optimization and maintenance opportunities, and look across facilities for efficiencies that could be leveraged at broader scale.
Building Smarter Equipment
Taking things a step further… what if I were to attach a sensor under the hood of my car, take the data, build a model, and try to make driving decisions using that model and my existing dashboard as input? The concept seems a little ridiculous given the systems already in place within a car to help make the driving experience safe and efficient. That being said, in a manufacturing facility with legacy equipment, that intelligence isn’t always built in, and the role of analytics can become an informed guessing game of how a piece of equipment is functioning without the benefit of the knowledge of the people who built the equipment to begin with.
Ultimately, the goal should be for the intelligence to be embedded within the equipment itself, to enable a level of self-healing or alerting, and then within control systems to look at operating conditions across a connected ecosystem to determine appropriate interventions as they occur, whether that be a minor adjustment to operating parameters or a level of preventative maintenance.
The Role of Edge Computing and Facility Data
The desire to optimize performance and safety at the individual facility level means that decisions need to be informed and actions taken in near real-time as much as possible. This premise then suggests that facility data management and edge computing will continue to increase in criticality as more advanced uses of AI become part of everyday integrated work processes and facility operations.
Enabling Operators with Intelligence
The Knowledge Challenge
With the general labor shortage in the market and retirement of experienced, skilled laborers, managing knowledge and accelerating productivity is a major issue to be addressed in manufacturing facilities. There are a number of challenges associated with this situation, not the least of which can be safety related depending on the nature of the manufacturing environment itself. Beyond that, the longer it takes to make an operator productive in relation to their average tenure (something that statistics would suggest is continually shrinking over time), the effectiveness of the average worker can become a limiting factor in the operating performance of a facility overall.
Understanding Operator Overload
One way that things have gotten worse is the proliferation of systems that comes with “modernizing” the manufacturing environment itself. When confronted with the ever-expanding set of control, IT, ERP, and analytical systems, all of which can be sending alerts and requesting action (to varying degrees of criticality) on a relatively continuous basis, the pressure being created on individual operators and supervisors in a facility has increased substantially (with the availability of exponential amounts of data itself). This is further complicated in situations where an individual “wears multiple hats” in terms of fulfilling multiple roles/personas within a given facility and arbitrating which actions to take against that increased number of demands can be considerably more complex.
Why Digital Experience Matters
While the number of applications that are part of an operating environment may not be something that is easy to reduce or simplify without significant investment (and time to make change happen), it is possible to look at things like digital experience platforms (DXPs) as a means to manage multiple applications into a single, integrated experience, inclusive of AR/VR/XR technologies as appropriate. Organizing around an individual operator’s responsibilities can help reduce confusion, eliminate duplicated data entry, improve data quality, and ultimately improve productivity, safety, and effectiveness by extension.
The Role of the Intelligent Agent
With a foundation in place to organize and present relevant information and actions to an operator on a real-time basis, the next level of opportunity comes with the integration of intelligent agents (AI-enabled tools) into a digital worker platform to inform meaningful and guided actions that will ultimate create the most production and safety impact on an ongoing basis. Again, there is a significant dependency on edge computing, wireless infrastructure, facility data, mobile devices, a delivery mechanism (the DXP mentioned above), and a sound underlying technology strategy to enable this at scale, but it is ultimately where AI tools can have a major impact in manufacturing moving forward.
Optimizing Performance through Orchestration
Why Orchestration Matters
Orchestration itself isn’t a new concept in manufacturing from my perspective, as the legacy versions of it are likely inherent in control and MES systems themselves. The challenge occurs when you want to scale that concept out to include digital equipment, digital workers, digital devices, control systems, and connected applications into one, seamless, integrated end-to-end process. Orchestration provides the means to establish configurable and dynamic workflow and associated rules into how you operate and optimize performance within and across facilities in a digital enterprise.
While this is definitely a capability that would need to be developed and extended over time, the concept is to think of the manufacturing ecosystem as a seamless collaboration of operators and equipment to ultimately drive efficient and safe production of finished goods. Having established the infrastructure to coordinate and track activity, the process performance can be automatically recorded and analyzed to inform continuous improvement on an ongoing basis.
Orchestrating within the Facility
The number of uses of orchestration within a facility can be as simple as coordinating and optimizing material movement between autonomous vehicles and fork lifts within a facility, to computer vision applications for safety and quality management. With the increasing number of connected solutions within a facility,having the means to integrate and coordinate activity between and across them offers a significant opportunity in digital manufacturing moving forward.
Orchestrating across the Enterprise
Scaling back out to the enterprise level, looking across facilities, there are opportunities to look at things like procurement of MRO supplies and optimizing inventory levels, managing and optimizing production planning across similar facilities, benchmarking and analyzing process performance and looking for improvements that can be applied across facilities in a way that will create substantially greater impact than is possible if the focus is limited to the individual facility alone. Given that certain enterprise systems like ERPs tend to operate at largely a global versus local level, having infrastructure in place to coordinate activity across both can create visibility to improvement opportunities and thereby substantial value over time.
Coordinated Execution
Finally, to coordinate between the local and global levels of execution, a thoughtful approach to managing data and the associated analytics needs to be taken. As was mentioned in the opening, the overall operating model is meant to leverage a configurable, distributed paradigm, so the data that is shared and analyzed within and across layers is important to calibrate as part of the evolving operating and technology strategy.
Wrapping Up
There is a considerable amount of complexity associated with moving between a legacy, process and equipment-oriented mindset to one that is digitally enabled and based on insight-driven, orchestrated action. That being said, the good news is that the value that can be unlocked with a thoughtful digital strategy is substantial given we’re still on the front-end of the evolution overall.
I hope the ideas were worth considering. Thanks for spending the time to read them. Feedback is welcome as always.
Having touched on the importance of quality in accelerating value in my latest article Creating Value Through Strategy, I wanted to dive a little deeper into the topic of “speed versus quality”.
For those who may be unfamiliar, there is a general concept in project delivery that the three primary dimensions against which you operate are good (the level of effort you put into ensuring a product is well architected and meets functional and non-functional requirements at the time of delivery), fast (how quickly or often you produce results), and cheap (your ability to deliver the product/solution at a reasonable cost).
The general assumption is that the realities of delivery lead you to having to prioritize two of the three (e.g., you can deliver a really good product fast, but it won’t be cheap; or you can deliver a really good product at a low cost, but it will take a lot of time [therefore not be “fast”]). What this translates to, in my experience, has nearly always been that speed and cost are prioritized highest, with quality being the item compromised.
Where this becomes an issue is in the nature of the tradeoff that was made and the longer-term implications of those decisions. Quality matters. My assertion is that, where quality is compromised, “cheap” is only true in the short-term and definitely not the case overall.
The remainder of this article will explore several dimensions to consider when making these decisions. This isn’t to say that there aren’t cases where there is a “good enough” level of quality to deliver a meaningful or value-added product or service. My experience, however, has historically been that the concepts like “we didn’t have the time” or “we want to launch and learn” are often used as a substitute for discipline in delivery and ultimately undermine business value creation.
Putting Things in Perspective
Dimensions That Matter
I included the diagram above to put how I think of product delivery into perspective. In the prioritization of good, fast, and cheap, what often occurs is that too much focus and energy goes into the time spent getting a new capability or solution to market, but not enough on what happens once it is there and the implications of that. The remainder of this section will explore aspects of that worth considering in the overall context of product/solution development.
Some areas to consider in how a product is designed and delivered:
Architecture
Is the design of the solution modular and component- or service-based? This is important to the degree that capabilities may emerge over time that surpass what was originally delivered and, in a best-of-breed environment, you would ideally like to be able to replace part of a solution without having to fundamentally rearchitect or materially refactor the overall solution
Does the solution conform to enterprise standards and guidelines? I’ve seen multiple situations where concurrent, large-scale efforts were designed and developed without consideration for their interoperability and adherence to “enterprise” standards. By comparison, developing on a “program-“ or “project-level”, or in working with a monolithic technology/solution (e.g., with a relatively closed ERP system), creates technology silos that lead to a massive amount of technical debt as it is almost never the case that there is leadership appetite for refactoring or rewriting core aspects of those solutions over time
Is the solution cloud-native and does it support containerization to enable deployment of workloads across public and private clouds as well as the edge? In the highly complex computing environments of today, especially in industries like Manufacturing, the ability to operate and distribute solutions to optimize availability, performance, and security (at a minimum) is critical. Where these dimensions aren’t taken into account, there would likely be almost an immediate need for modernization to offset the risk of technology obsolescence at some point in the next year or two
Security
Does the product or service leverage enterprise technologies and security standards? Managing vulnerabilities and migrating towards “zero trust” is a critical aspect of today’s technology environment, especially to the degree that workloads are deployed on the public cloud. Where CI/CD pipelines are developed as part of a standard cloud platform strategy with integrated security tooling, the enterprise level ability to manage, monitor, and mitigate security risk will be significant improved
Integration
Does the product or service leverage enterprise technologies and integration standards? Interoperability with other internal and external systems, as well as your ability to introduce and leverage new capabilities and rationalize redundant solutions over time is fundamentally dependent on the manner in which applications are architected, designed, and integrated with the rest of a technology footprint. Having worked in environments with well-defined standards and strictly enforced governance versus ones where neither were in place, the level of associated complexity and costs in the ultimate operating environments was materially different
Data Standards
Does the product or service align to overall master data requirements for the organization? Master data management can be a significant challenge from a data governance standpoint, which is why giving this consideration up front in a product development lifecycle is extremely important. Where it isn’t considered in design, the end result could be master data that doesn’t map or align to other hierarchies in place, complicating integration and analytics intended to work across solutions and the “cleanup” required of data stewards (to the degree that they are in place) could be expensive and difficult post-deployment
Are advanced analytics aspirations taken into account in the design process itself? This is an area becoming increasingly important given AI-enabled (“intelligent”) applications as discussed in my article on The Intelligent Enterprise. Designing with data standards in mind and an eye towards how it will be used to enable and drive analytics, likely in concert with data in other adjacent or downstream systems is a step that can save considerable effort and cost downstream when properly addressed early in the product development cycle
“Good Enough”/Responsive Architecture
All the above points noted, I believe architecture needs to be appropriate to the nature of the solution being delivered. Having worked in environments where architecture standards were very “ivory tower”/theoretical in nature and made delivery extremely complex and costly versus ones where architecture was ignored and the delivery environment was essentially run with an “ask for forgiveness” or cowboy/superhero mentality, the ideal state in my mind should be somewhere in between, where architecture is appropriate to the delivery circumstances, but also mindful of longer-term implications of the solution being delivered so as to minimize technical debt and further interoperability in a connected enterprise ecosystem environment.
Thinking Total Cost of Ownership
What makes product/software development challenging is the level of unknowns that exist. At any given time, when estimating a new endeavor, you have the known, the known unknown, and the complete unknown (because what you’re doing is outside your team’s collective experience). The first two components can be incorporated into an estimation model that can be used for planning and the third component can be covered through some form of “contingency” load that is added to an estimate to account for those blind spots to a degree.
Where things get complicated is, once execution begins, the desire to meet delivery commitments (and the associated pressure thereof) can influence decisions being made on an ongoing basis. This is complicated by the normal number of surprises that occur during any delivery effort of reasonable scale and complexity (things don’t work as expected, decisions or deliverables are delayed, requirements become increasingly clear over time, etc.). The question is whether a project has both disciplined, courageous leadership in place and the appropriate level of governance to make sure that, as decisions need to made in the interest of arbitrating quality, cost, time, and scope, that they are done with total cost of ownership in mind.
