Often, it begins with transforming your own business model so that you can help your customers transform the customer and employee experiences they deliver.

And all of it is underpinned by the tools that enable it directly and indirectly.

I recently attended Zoho’s analyst summit and had the opportunity to speak with Sean Rivers, Director of Technology with Relay, Inc. about this transformational process.

We cover how they started with their own business transformation as they had to pivot as they found the right product-market fit for a new technology. Then, we talk about how they help their customers execute a #businesstransformation, changing the way they communicate.

Finally, we talk about how Zoho became the enabler of both of these transformational forces, driven by its simplicity, flexibility, and adaptability.

Watch the full video interview here:

I hate my email feed at the end of the year because it’s filled with the two types of content I find the most meaningless: annual recaps and prediction pieces. Thankfully, I think we're mostly through that phase!

Still, the wrapping up of one year and the fresh start of a new one invites reflection. And reflection is something I do believe is worthwhile.

This year, in particular, I found myself contemplating how I'd be looking at my 2024 plans if I were a CIO. It is an unquestionable period of change and disruption in the world of enterprise tech — a combination that always creates both risk and opportunity. So, I found myself contemplating all of this change from the perspective of what I would be doing if I were still running an IT organization.

As an industry analyst focused on digital transformation, let me share my perspectives on the bets and investments I’d be making in 2024 if I were once again in your shoes. These aren’t predictions, but they are based on my beliefs about what is happening, the pace of change you can expect, and the implications they may have on your current state and future ability to execute.

Shifting to a Capabilities-First Posture

My first course of action in 2024 would be a mental mindset shift. As I've spoken with executives over the last year, there's been plenty of talk (and debate) over things like whether or not you should be "cloud-first" or "SaaS-first" or other similar postures. While there's value in these sorts of positions, they remain technology-focused.

As a leader, you have only a few anchor positions that you can take that will resonate with your team. You can only rally them around one or two organizing principles that will guide day-to-day decisions. Therefore, I think it's critical that you focus on those that will create the greatest lasting impact and guide the greatest number of decisions.

Because of this, my focus in 2024 would be to become a capabilities-first organization.

I believe that the one thing we can be certain of over the next several years is that we will be living through a period of incredible — and unpredictable — change. But the key word there is unpredictable. Frankly, you can ignore most of those prediction pieces you read over the last few weeks. Things are changing so fast, it's very hard to understand which technologies (or even management practices) will have an appreciable impact and which will fade away.

As a result, the most important thing you can do is to focus on building sustainable capabilities that give your organization the ability to adapt, shift, and change as the unpredictability sweeps over your organization.

This capabilities-first posture would result in me focusing on three specific capabilities:

• Business Architecture

• Building a Data-driven Culture

• Embedding the employee experience and an adoption mindset into the operating management model

These three capabilities are very involved (in fact, I feel another article on these three capabilities coming in the near future!), but let me briefly explain them.

Business Architecture

I've written about the power of a business architecture capability previously in the context of the architecture stack of the Digital Transformation Platform. When you build a business architecture capability you are creating the ability to understand your entire organization through a common, value-oriented lens and then rationalize activities and investments in the context of their overall impact to the organization's mission. This capability is the anchor that allows the organization to pivot and adapt without destroying itself — and it's virtually impossible to do effectively without a solid connection between the overarching business architecture and the technologies that support it.

Data-driven Culture

If the rise of Generative AI has taught us anything, it is the supremacy of data in creating organizational value in the digital era. It was true long before the latest hype cycle, and it includes much more than just AI use cases. An organization's ability to leverage data, holistically and dynamically across the organization, will be inextricably linked to its ability to generate differentiated value. Everything from creating unique customer experiences, to improving automation-powered efficiency, to deploying AI will all require a disciplined ability to leverage data at scale and velocity. And, because the most powerful data in any organization is that which crosses functional domains, it will be IT's ability to marshal and foster this data-driven culture that will make all the difference.