As an example, there was a point in the past where I encountered a large implementation program ($100MM+ in scale) with a timeline of over a year to deploy an initial release. During the project, the team announced that all the pivotal architecture decisions needed to be made within a one-week window of time, suggesting that the “dates wouldn’t be met” if that wasn’t done. That logic was then used at a later point to decide that standards shouldn’t be followed for other key aspects of the implementation in the interest of “meeting delivery commitments”. What was unfortunate in this situation was that, not only were good architecture and standards not implemented, the project encountered technical challenges (likely due to one or two of those root causes, among other things) that caused it to be delivered over a year late regardless. The resulting solution was more difficult to maintain, integrate, scale, or leverage for future business needs. In retrospect, was any “speed” obtained through that decision making process and the lack of quality in the solution? Certainly not, and this situation unfortunately isn’t unique to larger scale implementations in my experience. In these cases, the ongoing run rate of the program itself can become an excuse to make tactical decisions that ultimately create a very costly and complex solution to manage and maintain in the production environment, none of which anyone typically wants to remediate or rewrite post-deployment.
So, given the above example, the argument could be made that the decisions were a result of inexperience or pure unknowns that existed when the work was estimated and planned to begin with, which is a fair point. Two questions come to mind in terms of addressing this situation:
Are ongoing changes being reviewed through a change control process in relation to project cost, scope, and deadline, or are the longer-term implications in terms of technical debt and operating cost of ownership also considered? Compromise is a reality of software delivery and there isn’t a “perfect world” situation pretty much ever in my experience. That being said, these choices should be conscious ones, made with full transparency and in a thoughtful manner, which is often not the case, especially when the pressures surrounding a project are high to begin with.
Are the “learnings” obtained on an ongoing basis factored into the estimation and planning process so as to mitigate future needs to compromise quality when issues arise? Having been part of and worked closely with large programs over many years, there isn’t a roadmap that ever plays out in practice how it is drawn up on paper at the outset. That being said, every time the roadmap is revised, as pivot points in the implementation are reached and plans adjusted, are learnings being incorporated such that mistakes or sacrifices to quality aren’t being repeated over and over again. This is a tangible thing that can be monitored and governed over time. In the case of Agile-driven efforts, it would be as simple as looking for patterns in the retrospectives (post-sprint) to see whether the process is improving or repeating the same mistakes (a very correctible situation with disciplined delivery leadership)
Speed on the Micro- Versus Macro-Scale
I touched on this somewhat in the previous point, but the point to call out here is that tactical decisions made in the interest of compromising quality for the “upcoming release” can and often do create technical issues that will ultimately make downstream delivery more difficult (i.e., slower and more costly).
As an example, there was a situation in the past where a team integrated technology from multiple vendors that provided the same underlying capability (i.e., the sourcing strategy didn’t have a “preferred provider”, so multiple buys were done over time using different partners, sometimes in parallel). In each case, the desire from the team was to deliver solutions as rapidly as possible in the interest of “meeting customer demand” and they were recognized and rewarded for doing so at speed. The problem with this situation was that the team perceived standards as an impediment to the delivery process and, therefore, either didn’t leverage any or did so on a transactional or project-level basis. Where this became problematic was where there became a need to:
Replace a given vendor – other partners couldn’t be leveraged because they weren’t integrated in a common way
Integrate across partners – the technology stack was different and defined unique to each use case
Run analytics across solutions – data standards weren’t in place so that underlying data structures were in a common format
The point of sharing the example is that, at a micro-level, the team’s approach seems fast, cheap, and appropriate. The accumulation of the technical debt, however, is substantial when you scale and operate under that mindset for an extended period of time, and it does both limit your ability to leverage those investments, migrate to new solutions, introduce new capabilities quickly and effectively, and integrate across individual point solutions where needed. Some form of balance should be in place to optimize the value created and cost of ownership over time. Without it, the technical debt will undermine the business value in time.
Consulting Versus Corporate Environments
Having worked in both corporate and consulting environments, it’s interesting to me that there can be a different perspective on quality depending on where you sit (and the level of governance in place) in relation to the overall delivery.
Generally speaking, it’s somewhat common on the corporate side of the equation to believe that consultants lack the knowledge of your systems and business to deliver solutions you could yourself “if you had the time”. By contrast, on the consulting side, ideally, you believe that clients are thinking of you as a “hired gun” when it comes to implementations, because you’re bringing in necessary skills and capacity to deliver on something they may not have the experience or bench strength to deliver on their own.
So, with both sides thinking they know more than the other and believing they are capable of doing a quality job (no one does a poor job on purpose), why is quality so often left unattended on larger scale efforts?
On this point:
The delivery pressures and unknowns I mentioned above apply regardless of who is executing a project.
A successful delivery in many cases requires a blend of internal and external resources (to the extent they are being leveraged) so there is a balance of internal knowledge and outside expertise to deliver the best possible solution from an objective standpoint.
Finally, you can’t deliver to standards of excellence that aren’t set. I’ve seen and worked in environments (both as a “client” and as a consultant) where there were very exacting standards and expectations of quality and ones where quality wasn’t governed at the level it should be
I didn’t want to belabor this aspect of delivery, but it is interesting how the perspective and influence over quality decisions can be different depending on one’s role in the delivery process (client, consultant, or otherwise).
Wrapping Up
Bringing things back to the overall level, the point of writing this article was to provide some food for thought on the good, fast, cheap concept and the reality that, in larger and more complex delivery situations, the cost of speed isn’t always evaluated effectively. There is no “perfect world”, for certain, but having discipline, thinking through some of the dimensions above, and making sure the tradeoffs made are thoughtful and transparent in nature could help improve value/cost delivered over time.
I hope the ideas were worth considering. Thanks for spending the time to read them. Feedback is welcome as always.
One of the things that I’ve come to appreciate over the course of time is the value of what I call “actionable strategy”. By this, I mean a blend of the conceptual and practical,a framework that can be used to set direction and organize execution without being too prescriptive, while still providing a vision and mental model for leadership and teams to understand and align on the things that matter.
Without a strategy, you can have an organization largely focused on execution, but that tends to create significant operating or technical debt and complexity over time, ultimately having an adverse impact on competitive advantage, slowing delivery, and driving significant operating cost. Similarly, a conceptual strategy that doesn’t provide enough structure to organize and facilitate execution tends to create little impact over time as teams don’t know how to apply it in a practical sense, or it can add significant overhead and cost in the administration required to map its strategic objectives to the actual work being done across the organization (given they aren’t aligned up front or at all). The root causes of these situations can vary, but the important point is to recognize the criticality of an actionable business-aligned technology strategy and its role in guiding execution (and thereby the value technology can create for an organization).
In reality, there are so many internal and external factors that can influence priorities in an organization over time, that one’s ability to provide continuity of direction with clear conceptual outcomes (while not being too hung up on specific “tasks”) can be important in both creating the conditions for transformation and sustainable change without having to “reset” that direction very often. This is the essence of why framework-centric thinking is so important in my mind. Sustainable change takes time, because it’s a mindset, a culture, and way of operating. If a strategy is well-conceived and directionally correct, the activities and priorities within that model may change, but the ability to continue to advance the organization’s goals and create value should still exist. Said differently: Strategies are difficult to establish and operationalize. The less you have to do a larger-scale reset of them, the better. It’s also far easier to adjust priorities and activities than higher-level strategies, given the time it takes (particularly in larger organizations) to establish awareness of a vision and strategy. This is especially true if the new direction represents a departure from what has been in place for some time.
To be clear, while there is a relationship between this topic and what I covered in my article on Excellence By Design, the focus there is more on the operation and execution of IT within an organization, not so much the vision and direction of what you’d ideally like to accomplish overall.
The rest of this article will focus on the various dimensions that I believe compromise a good strategy, how I think about them, and ways that they could create measurable impact. There is nothing particularly “IT-specific” about these categories (i.e., this is conceptually akin to ‘better, faster, cheaper’) and I would argue they could apply equally well to other areas of a business, but differ in how they translate on an operating level.
In relation to the Measures outlined in each of the sections below, a few notes for awareness:
I listed several potential areas to consider and explore in each section, along with some questions that come to mind with each.
The goal wasn’t to be exhaustive or suggest that I’d recommend tracking any or all of them on an “IT Scorecard”, rather to provide some food for thought
My general point of view is that it’s better to track as little as possible from an “IT reporting” standpoint,unless there is intention to leverage those metrics to drive action and decisions. My experience with IT metrics historically is that they are overreported and underleveraged (and therefore not a good use of company time and resources). I touch on some of these concepts in the article On Project Health and Transparency
Innovate
What It Is and Why It Matters
Stealing from my article on Excellence By Design: “Relentless innovation is the notion that anything we are doing today may be irrelevant tomorrow, and therefore we should continuously improve and reinvent our capabilities to ones that create the most long-term value.”
Technology is evolving at a rate faster than most organizations’ ability to adopt or integrate those capabilities effectively. As a result, a company’s ability to leverage these advances becomes increasingly challenging over time, especially to the degree that the underlying environment isn’t architected in a manner to facilitate their integration and adoption.
The upshot of this is that the benefits to be achieved could be marginalized as any attempts to capitalize on these innovations will likely become point solutions or one-off efforts that don’t scale or create a different form of technical debt over time. This is very evident in areas like analytics where capabilities like GenAI and other artificial intelligence-oriented solutions are only as effective as the underlying architecture of the environment into which they are integrated. Are wins possible that could be material from a business standpoint? Absolutely yes. Will it be easy to scale them if you don’t invest in foundational things to enable that? Very likely not.
The positive side of this is that technology is in a much different place than it was ten or twenty years ago, where it can significantly improve or enhance a company’s capabilities or competitive position. Even in the most arcane of circumstances, there likely is an opportunity for technology to fuel change and growth in a digital business environment, whether that is internal to the operations of a company, or through its interactions with customers, suppliers, or partners (or some combination thereof).
Key Dimensions to Consider
Thinking about this area, a number of dimensions came to mind:
Promoting Courageous Leadership
This begins by acknowledging that leadership is critical to setting the stage for innovation over time
There are countless examples of organizations that were market leaders who ultimately lost their competitive advantage due to complacency or an inability to see or respond to changing market conditions effectively
Fueling Competitive Advantage
This is about understanding how technology helps create competitive advantage for a company and focusing in on those areas rather than trying to do everything in an unstructured or broad-based way, which would likely diffuse focus, spread critical resources, and marginalize realized benefits over time
Investing in Disciplined Experimentation
This is about having a well-defined process to enable testing out new business and technology capabilities in a way that is purposeful and that creates longer-term benefits
The process aspect of this important as it is relatively easy to spin up a lot of “innovation and improvement” efforts without taking the time to understand and evaluate the value and implications of those activities in advance. The problem of this being that you can either end up wasting money where the return on investment isn’t significant or that you can develop concepts that can’t easily be scaled to production-level solutions, which will limit their value in practice
Enabling Rapid Technology Adoption
This dimension is about understanding the role of architecture, standards, and governance in integrating and adopting new technical capabilities over time
As an example, an organization with an established component (or micro-service) architecture and integration strategy should be able to test and adopt new technologies much faster than one without them. That isn’t to suggest it can’t be done, but rather that the cost and time to execute those objectives will increase as delivery becomes more of a brute force situation than one enabled by a well-architected environment
Establishing a Culture of Sustainability
Following onto the prior point, as new solutions are considered, tested, and adopted, product lifecycle considerations should come into play.
Specifically, as part of the introduction of something new, is it possible to replace or retire something that currently exists?
At some point, when new technologies and solutions are introduced in a relatively ungoverned manner, it will only be a matter of time before the cost and complexity of the technology footprint will choke an organization’s ability to continue to both leverage those investments and to introduce new capabilities rapidly.
Measuring Impact
Several ways to think about impact:
Competitive Advantage
What is a company’s absolute position relative to its competition in markets where they compete and on metrics relative to those markets?
Market Differentiation
Is innovation fueling new capabilities not offered by competitors?
Is the capability gap widening or narrowing over time?
I separated these first two points, though they are arguably flavors of the same thing, to emphasize the importance of looking at both capabilities and outcomes from a competitive standpoint. One can be doing very well from a competitive standpoint relative to a given market, but have competitors developing or extending their capabilities faster, in which case, there could be risk of the overall competitive position changing in time
Reduced Time to Adopt New Solutions
What is the average length of time between a major technology advancement (e.g., cloud computing, artificial intelligence) becoming available and an organization’s ability to perform meaningful experiments and/or deploy it in a production setting?
What is the ratio of investment on infrastructure in relation to new technologies meant to leverage it over time?
Reduced Technical Debt
What percentage of experiments turn into production solutions?
How easy is to scale those production solutions (vertically or horizontally) across an enterprise?
Are new innovations enabling the elimination of other legacy solutions? Are they additive and complementary or redundant at some level?
Accelerate
What It Is and Why It Matters
“Take as much as time as you need, let’s make sure we do it right, no matter what.” This is a declaration that I don’t think I’ve ever heard in nearly thirty-two years in technology. Speed matters, “first mover advantage”, or any other label one could place upon the desire to produce value at a pace that is at or beyond an organization’s ability to integrate and assimilate all the changes.
That being said, the means to speed is not just a rush to iterative methodology. The number of times I’ve heard or seen “Agile Transformation” (normally followed by months of training people on concepts like “Scrum meetings”, “Sprints”, and “User Stories”) posed as a silver bullet to providing disproportionate delivery results goes beyond my ability to count and it’s unfortunate. Similarly, I’ve heard glorified versions of perpetual hackathons championed, where the delivery process involves cobbling together solutions in a “launch and learn” mindset that ultimately are poorly architected, can’t scale, aren’t repeatable, create massive amounts of technical debt, and never are remediated in production. These are cases where things done in the interest of “speed” actually destroy value over time.
That being said, moving from monolithic to iterative (or product-centric) approaches and DevSecOps is generally a good thing to do. Does this remedy issues in a business/IT relationship, solve for a lack of architecture, standards and governance, address an overall lack of portfolio-level prioritization, or a host of other issues that also affect operating performance and value creation over time? Absolutely not.
The dimensions discussed in this section are meant to highlight a few areas beyond methodology that I believe contribute to delivering value at speed, and ones that are often overlooked in the interest of a “quick fix” (which changing methodology generally isn’t).
Key Dimensions to Consider
Dimensions that are top of mind in relation to this area:
Optimizing Portfolio Investments
Accelerating delivery begins by first taking a look at the overall portfolio makeup and ensuring the level of ongoing delivery is appropriate to the capabilities of the organization. This includes utilization of critical knowledge resources (e.g., planning on a named resource versus an FTE-basis), leverage of an overall release strategy, alignment of variable capacity to the right efforts, etc.
Said differently, when an organization tries to do too much, it tends to do a lot of things ineffectively, even under the best of circumstances. This does not help enhance speed to value at the overall level
Promoting Reuse, Standards, and Governance
This dimension is about recognizing the value that frameworks, standards and governance (along with architecture strategy) play in accelerating delivery over time, because they become assets and artifacts that can be leveraged on projects to reduce risk as well as effort
Where these things don’t exist, there almost certainly will be an increase in project effort (and duration) and technical debt that ultimately will slow progress on developing and integrating new solutions into the landscape
Facilitating Continuous Improvement
This dimension is about establishing an environment where learning from mistakes is encouraged and leveraged proactively on an ongoing basis to improve the efficacy of estimation, planning, execution, and deployment of solutions
It’s worth noting that this is as much an issue of culture as of process, because teams need to know that it is safe, expected, and appreciated to share learnings on delivery efforts if there is to be sustainable improvement over time
Promoting Speed to Value
This is about understanding the delivery process, exploring iterative approaches, ensuring scope is managed and prioritized to maximize impact, and so on
I’ve written separately that methodology only provides a process, not necessarily a solution to underlying cultural or delivery issues that may exist. As such, it is part of what should be examined and understood in the interest of breaking down monolithic approaches and delivering value at a reasonable pace and frequency, but it is definitely not a silver bullet. They don’t, nor will they ever exist.
Establishing a Culture of Quality
In the proverbial “Good, Fast, or Cheap” triangle, the general assumption is that you can only choose two of the three as priorities and accept that the third will be compromised. Given that most organizations want results to be delivered quickly and don’t have unlimited financial resources, the implication is that quality will be the dimension that suffers.
The irony of this premise is that, where quality is compromised repeatedly on projects, the general outcome is that technical debt will be increased, maintenance effort along with it, and future delivery efforts will be hampered as a consequence of those choices
As a result, in any environment where speed is important, quality needs to be a significant focus so ongoing delivery can be focused as much as possible on developing new capabilities and not fixing things that were not delivered properly to begin with
Measuring Impact
Several ways to think about impact:
Reduced Time to Market
What is the average time from approval to delivery?
What is the percentage of user stories/use cases delivered per sprint (in an iterative model)? What level of spillover/deferral is occurring on an ongoing basis (this can be an indicator of estimation, planning, or execution-related issues)?
Are retrospectives part of the delivery process and valuable in terms of their learnings?
Increase in Leverage of Standards
Is there an architecture review process in place? Are standards documented, accessible, and in use? Are findings from reviews being implemented as an outcome of the governance process?
What percentage of projects are establishing or leveraging reusable common components, services/APIs, etc.?
Increased Quality
Are defect injection rates trending in a positive direction?
What level of severity 1/2 issues are uncovered post-production in relation to those discovered in testing pre-deployment (efficacy of testing)?
Are criteria in place and leveraged for production deployment (whether leveraging CI/CD processes or otherwise)?
Is production support effort for critical solutions decreasing over time (non-maintenance related)?
Lower Average Project Cost
Is the average labor cost/effort per delivery reducing on an ongoing basis?
Optimize
What It Is and Why It Matters
Along with the pursuit of speed, it is equally important to pursue “simplicity” in today’s complex technology environment. With so many layers now being present, from hosted to cloud-based solutions, package and custom software, internal and externally integrated SaaS and PaaS solutions, digital equipment and devices, cyber security requirements, analytics solutions, and monitoring tools… complexity is everywhere. In large organizations, the complexity tends to be magnified for many reasons, which can create additional complexities in and across the technology footprint and organizations required to design, deliver, and support integrated solutions at scale.
My experience with optimization historically is that it tends to be too reactive of a process, and generally falls by the wayside when business conditions are favorable. The problem with this is the bloat and inefficiency that tends to be bred in a growth environment, that ultimately reduces the value created by IT with increasing levels of spend. That is why a purposeful approach that is part of a larger portfolio allocation strategy is important. Things like workforce and sourcing strategy, modernization, ongoing rationalization and simplification, standardization and continuous improvement are important to offset what otherwise could lead to a massive “correction” the minute conditions change. I would argue that, similar to performance improvement in software development, an organization should never be so cost inefficient that a massive correction is even possible. For that to be the case, something extremely disruptive should have occurred, otherwise the discipline in delivery and operations likely wasn’t where it needed to be leading up to that adjustment.
I’ve highlighted a few dimensions that are top of mind in regard to ongoing optimization, but have written an entire article on optimizing value over cost that is a more thorough exploration of this topic if this is of interest (Optimizing the Value of IT).
Key Dimensions to Consider
Dimensions that are top of mind in relation to this area:
Reducing Complexity
There is some very simple math related to complexity in an IT environment, which is that increasing complexity drives a (sometimes disproportionate) increase in cost and time to deliver solutions, especially where there is a lack of architecture standards and governance
In areas like Integration and Analytics, this is particularly important, given they are both foundational and enable a significant amount of business capabilities when done well
It is also important to clarify that reducing complexity doesn’t necessarily equate to reducing assets (applications, data solutions, technologies, devices, integration endpoints, etc.), because it could be the case that the number of desired capabilities in an organization requires an increasing number of solutions over time. That being said, with the right integration architecture and associated standards, as an example, the ability to integrate and rationalize solutions will be significantly easier and faster than without them (which is complexity of a different kind)
Optimizing Ongoing Costs
I recently wrote an article on Optimizing the Value of IT, so I won’t cover all that material again here
The overall point is that there are many levers available to increase value while managing or reducing technology costs in an enterprise
That being said, aggregate IT spend can and may increase over time, and be entirely appropriate depending on the circumstances, as long as the value delivered increases proportionately (or in excess of that amount)
Continually Modernizing
The mental model that I’ve had for support for a number of years is to liken it to city planning and urban renewal. Modernizing a footprint is never a one-time event, it needs to be a continuous process
Where this tends to break down in many organizations is the “Keep the Lights On” concept, which suggests that maintenance spend should be minimized on an ongoing basis to allow the maximum amount of funding for discretionary efforts that advance new capabilities
The problem with this logic is that it can tend to lead to neglect of core infrastructure and solutions that then become obsolete, unsupportable, pose security risks, and that approach end of life with only very expensive and disruptive paths to upgrade or modernize them
It would be far easier to carve out a portion of the annual spend allocation for a thoughtful and continuous modernization where these become ongoing efforts, are less disruptive, and longer-term costs are managed more effectively at lower overall risk
Establishing and Maintaining a Workforce Strategy
I have an article in my backlog for this blog around workforce and sourcing strategy, having spent time developing both in the past, so I won’t elaborate too much on this right now other than to say it’s an important component in an organizational strategy for multiple reasons, the largest being that it enables you to flex delivery capability (up and down) to match demand while maintaining quality and a reasonable cost structure
Proactively Managing Performance
Unpopular though it is, my experience in many of the organizations in which I’ve worked over the years has been that performance management is handled on a reactive basis
Particularly when an organization is in a period of growth, notwithstanding extreme situations, the tendency can be to add people and neglect the performance management process with an “all hands, on deck” mentality that ultimately has a negative impact on quality, productivity, morale, and other measures that matter
This isn’t an argument for formula-driven processes, as I’ve worked in organizations that have forced performance curves against an employee population, and sometimes to significant, detrimental effect. My primary argument is that I’d rather have an environment with 2% involuntary annual attrition (conceptually), than one where it isn’t managed at all, market conditions change, and suddenly there is a push for a 10% reduction every three years, where competent “average” talent is caught in the crossfire. These over-corrections cause significant disruption, have material impact on employee loyalty, productivity, and morale, and generally (in my opinion) are the result of neglecting performance management on an ongoing basis
Measuring Impact
Several ways to think about impact:
Increased Value/Cost Ratio
Is the value delivered for IT-related effort increasing in relation to cost (whether the latter is increasing, decreasing, or remaining flat)?
Reduced Overall Assets
Have the number of duplicated/functionally equivalent/redundant assets (applications, technologies, data solutions, devices, etc.) reduced over time?
Lower Complexity
Is the percentage of effort on the average delivery project spent on addressing issues related to a lack of standards, unique technologies, redundant systems, etc. reducing over time?
Lower Technical Debt
What percentage of overall IT spend is committed to addressing quality, technology, end-of-life, or non-conformant solutions (to standards) in production on an ongoing basis?