An Employee Experience and Adoption Mindset

It may not feel it with the rash of layoffs last year and continuing in the first weeks of 2024, but the ability to attract and retain the right talent will remain a chief competitive driver as organizations become evermore reliant on data and the customer experience to compete. As a result, the ability to deliver and manage an employee experience that creates a positive relationship between the organization and the employee is essential. Moreover, there is ample data that now clearly shows the relationship between a positive employee experience and a better customer experience, improved efficiency, and increased productivity. Almost universally, a better employee experience is one that is streamlined and efficient, creating the proverbial win-win for IT leaders.

At the same time, the continually increasing rate of change means that those same employees are consistently struggling with adapting to new technologies. Creating an adoption mindset, ensuring that employees are learning, leveraging, and benefiting from new technologies, will become a critical capability that ensures your ability to continually adapt to market shifts. It also has the added benefit of protecting your massive investments in new technology.

Embrace a Hybrid IT Footprint Rooted in Two Estates: Effieciency & Innovation

Mindset. Check. That's the most critical place to start and will go a long way to moving the organization forward. But it's still a bit abstract. I'd be looking for a next layer of guidance to help my team understand how we should be operating on a day-to-day basis. I call this my organizational footing.

Here, I'd be rooting my organization — culturally and strategically — around two modalities: a hybrid model and a two-estate model.

Hybrid IT for the Foreseeable Future

The first, rooting to the concept of a hybrid IT operating model, is fairly straightforward. Amid all the talk about cloud-first, SaaS-first, and so on, one fact gets skipped: the vast majority of workloads are still running on legacy architectures, at least in part. Any organization that has been around for more than a decade almost certainly has significant parts of its technical operating model running on older technologies.

And while there is plenty of cause for IT leaders to be embarking on modernization efforts, for many of these workloads, the appropriate adage is, "If it aint broke, don't fix it."

The natural (and correct) result is that the vast majority of enterprise IT organizations will be operating in a we-run-it-all hybrid model for the foreseeable future. I don't expect this will change for at least the next decade — if not longer. Enterprises will run workloads on-prem, in co-lo facilities, in private clouds, in public clouds, on SaaS, and everywhere in between. You should considered it the de facto state and establish a footing that these various environments are neither good, nor bad.

But you and your team should also not consider them to be sacrosanct.

Just because a workload has always run in a certain environment doesn't mean you shouldn't move it — nor does it mean you should. In this semi-permanent hybrid IT world, every workload must be consistently evaluated for best fit and evaluated solely based on its characteristics.

Your Two Tech Estates

While a Hybrid IT footing is really about accepting the de facto state and embracing a workload-to-environment alignment perspective, the second footing will require a bigger shift.

Most IT organizations look at their tech stack through a single lens — it's all part of one big organizational mission. While that's true, it also belies the fact that an organization's mission is almost always complex and often at odds with itself. On the one hand, every organization is constantly looking to improve efficiency and reduce costs. Technology is, and has always been, a huge part of that process. In fact, I'd argue that technology-fueled efficiency has been IT's raison d'être.

But organizations are also fighting to create market differentiation in an ever-changing world — and looking to IT to deliver innovation that can shift the balance of power.

The challenge is that how you structure and operate a tech stack for efficiency — removing variability, focus on reliability — is vastly different from how you do so when you're focused on innovation, where it's all about speed and agility.

The result is that IT must operate two tech estates: one focused on efficiency and the other focused on innovation. They obviously need to exist cohesively as part of the larger whole, but it will be essential that your organization evaluate how you design, build, deploy, and manage the various components of these two estates from their respective perspectives.

Go All-In on Tech to Drive Competitive Advantage (and yes, this includes GenAI)

Beyond mindsets and posture, the business of IT remains technology. So what technology bets would I be making in 2024?