Inspire
What It Is and Why It Matters
Having written my last article on culture, I’m not going to dive deeply into the topic, but I believe the subject of employee engagement and retention (“People are our greatest asset…”) is often spoken about, but not proportionately acted on in deliberate ways. It is far different, as an example, to tell employees their learning and development is important, but then either not provide the means for them to receive training and education or put “delivery” needs above that growth on an ongoing basis. It’s expedient on a short-term level, but the cost to an organization in loyalty, morale, and ultimately productivity (and results) is significant.
Inspiration matters. I fundamentally believe you achieve excellence as an organization by enrolling everyone possible in creating a differentiated and special workplace. Having worked in environments where there was a contagious enthusiasm in what we were doing and also in ones I’d consider relatively toxic and unhealthy, there’s no doubt on the impact it has on the investment people make in doing their best work.
Following onto this, I believe there is also a distinction to be drawn in engaging the “average” employees across the organization versus targeting the “top performers”. I have written about this previously, but top performers, while important to recognize and leverage effectively, don’t generally struggle with motivation (it’s part of what makes them top performers to begin with). The problem is that placing a disproportionate amount of management focus on this subset of the employee population can have a significant adverse impact, because the majority of an organization is not “top performers” and that’s completely fine. If the engagement, output, and productivity of the average employee is elevated even marginally, the net impact to organizational results should be fairly significant in most environments.
The dimensions below represent a few ways that I think about employee engagement and creating an inspired workplace.
Key Dimensions to Consider
Dimensions that are top of mind in relation to this area:
Becoming an Employer of Choice
Reputation matters. Very simple, but relevant point
This becomes real in how employees are treated on a cultural and day-to-day level, compensated, and managed even in the situation where they exit the company (willingly or otherwise)
Having worked for and with organizations that have had a “reputation” that is unflattering in certain ways, the thing I’ve come to be aware of over time is how important that quality is, not only when you work for a company, but the perception of it that then becomes attached to you afterwards
Two very simple questions to employees that could serve as a litmus test in this regard:
If you were looking for a job today, knowing what you know now, would you come work here again?
How likely would you be to recommend this as a place to work to a friend?
Promoting a Healthy Culture
Following onto the previous point, I recently wrote about The Criticality of Culture, so I won’t delve into the mechanics of this beyond the fact that dedicated, talented employees are critical to every organization, of any size, and the way in which they are treated and the environment in which they work is crucial to optimizing the experience for them and the results that will be obtained for the organization as a whole
Investing in Employee Development
Having worked in organizations where there was both an explicit, dedicated commitment to ongoing education and development and others where there was “never time” to invest in or “delivery commitments” that interfered with people’s learning and growth, the consequent impact on productivity and organizational performance has always been fairly obvious and very negative from my perspective
A healthy culture should create space for people to learn and grow their skills, particularly in technology, where the landscape is constantly changing and there is a substantial risk of skills becoming atrophied if not reinforced and evolved as things change.
This isn’t an argument for random training, of course, as there should be applicability for the skills into which an organization invests on behalf of its employees, but it should be an ongoing priority as much as any delivery effort so you maintain your ability to integrate new technology capabilities as and when they become available over time
Facilitating Collaboration
This and the next dimension are both discussed in the above article on culture, but the overall point is that creating a productive workplace goes beyond the individual employee to encouraging collaboration and seeking the kind of results discussed in my article on The Power of N
The secondary benefit from a collaborative environment is the sense of “connectedness” it creates across teams when it’s present, which would certainly help productivity and creativity/solutioning when part of a healthy, positive culture
Creating an Environment of Transparency
Understanding there are always certain things that require confidentiality or limited distribution (or both), the level of transparency in an environment helps create connection between the individual and the organization as well as helping to foster and engender trust
Reinforcing the criticality of communication in creating an inspiring workplace is extremely obvious, but having seen situations where the opposite is in place, it’s worth noting regardless
Measuring Impact
Several ways to think about impact:
Improved Productivity
Is more output being produced on a per FTE basis over time?
Are technologies like Copilot being leveraged effectively where appropriate?
Improved Average Utilization
Are utilization statistics reflecting healthy levels (i.e., not significantly over or under allocated) on an ongoing basis (assuming plan/actuals are reasonably reflected)?
Improved Employee Satisfaction
Are employee surveys trending in a positive direction in terms of job satisfaction?
Lower Voluntary Attrition
Are metrics declining in relation to voluntary attrition?
Perform
What It Is and Why It Matters
Very simply said: all the aspirations to innovate, grow, and develop capabilities don’t mean a lot if your production environment doesn’t support business and customer needs exceptionally well on a day-to-day basis.
As a former account executive and engagement manager in consulting at various organizations, any account strategy for me always began with one statement: “Deliver with quality”. If you don’t block and tackle well in your execution, the best vision and set of strategic goals will quickly be set aside until you do. This is fundamentally about managing infrastructure, availability, performance of critical solutions, and security. In all cases, it can be easy to operate in a reactive capacity and be very complacent about it, rather than looking for ways to improve, simplify, and drive greater stability, security, and performance over time.
As an example, I experienced a situation where an organization spent tens of millions of dollars annually on production support, planning for things that essentially hadn’t broken yet, but had no explicit plan or spend targeted at addressing the root cause of the issues themselves. Thankfully, we were able to reverse that situation, plan for some proactive efforts that ultimately took millions out of that spend by simply executing a couple projects. In that case, the issue was the mindset, assuming that we had to operate in a reactive rather than proactive way, while the effort and dollars being consumed could have been better applied developing new business capabilities rather than continuing to band-aid issues we’d never addressed.
Another situation that is fairly prevalent today is the role of FinOps in managing cloud costs. Without governance, the convenience of spinning up cloud assets and services can add considerable complexity, cost, and security exposure, all under the promise of shifting from a CapEx to OpEx environment. The reality is that the maturity and discipline required to manage it effectively requires focus so it doesn’t become problematic over time.
There are many ways to think about managing and optimizing production, but the dimensions that come to mind as worthy of some attention are expressed below.
Key Dimensions to Consider
Dimensions that are top of mind in relation to this area:
Providing Reliability of Critical Solutions
Having worked with a client where the health of critical production solutions was in a state where that became the top IT priority, this can’t be overlooked as a critical priority in any strategy
It’s great to advance capabilities through ongoing delivery work, but if you can’t operate and support critical business needs on a daily level, it doesn’t matter
Effectively Managing Vulnerabilities
With the increase in complexity in managing technology environments today, internal and external to an organization, cyber exposure is growing at a rate faster than anyone can manage it fully
To that end, having a comprehensive security strategy, from managing external to internal threats, ransomware, etc. (from the “outside-in”) is critical to ensuring ongoing operations with minimal risk
Evolving Towards a “Zero Trust” Environment
Similar to the previous point, while the definition of “zero trust” continues to evolve, managing a conceptual “least privilege” environment (from the “inside-out”) that protects critical assets, applications, and data is an imperative in today’s complex operating environment
Improving Integrated Solution Performance
Again, with the increasing complexity and distribution of solutions in a connected enterprise (including third party suppliers, partners, and customers), the end user experience of these solutions is an important consideration that will only increase in importance
While there are various solutions for application performance monitoring (APM) on the market today, the need for integrated monitoring, analytics, and optimization tools will likely increase over time to help govern and manage critical solutions where performance characteristics matter
Developing a Culture Surrounding Security
Finally, in relation to managing an effective (physical and cyber) security posture, while a deliberate strategy for managing vulnerability and zero trust are the methods by which risk is managed and mitigated, equally there is a mindset that needs to be established and integrated into an organization for risk to be effectively managed
This dimension is meant to recognize the need to provide adequate training, review key delivery processes (along with associated roles and responsibilities), and evaluate tools and safeguards to create an environment conducive to managing security overall
Measuring Impact
Several ways to think about impact:
Increased Availability
Is the reliability of critical production solutions improving over time and within SLAs?
Lower Cybersecurity Exposure
Is a thoughtful plan for managing cyber security in place, being executed, monitored, and managed on a continuous basis?
Do disaster recovery and business continuity plans exist and are they being tested?
Improved Systems Performance
Are end user SLAs met for critical solutions on an ongoing basis?
Lower Unplanned Outages
Are unplanned outages or events declining over time?
Wrapping Up
Overall, the goal of this article was to share some concepts surrounding where I see the value of strategy for IT in enabling a business at an overall level. I didn’t delve into what the makeup of the underlying technology landscape is or should be (things I discuss in articles like The Intelligent Enterprise and Perspective on Impact Driven Analytics), because the point is to think about how to create momentum at an overall level in areas that matter… innovation, speed, value/cost, productivity, and performance/reliability.
Feedback is certainly welcome… I hope this was worth the time to read it.
I’ve spent a reasonable amount of time in recent years considering data strategy and how to architect an enterprise environment responsive and resilient to change. What’s complicated matters is the many dimensions to establishing a comprehensive data strategy and the pace with which technologies and solutions have and continue to be introduced, none of which appears to be slowing down… quite the opposite. At the same time, the focus on “data centricity” and organizations’ desire to make the most of the insights embedded within and across their enterprise systems has created a substantial pull to drive experimentation and create new solutions aimed at monetizing those insights for competitive advantage. With the recent advent of Generative AI and large language models, the fervor surrounding analytics has only garnered more attention as to the potential it may create, not all to a favorable end.
The problem with the situation is that, not unlike many other technology “gold rush” situations that have occurred over the last thirty-one years I’ve been working in the industry, the lack of structure and discipline (or an overall framework) to guide execution can lead to a different form of technical debt, suboptimized outcomes, and complexity that ultimately doesn’t scale to the enterprise. Hopefully this article will unpack the analytics environment and provide a way to think about the various capabilities that can be brought to bear in a more structured approach, along with the value in doing so.
Ultimately, analytics value is created in insight-driven, orchestrated actions taken, not on presentment or publication of data itself.
Drawing a “Real World” Comparison
The anecdotal hallmark of “traditional business intelligence” is the dashboard, which in many cases, reflects a visual representation of data contained in one or more underlying system, meant to increase end user awareness of the state of affairs, whatever the particular business need may be (this is a topic I’ve peripherally addressed in my On Project Health and Transparency article).
Having leased a new car last summer, I was both impressed and overwhelmed by the level of sophistication available to me through the various displays in the vehicle. The capabilities have come a long way from a bunch of dials on the dashboard with a couple lights to indicate warnings. That being said, there was a simplicity and accessibility to that design. You knew the operating condition of the vehicle (speed, fuel, engine temp, etc.), were warned about conditions you could address (add oil, washer fluid), and situations where expert assistance might be needed (the proverbial “check engine” light).
What impressed me about the current experience design was the level of configurability involved, what I want to see on each of the displays, from advanced operating information, to warnings (exceeding the speed limit, not that this ever happens…), to suggestions related to optimizing engine performance and fuel efficiency based on analytics run over the course of a road trip.
This isn’t very different than the analytics environment available to the average enterprise, the choices are seemingly endless, and they can be quite overwhelming if not managed in some way. The question of modeling the right experience comes down to this: starting with the questions/desired outcome, then working backwards in terms of capabilities and data that need to be brought to bear to address those needs. Historical analytics can feel like it becomes a “data- or source-forward” mental model, when the ideal environment should be defined from the “outcome-backwards”, where the ultimate solution is rooted in a problem (or use case) meant to be solved.
Where Things Break Down
As I stated in the opening, the analytics landscape has gotten extremely complex in recent years and seemingly at an increasing pace. What this can do, as is somewhat the case with large language models and Generative AI right now, is create a lot of excitement over the latest technology or solution without a sense of how something can be used or scaled within and across an enterprise. I liken this to a rush to the “cool” versus the “useful”, and it becomes a challenge the minute it becomes a distraction from underlying realities of the analytics environment.
Those realities are:
Business ownership and data stewardship are critical to identifying the right opportunities and unlocking the value to be derived from analytics. Technology is normally NOT the underlying issue in having an effective data strategy, though disciplined delivery can obviously be a challenge depending on the capabilities of the organization
Not all data is created equal, and it’s important to discriminate in what data is accessed, moved, stored, curated, governed… because there is a business and technology cost for doing so
Technologies and enabling capabilities WILL change, so the way they are integrated and orchestrated is critically important to leveraging them effectively over time
It is easy to develop solutions that solve a specific need or use case but not to scale and integrate them as enterprise-level solutions. In an Intelligent Enterprise, this is where orders of magnitude in value and longer-term competitive advantage is created, across digitally connected ecosystems (including those with partners), starting with effective master data management and extending to newer capabilities that will be discussed below
At an overall level, while it’s relatively easy to create high-level conceptual diagrams or point solutions in relation to data and analytics, it takes discipline and thought to architect an environment that will produce value and agility at scale… that is part of what this article is intended to address.
Thoughts on “Data Centricity”
Given there is value to be unlocked through an effective data strategy, “data centricity” has become fairly common language as an anchor point in discussion. While I feel that calling attention to opportunity areas can be healthy and productive, there is also a risk that concepts without substance (the antithesis of what I refer to as “actionable strategies”) can become more of a distraction than a facilitator of progress and evolution. A similar situation arguably exists with “zero trust” right now, but that’s a topic worthy of its own article at a future date.
In the case of being “data centric”, the number of ways the language can be translated has seemed problematic to me, largely because I fundamentally believe data is only valuable to the extent it drives a meaningful action or business outcome. To that end, I would much rather be “insight-centric” or “value-focused”, “action-oriented”, or some other phrase that leans towards what we are doing with the data we acquire and analyze, not the fact that we have it, can access, store, or display it. Those things may be part of the underlying means to an end, but they aren’t the goal in itself, and place emphasis on the road versus the destination of a journey.
To the extent that “data centricity” drives a conversation on what data a business has that may, if accessed and understood, create value, fuel innovation, and provide competitive advantage, I believe there is value in pursuing it, but a robust and thoughtful data strategy requires end-to-end thinking at a deeper level than a catch phrase or tagline on its own.
What “Good” Looks Like
I would submit that there are two fundamental aspects of having a robust data strategy once you address business ownership and stewardship as a foundational requirement: asking the right questions, and architecting a resilient environment.
Asking the Right Questions
Arguably the heading is a bit misleading here, because inference-based models can suggestimprovements to move from an existing to a desired state, but the point is to begin with the problem statement, opportunity, or desired outcome, and work back to the data, insights, and actions required to achieve that result. This is a business-focused activity and is, therefore, why establishing ownership and stewardship is so critical.
“We can accomplish X if we optimize inventory across Y locations while maintaining a fulfillment window of Z”
The statement above is different than something more “traditional” in the sense of producing a dashboard that shows “inventory levels across locations”, “fulfillment times by location”, etc. that then is intended to inform someone who ultimately may make a decision independent of secondary impacts or, better yet, recommends or enables actions to keep the inventory ecosystem calibrated in a dynamic way that continuously recalibrates to changing conditions, within defined business constraints.
While the example itself may not be perfect, the point is whether we think about analytics as presentment-focused or outcome-focused. To the degree we focus on enabling outcomes, the requirements of the environment we establish will likely be different, more dynamic, and more biased towards execution.
Architecting a Resilient Environment
With the goals identified, the technology challenge becomes about enabling those outcomes, but architecting an environment that can and will evolve as those needs change and as the underlying capabilities continue to advance in what we are able to do in analytics as a whole.
What that means, and the next section will explore, is having a structured and layered approach so that capabilities can be applied, removed, and evolved with minimal disruption to other aspects of the overall environment. This is, at its essence, a modular and composable architecture that enables interoperability through standards-based interaction across the layers of the solution in a way that will accelerate delivery and innovation over time.
The benefit to designing an interoperable environment is simple: speed, cost, and value. As I mentioned in where things tend to break down, in technology, there should always a bias towards rapid delivery. That being said, focusing solely on speed can tend to create a substantial amount of technical debt and monolithic solutions that don’t create cumulative or enterprise-level value. Short-term, they may produce impact, but medium- to longer-term, they make things considerably worse once they have to be maintained and supported and the costs for doing so escalate. Where a well-designed environment can help is in creating a flywheel effect over time to accelerate delivery using common infrastructure, integration standards, and frameworks so that the distance between idea and implementation is significantly reduced.
Breaking Down the Environment
The following diagram represents the logical layers of an analytics environment and some of the solutions or capabilities that can exist at each tier. While the diagram could arguably be drawn in various ways, the reason I’ve drawn it like this is to show the separation of concerns between where content and data originates and ultimately where it’s consumed, along with the layers of processing that can occur in between.
Having the separation of concerns defined and standards (and reference architecture) established, the ability to scale, integrate new solutions and capabilities over time, and retire or modernize those that don’t create the right level of value, becomes considerably easier than when analytics solutions are purpose built in an end-to-end manner.
The next section will elaborate on each the layers to provide more insight on why they are organized in this manner.
Consume and Engage
The “outermost” tier of the environment is the consumption layer, where all of the underlying analytics capabilities of an organization should be brought to bear.
In the interest of transforming analytics, as was previously mentioned in the context of “data centricity”, the dialogue needs to move from “What do you want to see?” in business terms to “What do you want to accomplish and how do you want that to work from an end user standpoint?”, then employing capabilities at the lower level tiers to enable that outcome and experience (both).
The latter dimension is important, because it is possible to deliver both data and insights and not enable effective action, and the goal of a modern analytics environment is to enable outcomes, not a better presentment of a traditional dashboard. This is why I’ve explicitly called out the role of a Digital Experience Platform (DXP) or minimally an awareness of how end users are meant to consume, engage, and interact with the outcome of analytics, ideally as part of an integrated experience that enables or automates action based on the underlying goals.
As analytics continue to move from passive and static to more dynamic and near real-time solutions, the role of data apps as an integrated part of applications or a digital experience for end users (internal or external) will become critical to delivering on the value of analytics investments.
Again, the requirements at this level are defined by the business goals or outcomes to be accomplished, questions to be answered, user workflows to be enabled, etc. and NOT the technologies to be leveraged in doing so. Leading with technologies is almost certainly a way to head down a path that will fail over time and create technical debt in the process.
At an overall level, the reason for separating consumption and thinking of it independent of anything that “feeds” it, is that, regardless of how good the data or insights produced in the analytics environment are, if the end user can’t take effective action upon what’s delivered, there will be little value created in the solution.
Understand and Analyze
Once the goal is established, the capabilities to be brought to bear becomes the next level of inquiry:
If there is a set of activities associated with this outcome that requires workflow, rules, and process automation, orchestration should be integrated into the solution
If defined inputs are meant to be processed against the underlying data and a result dynamically produced, this may be a case where a Generative AI engine could be leveraged
If natural language input is desired, a natural language processing engine should be integrated
If the goal is to analyze the desired state or outcome against the current environment or operating conditions and infer the appropriate actions to be taken, causal models and inference-based analytics could be integrated. This is where causal models take a step past Generative AI in their potential to create value at an enterprise level, though the “describe-ability” of the underlying operating environment would likely play a key role in the efficacy of these technologies over time
Finally, if the goal is simply to run data sets through “traditional” statistical models for predictive analytics purposes (as an example), AI/ML models may be leveraged in the eventual solution
Having referenced the various capabilities above there are three important points to understand in why this layer is critical and separated from the rest:
Any or all of these capabilities may be brought to bear, regardless of how they are consumed by an end user, and regardless of how the underlying data is sourced, managed, and exposed.
Integrating them in ways are standards-based will allow them to be applied as and when needed into various solutions to create considerable cumulative analytical capability at an enterprise level
These capabilities definitely WILL continue to evolve and advance rapidly, so thinking about them in a plug-and-play based approach will create considerable organizational agility to respond and integrate innovations as and when they emerge over time, which translates into long-term value and competitive advantage.
Organize and Expose
There are three main concepts I outlined in this tier of the environment:
Virtualization – how data is exposed and accessed from underlying internal and external solutions
Semantic Layer – how data is modeled for the purpose of allowing capabilities at higher tiers to analyze, process, and present information at lower levels of the model
Data Products – how data is packaged for the purposes of analysis and consumption
These three concepts can be implemented with one or more technologies, but the important distinction being that they offer a representation of underlying data in a logical format that enables analysis and consumption, not necessarily that they are a direct representation of the source data or content itself.
With regard to data products in particular, while there is a significant amount of attention paid to their identification and development, they represent marginal value in an overall data strategy, especially when analytical capabilities and consumption models have evolved to such a great degree. Where data products should be a focus (as a foundational step) is where the underlying organization and management of data is in such disarray that an examination of how to restructure and clean up the environment is important to reducing the chaos that exists in the current state. What that implies, however, is less distractions and potential technical debt by extension, but not the kind of competitive advantage that comes from advanced capabilities and enabled consumption. The other scenario where data products create value in themselves is when they are packaged and marketed for external consumption (e.g., credit scores, financial market data). It’s worth noting in this case, however, that the end customer is assuming the responsibility of analyzing, integrating, and consuming those products as they are not an “end” in themselves in an overall analytics value chain.
Manage, Structure, and Enrich
While I listed a number of different types of solutions that can comprise a “storage” layer in the analytics environment, the best-case scenario would be that it doesn’t exist at all. Where the storage layer creates value in analytics is providing a means to map, associate, enrich, and transform data in ways that would be too time consuming or expensive to do “on the fly” for the purposes of feeding the analytics and consumption tiers of the model. There is certainly value, for instance, in graph databases for modeling complex many-to-many relationships across data sets, marts and warehouses for dealing with structured data, and data lakes for archival, managing unstructured data, and training of analytical models, but where source data can be exposed and streamed directly to the downstream models and solutions, there will be lower complexity, cost, and latency in the overall solution.
Acquire and Transmit
As capabilities continue to advance and consumption models mature, the desire for near real-time analytics will almost certainly dominate the analytics environment. To that end, leveraging event-based processing, whether through an enterprise service or event bus, will be critical. To the degree that enterprise integration standards can be leveraged (and canonical objects, where defined), further simplification and acceleration of analytics efforts will be possible.
Given the varied capabilities across cloud platforms (AWS, Azure, and GCP), not to mention the probability that data will be distributed between enterprise systems that could be hosted in a different cloud platform than its documents (as those in Office 365), the ability to think critically about how to integrate and synthesize across platforms is also important. Without a defined strategy for managing multi-cloud in this domain in particular, costs for egress/ingress of data could be substantial depending on the scale of the analytics environment itself, not to mention the additional complexities that would be introduced into governance and compliance efforts surrounding duplicated content across cloud providers.
Generate and Provide
The lowest tier of the model is the simplest to describe, given it’s where data and content originate, which can be a combination of applications, databases, digital devices and equipment, and so forth, internal and external to an organization. Back to the original point on business ownership and stewardship of data, if the quality of data emanating from these sources isn’t managed and governed, everything downstream will bear the fruit of the poisoned tree depending on the degree of issues involved.
Given the amount of attention given to large language models and GenAI right now, I thought it was worth noting that I consider these as another form of content generation more logically associated with the other types of solutions at this tier of the analytics model. It could be the case that generated content makes its way through all the layers as a “data set” delivered directly to a consumer in the model, but by orienting and associating it with the rest of the sources of data, we create the potential to apply other capabilities at the next tiers of processing to that generated content, and thereby could enrich, analyze, and do more interesting things with it over time.