It would be a three-part bet:

• In-house development

• Artificial Intelligence

• Capabilities Support

In-House Development

I've been somewhat dismayed that many of the enterprise IT execs I've spoken with are putting little focus on in-house development capabilities. While I'm firmly in the camp of not wasting internal effort on commodity functionality (e.g., customizing a CRM, ERP, etc.), the fact remains that technology now drives competitive advantage — and you can't create it from out-of-the-box software.

The ability to deliver bespoke technology that moves the competitive needle for your organization should be a top priority — and that will mean that you invest in an in-house development capability. But that doesn't necessarily mean that you need to have an army of developers on-staff. You can achieve much of this capability using the ever-improving breed of no-code and low-code development platforms that allow you to both democratize development and get more bang for your development buck.

Likewise, the recent advent of development co-pilots using various forms of generative AI will allow you to extend your development productivity even further, making it easier to invest in this capability without breaking the bank. But whatever approach you take, the smart enhancement of your in-house development capability should be a foundational investment.

Artificial Intelligence

It will be hard to escape AI in 2024. However, the trick will be to not be an also-ran when it comes to how you apply it.

The starting point to leveraging AI to create an advantage will be to recognize that AI is not synonymous with Generative AI. While GenAI will unquestionably see exponential growth and improvement, its use cases in the enterprise will remain nascent and speculative. Its blackbox nature, security risks, and unpredictable penchant for hallucinations will demand a slow and cautious approach for most use cases.

But it will be the potential to mix GenAI with more established forms of AI that will likely present the greatest and most potent opportunities. I would be actively and aggressively looking for opportunities to harness the fascination with GenAI and mix it with more stable and mature forms of predictive and conversational AI to create breakthrough opportunities directly aligned with the organization's go-to-market value propositions.

There will also be ample opportunities to leverage AI and various forms of co-pilots to goose productivity gains. Those use cases will be well and good, but will also be insufficient (in most cases) to move the competitive needle and will likely result in hand-waving and disillusionment. Harvest productivity gains, but remain steadfastly focused on how you can leverage a mix of AI technologies to move the ball forward for your organization.

Capability-Supporting Technologies

The last area of technology focus will be on those technologies that support specific organizational capabilities you are developing. For instance, if you do not already have technologies that support the development of a business architecture capability, that would be a place for targeted investment. Likewise for technologies that support the development of a data culture or the employee experience.

However, the capabilities that will move your organization forward are, by definition, unique to your organization. Understand those capabilities and then create a technology strategy that will enable them. The overarching focus should always remain on those capabilities that will drive competitive, differentiating value for the organization. Any investment in technologies that support those capabilities will always be a winning bet.

2024: A Tumultuous Year of Opportunity

I don't know that we've ever had a quiet, steady-as-you-go, year in the world of IT. Still, even by our chaotic standards, 2024 will likely be a tumultuous year for CIOs.

The volatility of the broader market, geopolitical instability, the rise of GenAI, and the continued disruption of traditional enterprises along multiple fronts will continue to put pressure on IT leaders to strike the ever challenging balance between leveraging technology to drive organizational efficiency and enable innovation that delivers competitive advantage.

As has been the case for some time, it will be no simple task.

Still, this year may represent one of the greatest opportunities for those IT leaders who can harness the power of technology to drive meaningful, value-driven change in the enterprise. Doing so will require some calculated risks and placing the right bets, but this may be the year that it all finally pays off.

To paraphrase the diddy of Love Actually's aging rocker, Observability is All Around. The term has unquestionably won the buzzword wars — well, at least for now. But beneath this surface skirmish, there's a more fundamental truth: enterprise IT leaders are in a heated battle with their own environments. As complexity spins out of control, they are necessarily grasping for anything that will help them accomplish their two core needs: to understand what's going on within their environments, and figuring out what to do about it.

There is a long list of technologies that have sought to help enterprise IT leaders do just that, beginning with traditional infrastructure monitoring and application performance management tools and culminating with the more recent class of AIOps and Observability platforms. But the fights over terminology get in the way of helping enterprise IT leaders actually address their challenges.

Therefore, it's time to wade through these muddy waters and get down to the core of the issue — and plot a path forward.