Wrapping Up
As I indicated at the opening, the modern analytics environment is complex and highly adaptive, which presents a significant challenge to capturing the value and competitive advantage that is believed to be resident in an organization’s data.
That being said, through establishing the right level of business ownership, understanding the desired outcomes, and applying disciplined thinking in how an enterprise environment is designed and constructed, there can be significant and sustainable value created for an enterprise.
I hope the ideas were thought provoking. I appreciate those taking the time to read them.
Given the challenging economic environment, I thought it would be a good time to revisit something that was an active part of my work for several years, namely IT cost optimization.
In the spirit of Excellence by Design, I don’t consider cost optimization to be a moment in time activity that becomes a priority on a periodic (“once every X years”) or reactive basis. Optimizing the value/cost ratio is something that should always be a priority in the interest of having disciplined operations, maintaining organizational agility, technical relevance, and competitive advantage.
In the consulting business, this is somewhat of a given, as most clients want more value for the money they spend on an annualized basis, especially if the service is something provided over a period of time. Complacency is the fastest path to lose a client and, consequently, there is a direct incentive to look for ways to get better at what you do or provide equivalent service at a lower cost to the degree the capability itself is already relatively optimized.
On the corporate side, however, where the longer-term ramifications of technology decisions bear out in accumulated technical debt and complexity, the choices become more complex as they are less about a project, program, or portfolio and become more focused on the technology footprint, operating model, and organizational structure as a whole.
To that end, I’ll explore various dimensions of how to think about the complexity and makeup of IT from a cost perspective along with the various levers to explore in how to optimize value/cost. I’m being deliberate in mentioning both because it is very easy to reduce costs and have an adverse impact on service quality or agility, and that’s why thoughtful analysis is important in making informed choices on improving cost-efficiency.
Framing the Problem
Before looking at the individual dimensions, I first wanted to cover the simple mental model I’ve used for many years in terms of driving operating performance:
The model above is based on three connected components that feed each other in a continuous cycle:
Transparency
We can’t govern what we can’t see. The first step in driving any level of thoughtful optimization is having a fact-based understanding of what is going on
This isn’t about seeing or monitoring “everything”. It is about understanding thecritical, minimum information that is needed to make informed decisions and then obtaining as accurate a set of data surrounding those points as possible.
Governance
With the above foundation in place, the next step is to have leadership engagement to review and understand the situation, and identify opportunities to improve.
This governance is a critical step in any optimization effort because, if there are not sustainable organizational or cultural changes made in the course of transforming, the likelihood of things returning to a similar condition will be relatively high.
Improvement
Once opportunities are identified, executing effectively on the various strategies becomes the focus, with the goal of achieving the outcomes defined through the governance process
The outcomes of this work should then be reflected in the next cycle of operating metrics and the cycle can be repeated on a continuing basis.
The process for optimizing IT costs is no different than what is expressed here: understand the situation first, then target areas of improvement, make adjustments, continue. It’s a process, not a destination. From here, we’ll explore the various dimensions of complexity and cost within IT, and the levers to consider in adjusting them.
At an Operating-Level
Before delving into the footprint itself, a couple areas to consider at an overall level are portfolio management and release strategy.
Portfolio management
Given that I am mid-way through writing an article on portfolio management and am also planning a separate one on workforce and sourcing strategy, I won’t explore this topic much beyond saying that having a mature portfolio management process can help influence cost-efficiency.
That being said, I don’t consider ineffective portfolio management to be a root cause of IT value/cost being imbalanced. An effective workforce and sourcing strategy that aligns variable capacity to sources of demand fluctuation (within reasonable cost constraints) should enable IT to deliver significant value even during periods of increased business demand. That being said, a lack of effective prioritization, disciplined estimation and planning, resource planning, and sourcing strategy in combination with each other can have significant and harmful effects on cost-efficiency and, therefore, generally provide opportunities for improvement.
Some questions to consider in this area:
Is prioritization effective in your organization? When “priority” effort arise, are other ongoing efforts stopped or delayed to account for them or is the general trend to take on more work without recalibrating existing commitments?
Are estimation and planning efforts benchmarked, reviewed, analyzed and improved, so the integrity of ongoing prioritization and slotting of projects can be done effectively?
Is there a defined workforce and sourcing strategy to align variable capacity to fluctuating demand so that internal capacity can be reallocated effectively and sourcing scaled in a way that doesn’t disproportionately have an adverse impact on cost? Conversely, can demand decline without significant need for recalibration of internal, fixed capacity? There is a situation I experienced where we and another part of the organization took the same level of financial adjustment, but they had to make 3x the level of staffing adjustment given we were operating under a defined sourcing strategy and the other organization wasn’t. This is an important reason to have a workforce and sourcing strategy.
Is resource planning handled on an FTE (e.g., role-based) or resource-basis (e.g., named resource), or some combination thereof? What is the average utilization of “critical” resources across the organization on an ongoing basis?
Release strategy
This is an area that often seems overlooked in my experience (outside product delivery environments) as a means to both improve delivery effectiveness, manage cost, and improve overall quality.
Having a structured release strategy that accounts for major and minor releases, with defined criteria and established deployment windows, versus an arbitrary or ad-hoc approach can be a significant benefit both from an IT delivery and business continuity perspective. Generally speaking, delivery cycles (in a non-CI/CD, DevSecOps-oriented environment) tend to consume time and energy that slows delivery progress. The more windows that exist, the more disruption that occurs over a calendar year. When those windows are allowed to occur on an ad-hoc basis, the complexities of integration testing, configuration management, and coordination from a project, program, and change management perspective tends to increase proportional to the number of release windows involved. Similarly, the risk of quality issues occurring within and across a connected ecosystem increases as the process for stabilizing and testing individual solutions, integrating across solutions, and managing post-deployment production issues is spread across multiple teams in overlapping efforts. Where standard integration patterns and reference architecture is in place to govern interactions across connected components, there are means to manage and mitigate risk, but generally speaking, it’s better and more cost-effective to manage a smaller set of larger, scheduled release windows than allow a more random or ad-hoc environment to exist at scale.
Applications
In the application footprint, larger organizations or those built through acquisition tend to have a fairly diverse and potentially redundant application landscape, which can lead to significant cost and complexity, both in maintaining and integrating the various systems in place. This is also true when there is a combination of significant internally (custom) developed solutions working in concert with external SaaS solutions or software packages.
Three main levers can have a significant influence along the lines of what I discuss in The Intelligent Enterprise:
Ecosystem Design
Whether one chooses to refer to this as business architecture, domain-driven design, component architecture, or something else, the goal is to identify and govern a set of well-defined connected ecosystems that are composable, made up of modular components that provide a clear business (or technical) capability or set of services
This is critical enabler to both optimizing the application footprint as well as promoting interoperability and innovation over time, as new capabilities can be more rapidly integrated into a standards-based environment
Where complexity comes about is where custom or SaaS/package solutions are integrated in a way that blurs these component boundaries and creates functional overlaps that create technical debt, redundancy, data integrity issues, etc.
Integration strategy
With a set of well-defined components, the secondary goal is to leverage standard integration patterns with canonical objects to promote interoperability, simplification, and ongoing evolution of the technology footprint over time.
Without standards for integration, an organization’s ability to adopt new, innovative technologies will be significantly hindered over time and the leverage of those investments marginalized, because of the complexity involved in bringing those capabilities into the existing environment rapidly without having refactor or rewrite a portion of what exists to leverage them.
At an overall level, it is hard to argue that technologies are advancing at a rate faster than any organization’s ability to adopt and integrate them, so having a well-defined and heavily leveraged enterprise integration strategy is critical to long-term value creation and competitive advantage.
Application Rationalization
Finally, with defined ecosystems and standards for integration, having the courage and organizational leadership to consolidate like solutions to a smaller set of standard solutions for various connected components can be a significant way to both reduce cost and increase speed-to-value over time.
I deliberately focused on the organizational aspects of rationalization, because one of the most significant obstacles in technology simplification is the courageous leadership needed to “pick a direction” and handle the objections that invariably result in those tradeoff decisions being made.
Technology proliferation can be caused by a number of things, but organizational behaviors can certainly contribute when two largely comparable solutions exist without one of them being retired solely based on resistance to change or perceived control or ownership associated with a given solution.
At a capability-level, evaluating similar solutions, understanding functional differences and associating the value with those dimensions is a good starting point for simplifying what is in place. That being said, the largest challenge in application rationalization doesn’t tend to be identifying the best solution, it’s having the courage to make the decision, commit the investment, and execute on the plan given “new projects” tend to get more organizational focus and priority in many companies than cleaning up what they already have in place. In a budget-constrained environment, the new, shiny thing tends to win in a prioritization process, which is something I’ll write about in a future article.
Overall, the larger the organization, the more opportunity may exist in the application domain, and the good news is that there are many things that can be done to simplify, standardize, rationalize, and ultimately optimize what’s in place in ways that both reduce cost and increase the agility, speed, and value that IT can deliver.
Data
The data landscape and associated technologies, especially when considering advanced analytics, has significantly added complexity (and likely associated cost) in the last five to ten years in particular. With the growing demand for AI/ML, NLP, and now Generative AI-enabled solutions, the ability to integrate, manage, and expose data, from producer to ultimate consumer has taken on significant criticality.
Some concepts that are directionally important in my opinion in relation to optimizing value/cost in data and analytics enablement:
Managing separation of concerns
Similar to the application environment, thinking of the data and analytics environment (OLTP included) as a set of connected components with defined responsibilities, connected through standard integration patterns is important to reducing complexity, enabling innovation, and accelerating speed-to-value over time
Significant technical debt can be created where the relationship of operational data stores (ODS), analytics technologies, purpose-built solutions (e.g., graph or time series databases) master data management tools, data lakes, lake houses, virtualization tools, visualization tools, data quality tools, and so on are not integrated in clear, purposeful ways.
Where I see value in “data centricity” is in the way it serves as a reminder to understand the value that can be created for organizations in leveraging the knowledge embedded within their workforce and solutions
I also, however, believe that value will be unlocked over time through intelligent applications that leverage knowledge and insights to accelerate business decisions, drive purposeful collaboration, and enable innovation and competitive advantage.Data isn’t the outcome, it’s an enabler of those outcomes when managed effectively.
Minimizing data movement
The larger the landscape and number of solutions involved in moving source data from the original producer (whether it’s a connected application, device, or piece of equipment) to the end consumer (however that consumption is enabled) has a significant impact on innovation and business agility.
As such, concepts like data mesh / data fabric, enabling distributed sourcing of data in near-real time with minimized data movement to feed analytical solutions and/or deliver end user insights is critical in thinking through a longer-term data strategy.
In a perfect world, where data enrichment is not a critical requirement, the ability to virtualize, integrate, and expose data across various sources to conceptually “flatten” the layers of the analytics environment is an area where end consumer value can be increased while reducing cost typically associated with ETL, storage, and compute spread across various components of the data ecosystem
Concepts like zero ETL, data sharing, and virtualization are also key enablers that have promise in this regard
Limiting enabling technologies
As in the application domain, the more diverse and complex a data ecosystem is, the likelihood that a diverse set of overlapping technologies is in place, with overlapping or redundant capabilities.
At a minimum, a thoughtful process for reviewing and governing any new technology introductions, to evaluate how they complement, replace, or are potentially redundant or duplicative with solutions already in place is an important capability to have in place
Similarly, it is not uncommon to introduce new technologies with somewhat of a “silver bullet” mindset, without considering the implications for supporting or operating those solutions, which can increase cost and complexity, or having a deliberate plan to replace or retire other solutions that provide a similar capability in the process.
Simply said, technical debt accumulates over time, through a set of individually rationalized and justified, but overall suboptimized short-term decisions.
Rationalize, simplify, standardize
Finally, where defined components exist, data sourcing and movement is managed, and technologies introductions are governed, there should be an ongoing effort to modernize, simplify, and standardize what is already in place.
Data solutions can tend to be very “purpose-built” in their orientation to the degree that the enable a specific use case or outcome. The problem that occurs in this situation is if the desired business architecture becomes the de facto technical architecture and significant complexity is created in the process.
Using a parallel, smaller scale analogy, there is a reason that logical and physical data modeling are separate activities in application development (the former in traditional “business design” versus the latter being part of “technical design” in waterfall-based approaches). What makes sense from a business or logical standpoint likely won’t be optimized if architected as defined in that context (e.g., most business users don’t think intuitively in third normal form, nor should they have to).
Modern technologies allow for relatively cheap storage and giving thought to how the underlying physical landscape should be designed from producer to consumer is critical in both enabling insight delivery at speed, but also doing so within a managed, optimized technology environment.
Overall, similar to the application domain, there are significant opportunities to enable innovation and speed-to-value in the data and analytics domain, but a purposeful and thoughtful data strategy is the foundation for being cost-effective and creating long-term value.
Technologies
I’ve touched on technologies through the process of discussing optimization opportunities in both the application and data domains, but it’s important to understand the difference between technology rationalization (the tools and technologies you use to enable your IT environment) and application or data rationalization (the solutions that leverage those underlying technologies to solve business problems).
The process for technology simplification is the same as described in the other two domains, so I won’t repeat the concepts here beyond reiterating that a strong package or technology evaluation process (that considers the relationship to existing solutions in place) and governance of new technology introductions with explicit plans to replace or retire legacy equivalents and ensure organizational readiness to support the new technologies in production is critical to optimizing value/cost in this dimension.
Infrastructure
At an overall level, unless there is a significant compliance, competitive, privacy, or legal reason to do so, I would argue that no one should be in the infrastructure business unless it IS their business. That may be a somewhat controversial point-of-view, but at a time when cloud and hosting providers are both established and mature, arguing the differentiated value of providing (versus managing) these capabilities within a typical IT department is a significant leap of faith in my opinion. Internal and external customer value and innovation is created in the capabilities delivered through applications, not the infrastructure, networking, and storage underlying those solutions. This isn’t to say these capabilities aren’t a critical enabler. They definitely are, though, the overall organizational goal in infrastructure from my perspective should be to ensure quality of service at the right cost (through third party providers to the maximum extent possible), and then manage and govern the reliability and performance of that set of environments, focusing on continuous improvement and enabling innovation as required by consuming solutions over time.
There are a significant number of cost elements associated with infrastructure, a lot of financial allocations involved, and establishing TCO through these indirect expenses can be highly complex in most organizations. As a result, I’ll focus on three overall categories that I consider significant and acknowledge there is normally opportunity to optimize value/cost in this domain beyond these three alone (cloud, hosted solutions, and licensing). This is partially why working with a defined set of providers and managing and governing the process can be a way to focus on quality of service and desired service levels within established cost parameters versus taking on the challenge of operationalizing a substantial set of these capabilities internally.
Certainly, a level of core network and cyber security infrastructure is necessary and critical to an organization under any circumstances, something I will touch on in a future article on the minimum requirements to run an innovation-centric IT organization, but even in those cases, that does not imply or require that those capabilities be developed or managed internally.
Cloud
With the ever-expanding set of cloud-enabled capabilities, there are three critical watch items that I believe have significant impact on cost optimization over time:
Innovation
Cloud platform providers are making significant advancements in their capabilities on an annual basis, some of which can help enable innovation
To the extent that some of the architecture and integration principles above are leveraged, and a thoughtful, disciplined process is used to evaluate and manage introduction of new technologies over time, organizations can benefit from their leverage of cloud as a part of their infrastructure strategy
Multi-cloud Integration
The reality of cloud providers today is also that no one is good at everything and there is differentiated value in various services provided from each of them (GCP, Azure, AWS)
The challenge is how to integrate and synthesize these differentiated capabilities in a secure way without either creating significant complexity or cost in the process
Again, having a modular, composable architecture mindset with API- or service-based integration is critical in finding the right balance for leveraging these capabilities over time
Where significant complexity and cost can be created is where data egress comes into play from one cloud platform to another and, consequently, the need for such data movement should be minimized in my opinion to situations where the value of doing so (ideally without persisting the data in the target platform) greatly outweighs the cost to operate in that overall environment
FinOps Discipline
The promise of having managed platforms that convert traditional capex to opex is certainly an attractive argument for moving away from insourced and hosted solutions to the cloud (or a managed hosting provider for that matter). The challenge is in having a disciplined process for leveraging cloud services, understanding how they are being consumed across an organization, and optimizing their use on an ongoing basis.
Understandably, there is not a direct incentive for platform providers to optimize this on their own and tools largely provide transparency into spend related to consumption of various services over time.
Hopefully, as these providers mature, we’ll see more of an integrated platform within and across cloud providers to help continuously optimize a footprint so that it provides reliability and scalability, but also without promoting over provisioning or other costs that don’t provide end customer value in the process.
Given the focus of this article is cost optimization and not cloud strategy, I’m not getting into cloud modernization, automation and platform services, containerization of workloads, or serverless computing, though arguably some of those also can provide opportunities to enable innovation, improve reliability, enable edge-based computing, and optimize value/cost as well.
Internally Managed / Hosted
Given how far we are into the age of cloud computing, I’m assuming that legacy environments have largely been moved into converged infrastructure. In some organizations, this may not be the case and should be evaluated along with the potential for outsourcing the hosting and management of these environments where possible (and competitive) at a reasonable value/cost level.
One interesting anecdote is how organizations don’t tend to want to make significant investments in modernizing legacy environments, particularly those in financial services resting on mainframe or midrange computing solutions. That being said, given that they are normally shared resources, as the burden of those costs shift (where teams selectively modernize and move off those environments) and allocations of the remaining MIPS and other hosting charges are adjusted, the priority in revisiting those strategies tends to change. Being proactive on modernization should be a continuous, proactive process rather than a reactive one, because the resulting technology decisions can otherwise be suboptimized and turned into lift-and-shift based approaches versus true modernization or innovation opportunities (I’d consider this under the broader excellence topic of relentless innovation).
Licensing
The last infrastructure dimension that I’d call out in relation to licensing. While I’ve already addressed the opportunity to promote innovation and optimize expense through rationalizing applications, data solutions, or underlying technologies individually, there are three other dimensions that are worth consideration:
Partner Optimization
Between leverage of multi-year agreements on core, strategic platforms and consolidation of tools (even in a best-of-breed environment) to a smaller set of strategic, third-party providers, there are normally opportunities to reduce the number of technology partners and optimize costs in large organizations
The watch item would be to ensure such consolidation efforts consider volatility in the underlying technology environment (e.g., the commitment might be too long for situations where the pace of innovation is very high) while also ensuring conformance to the component and integration architecture strategies of the organization so as not to create dependencies that would make transition of those technologies more complex in the future
Governance and Utilization
Where licensing costs are either consumption-based or up for renewal, having established practices for revisiting the value and usage of core technologies over time can help in optimization. This can also be important in ensuring compliance to critical contract terms where appropriate (e.g., named user scenarios, concurrent versus per-seat agreements)
In one example a number of years ago, we decided to investigate indirect expense coming through software licenses and uncovered nearly a million dollars of software that had been renewed on an annual basis that wasn’t being utilized by anyone. The reality is that we treated these as bespoke, fixed charges and no one was looking at them at any interval. All we needed to do in that case was pay attention and do the homework.
Transition Planning
The most important of these three areas is akin to having a governance process in place.
With regard to transition, establishing a companion process to the software renewal cycle for critical, core technologies (i.e., those providing a critical capability or having significant associated expense). This process would involve a health check (similar to package selection, but including incumbent technologies/solutions) at a point commensurate with the window of time it would take to evaluate and replace the solution if it was no longer the best option to provide a given capability.
Unfortunately, depending on the level of dependency that exists for third-party solutions, it is not uncommon for organizations to lack a disciplined process to review technologies in advance of their contractual renewal period and be forced to extend their licenses because of a lack of time to do anything else.
The result can be that organizations deploy new technologies in parallel with ones that are no longer competitive purely because they didn’t plan in advance for those transitions to occur in an organic way
Similar to the other categories, where licensing is a substantial cost component of IT expense, the general point is to be proactive and disciplined about managing and governing it. This is a source of overhead that is easy to overlook and that can create undue burden on the overall value/cost equation.
Services
I’m going to write on workforce and sourcing strategy separately, so I won’t go deeply into this topic or direct labor in this article beyond a few points in each.
In optimizing cost of third-party provided services, a few dimensions come to mind:
Sourcing Strategy
Understanding and having a deliberate mapping of primary, secondary and augmentation partners (as appropriate) for key capabilities or portfolios/solutions is the starting point for optimizing value/cost
Where a deliberate strategy doesn’t exist, the ability to monitor, benchmark, govern, manage, and optimize will be both complex and effective only on a limited basis
Effective sourcing and certain approaches to how partners are engaged can also be a key lever in both enabling rapid execution of key strategies, managing migration across legacy and modernized environments, establishing new capabilities where a talent base doesn’t currently exist internal to an organization, and in optimizing expense that may be either fragmented across multiple partners or enabled through contingency labor in ad-hoc ways, all of which can help optimize the value/cost ratio on an ongoing basis
Vendor Management
Worth noting that I’m using the word “vendor” here because the term is fairly well understood and standard when it comes to this process. In practice, I never use the word “vendor” in deference to “partner” as I believe the latter signals a healthy approach and mindset when it comes to working with third-parties.
Having worked in several consulting organizations over a number of years, it was very easy to tell which clients operated in a vendor versus a partnership mindset and the former of the two can be a disincentive to making the most of these relationships
That being said, organizations should have an ongoing, formalized process for reviewing key partner relationships, performance against contractual obligations, on-time delivery commitments, quality expectations, management of change, and achievement of strategic partner objectives.
There should also be a process in place to solicit ongoing feedback both on how to improve effectiveness and the relationship but also to understand and leverage knowledge and insights a partner has on industry and technology trends and innovation opportunities that can further increase value/cost performance over time.
Contract Management
Finally, having a defined, transparent, and effective process for managing contractual commitments and the associated incentives where appropriate can also be important to optimizing overall value/cost
It is generally true that partners don’t deliver to standards that aren’t established and governed
Defining service levels, quality expectations, utilizing fixed price or risk sharing models and so on and then reviewing and holding both partners and the internal organization working with those partners accountable to those standards is important in having both a disciplined operating and a disciplined delivery environment
There’s nothing wrong with assuming everyone will do their part when it comes to living into the terms of agreements, but there also isn’t harm in keeping an eye on those commitments and making sure that partner relationships are held to evolving standards that promote maturity, quality, and cost effectiveness over time
Similar to other categories, the level of investment in sourcing, whether through professional service firms or contingent labor, should drive the level of effort involved in understanding, governing, and optimizing it, but some level of process and discipline should be in place almost under any scenario.
Labor
The final dimension to optimizing value and cost is direct labor. I’m guessing, in writing this, that it’s fairly obvious I put this category last and I did so intentionally. It is often said that “employees are the greatest source of expense” in an organization. Interestingly enough “people are our greatest asset” has also been said many times as well.
In the section on portfolio management, I mentioned the importance of having a workforce and sourcing strategy and understanding the relationship between the alignment of people to demand on an ongoing basis. That is a given and should be understood and evaluated with a critical eye towards how things flex and adjust as demand fluctuates. It is also a given and assumed that an organization focused on excellence should be managing performance on a continuing basis (including times of favorable market conditions) so as not to create organizational bloat or ineffectiveness. Said differently, poor performance that is unmanaged in an organization drags down average productivity, has an adverse impact on quality, and ultimately a negative impact on value cost because the working capacity of an organization isn’t being applied to ongoing demand and delivery needs effectively. Where this is allowed to continue unchecked over too long a duration, the result may be an over-correction that also can have adverse impacts on performance, which is why it should be an ongoing area of focus by comparison with an episodic one.
Beyond performance management, I believe it’s important to think of all of the expense categories before this one to be variable, which is sometimes not the case in the way they are evaluated and managed. If non-direct labor expense is substantial, a different question to consider is the relative value of “working capacity” (i.e., “knowledge workers”) by comparison with expense consumed in other things. Said differently, a mental model that I used with a team in the past was that “every million dollars we save in X (insert dimension or cost element here… licensing, sourcing, infrastructure, applications) is Y people we can retain to do meaningful work.”
Wrapping Up
Understanding that this has been a relatively long article, but still only a high-level treatment of a number of these topics, hopefully it has been useful in calling out many of the opportunities that are available to promote excellence in operations and optimize value/cost over time.
In my experience, having been in multiple organizations that have realigned costs, it takes engaged and courageous leadership to make thoughtful changes versus expedient ones… it matters… and it’s worth the time invested to find the right balance overall. In a perfect world, disciplined operations should be a part of the makeup of an effectively led organization on an ongoing basis, not the result of a market correction or fluctuation in demand or business priorities.
Excellence always matters, quality and value always matter. The discipline it takes to create and manage that environment is worth the time it takes to do it effectively.
Thank you for taking the time to read the thoughts. As with everything I write, feedback and reactions are welcome. I hope this was worth the investment in time.
I’ve mentioned the concept of “framework-centric design” in a couple articles now, and the goal here is to provide some clarity with regard to where I believe technology and enterprise architecture strategy is heading. A challenge with trying to predict the future of this industry is the number of variables involved, including how rapidly various capabilities evolve, new technologies are introduced, skills of the “average developer” advance along with the tools enabling them to perform their work, methods of integrating and analyzing data arise, etc. I will make various assertions here. Undoubtedly some things will come to pass and others will not (and that’s ok), but the goal is to look forward and consider the possibilities in the interest of stirring discussion and exploration. Innovation is borne out of such ideas, and hopefully it will be worth the read.
That being said, I believe certain things will drive evolution in how digital business will operate:
A more “open systems” approach will be driven by market demand that reduces proprietary approaches (e.g., on various SaaS and ERP platforms) that constrain or inhibit API-based interactions and open data exchange between applications
Technologies will be introduced so rapidly that organizations will be forced to revisit integration in a way that enables accelerated adoption of new capabilities to remain competitive
Application architecture will evolve to where connected ecosystems and strategic outcomes become the focus of design, not the individual components themselves.
As an extension of the previous point, secure digital integration between an organization and its customers, partners, and suppliers will become more of a collaborative, fluid process than the transaction-centric model that is largely in place today (e.g., product design through fulfillment versus simple order placement).
“Data-centric” thinking will be replaced by intelligent ecosystems and applications as the next logical step in its evolution. Data quality processes and infrastructure will be largely supplanted by AI/ML technology that interprets and resolves discrepancies and can autocorrect source data in upstream systems without user intervention, overcoming the behavioral obstacles that inhibit significant progress in this domain today.
Cloud-enabled technology will reach edge computing environments to the degree that cloud native architecture becomes agnostic to the deployment environment and enables more homogenous design across ecosystems (said differently, we can design towards an “ideal state” without artificially constraining solutions to a legacy hosted/on-prem environment). Bandwidth will decide where workloads execute, not the physical environment.
Again, some of the above assertions may seem very obvious or likely where others are more speculative.
The point is to look at the cumulative effect of these changes on digital business and ask ourselves whether the investments we’re making today are taking us where we need to be, because the future will arrive before we expect or will be prepared for it.
The Emerging Ecosystem
In the world of application architecture, the shift towards microservices, SaaS platforms, and cloud native technologies (to name a few) seem to have pulled us away from a more strategic conceptualization of the role ecosystems play in the future environment.
Said simply: It’s not about the technology, it’s about the solution. Once you define the solution architecture, the technology part is solvable.
Too many discussions in my experience start with a package, or a technology, or an infrastructure concept without a fundamental understanding of the problem being solved in the first place, let alone a concept of what “good” would look like in addressing that problem at a logical (i.e., solution architecture) level.
Looking back in time, we started with a simple premise of automating activities performed by “knowledge workers” to promote efficiency, consistency, collaboration, and so on, where the application was the logical center of the universe. In some respects, this can still be the case where SaaS platforms, ERP systems, and even some custom built (and much beloved) home grown solutions tend to become dominant players in the IT landscape. Those solutions then force a degree of adaptation of the systems around them to conform to their integration and data models, with constraints generally defined by the builders of that core system. In a relatively unstructured or legacy environment, that could lead to a significant amount of point-to-point integration, custom solutions, and ultimately technical debt. What seems like a good solution in these cases can eventually hinder evolution as those primary elements in the technology footprint effectively become a limiting constraint on advancing the overall capabilities of the ecosystems they inhabit.
Moving one step in the right direction, we decouple systems and move towards a level of standard integration, whether through canonical objects or other means, that provides a means to replace systems more easily over time. In the case of a publish-and-subscribe based approach, there is also a way to support distributed transactions (e.g., address change in a system of record being promoted to multiple, dependent downstream applications) in a relatively linear sequential manner (synchronous or otherwise).
In a way, the “enterprise middleware” discussion has felt like a white whale of its own to me, having seen many attempts at it fail over years of delivery work, from the CORBA/DCOM days through many iterations of “EAI” technologies since. The two largest problems I’ve seen in this regard:scope and priorities. Defining the transactions/objects that matter, modeling them properly from a business standpoint, and enforcing standardization to a potentially significant number of new and legacy systems is a very difficult thing to do. I’ve seen “enterprise integration” over-scoped to the point it doesn’t create value (e.g., standards for standards sake), which then undermines the credibility of the work that actually could reduce complexity, promote resiliency and interoperability, and ultimately create speed to value in delivery. The other somewhat prevalent obstacle is the lack of leadership engagement on the value of standard integration itself, to the point that it is de-prioritized in the course of ongoing delivery work (e.g. date versus quality issue). In these situations, teams commit to “come back” and retrofit integrations to a strategic model, but ultimately never do, and the integrity of the overall integration architecture strategy falls apart for a lack of effective governance and adoption during execution.
Even with the best defined and executed integration strategy, my contention is that they fall short, because the endgame is not about modeling transactions, it’s about effective digital orchestration.
The shift in mental model is a pivot from application and transaction-centric thinking to an ecosystem-centric approach, where we look at the set of components in place as a whole and the ways in which they interoperate to bring about meaningful business outcomes. Effective digital orchestration is at the center of how the future ecosystems need to operate.
The goal in the future state is to think of the overall objective (e.g., demand generation for a sales ecosystem, production for manufacturing, distribution for transportation and logistics), then examine how each of the individual ecosystem components enable or support those overall goals, how they interoperate, and what the coordination of those elements needs to be across various use cases (standard and exception based) in an “ideal state” to optimize business outcomes. The applications move from the center of the model to components as endpoints and the process by which they interact is managed in the center through defined but highly configurable process orchestration. The configurability is a critical dimension because, as I will address in the data section below, the continuous optimization of that process through AI/ML is part of how the model will eventually change the way digital enterprises perform.
Said slightly differently, in an integration-centric model, transactions are initiated, either in a publish-and-subscribe or request-response manner, they execute, and complete. In an orchestration-centric model, the ecosystem itself is perpetually “running” and optimizing in the interest of generating its desired business outcomes. In the case of a sales ecosystem, as an example, monitoring associated with a lag in demand could automatically initiate lead generation processes that request actions executed through a CRM solution. In manufacturing, delays in a production line could automatically trigger actions in downstream processes or equipment to limit the impact of those disruptions on overall efficiency and output. The ecosystem becomes “self-aware” and therefore more effective in delivering on overall value than its individual components can on their own.
With a digitally connected ecosystem in place, the logical question becomes, “ok, what next?” Well, once you have discrete components that are integrated and orchestrated in a thoughtful manner that drives business outcomes, there are two ways to extend that value further:
Optimize both the performance of individual components and the overall ecosystem through embedded AI/ML (something I’ll address in the next section)
Connect that ecosystem internally to other intelligent ecosystems within the enterprise and use it as a foundation to drive digital integration with customers, partners, and suppliers
In the latter case, given the orchestration model itself is meant to be dynamic and configurable, an organization’s ability to drive digital collaboration with third parties (starting with customers) should be substantially improved beyond the transaction-centric models largely used today.
Moving Beyond “Data Centricity”
There is a lot of discussion related to data-centricity right now, largely under the premise that quality data can provide a foundation for meaningful insights that create business opportunity. The challenge that I see is in relation to how many organizations are modeling and executing strategies in relation to leveraging data more effectively.
The diagram above is meant to provide a simplistic representation of the logical model that is largely in place today to provide insights leveraging enterprise data. The overall assumption is that core systems should first publish their data to a centralized enterprise environment (data lake(s) in many instances). That data is then moved, transformed, enriched, etc. for the purpose of feeding downstream data solutions or analytical models that provide insights as to how to address whatever questions were originally the source of inquiry (e.g., customer churn, predictive maintenance).
The problem with the current model is that it is all reactive processing when the goal of our future state environment should be intelligent applications.
In the intelligent enterprise, AI/ML serves two purposes:
Making the orchestration across a set of connected components more effective. This would be accomplished presumably by collecting and analyzing process performance data gathered as part of the ecosystem’s ongoing operation.
Making applications more intelligent to the point that user intervention is greatly simplified, data quality is automatically enforced and improved, execution and performance of the component itself is continuously improved in line with its role in the larger ecosystem. Given there are not a significant number of “intelligent applications” today, it would seem as if the logical progression would be to first have a “configurable AI” capability that could integrate with existing applications, analyze their data and user interactions, and then make recommendations or simulate user actions in the interest of improving application performance. That capability would eventually be integrated within individual applications as a means to make it more attuned to individual application-oriented needs, user task flows, and data sets.
The implication on enterprise data strategy stemming from this shift in application architecture could be significant, as the goal of centralizing data for a larger “post-process” analytical effort would be moving upstream (at least in part) to the source applications, eliminating the need for a larger enterprise lake environment in deference to smaller data pools that facilitate change at a component/application level.
Wrapping Up
Bringing it all together, the purpose of this article was to share a concept of a connected future state technology environment where orchestration within and across ecosystems with embedded optimization could drive a significant amount of disruption in digital business.
As a conceptual model, there are undoubtedly gaps and unknowns to be considered, not the least of which is how to manage complexity in a distributed data strategy model and refactor existing transactional execution when it moves from being initiated by individual applications to a central orchestrator. Much to contemplate and consider, but fundamentally I believe this is where technology is going, and a substantial amount of digital capability will be unlocked in the process.
Feedback is welcome. The goal was to initiate discussion, not to suggest a well defined “answer”. Hopefully the concepts will stir questions and reaction.
Organized Chaos 