The Troubled Path from IPM and APM to AIOps and Observability

We began working on this analysis several months ago with a simple goal of delving into the AIOps sector of the market. But it took about fifteen minutes to realize that it was a fool's errand. Everywhere we turned, vendors were using a whole array of terms to describe what they were doing — and there was little consistency in any of it.

The truth is that the current state is the result of a long march from the earliest days of this category in which we simply talked about monitoring. Starting with infrastructure and eventually moving on to application performance management (APM), the initial goal was to instrument the environment and establish the equivalent of tripwires to alert an IT organization when something was going off the rails.

However, as complexity and the volume of these alerts increased, this approach became increasingly untenable. AIOps tools emerged with the promise of using algorithms and eventually forms of AI to handle the resulting alert storms and identify the real potential issues from the false positives. Then, observability tools emerged from the premise that it was becoming impossible to identify every potential source of an issue (and, thereby, instrument it) in advance. The answer, these tools proclaim, is to collect log, metric, and trace data so that you can simply observe what is happening in the environment and identify issues from those observations.

Finally, vendors across this spectrum have begun to recognize that all of this data is a treasure trove that can help organizations not only react in the moment (reactively and proactively) to respond to issues, but also leverage that data for more strategic planning efforts.

Along the way, these terms got all jumbled about as tech companies attempt to solve various aspects of these challenges and leverage one term or another to describe what they are doing.

But here's the thing: all these nuanced terms have only served to muddy the water. And they fail miserably at describing what all of this means in the increasingly hybrid, cloud-native world of enterprise IT.

Observability in a Cloud-native, but Hybrid World

Much of the issue around these cascading terms is that they have been borne out of a need to articulate the shift occurring within enterprise IT environments.

The rise of cloud-native architectures and the increasing complexity of the enterprise tech stack created a dynamic in which traditional approaches to monitoring and performance management were too cumbersome to manage. Ultimately, this management difficulty resulted in visibility gaps.

These new terms signified new approaches. That was great, but ironically, they've also been inadequate in addressing the true nature of the complexity challenge in two regards.

First, observability and AIOps sound like a common sense approach to solving the complexity challenge. Collect all the data and let AI sort it out. The problem is that in true cloud-native environments, that data becomes a costly management challenge in its own right — meaning that the ability to gain visibility requires more than just new tooling, it requires new approaches and mindsets.

Second, traditional enterprises have not moved to cloud-native architectures in some wholesale, forklift manner. Their reality now — and for the foreseeable future — is a hybrid environment in which they have traditional legacy environments, traditional cloud-based environments, and an increasing presence of cloud-native architectures. And all of these are intermixed with mission critical business processes often functioning across all of them.

The net result is that there is no single answer — and definitely no single tool — to address the visibility and management challenges facing enterprise IT organizations. Instead, what's needed is a broader, curated approach.

A Broader View: Why You Need to Focus on Observability & Intelligence

The biggest challenge with the muddy waters of observability, AIOps and the rest of them is that the distinctions no longer serve or provide any benefit to an enterprise IT leader. Moreover, they've become almost meaningless.

Vendors have begun to use the terms (all of them) interchangeably and almost any vendor providing meaningful value is necessarily crossing the spectrum in terms of the solutions they're delivering to the market.

As we began to look at the market holistically, we quickly realized that all of these terms and approaches really came down to just two critical actions for enterprise IT leaders:

• Understand what is happening within the enterprise tech stack, in context and in relationship to the other parts of the environment

• Distill and transform that knowledge into actionable insights that enable them to protect service provision and create a strategic capability from that data

We are choosing to call this first action observability. We believe it most accurately describes the business objective: to observe and understand what is happening in the environment. But in our view, observability includes the entire process of data capture. Not only the collection of log, metric, and trace data, but traditional monitoring feeds and any other data which will help the organization get to this level of understanding.

We call the second action intelligence. Here again, our use of this term is intended to be broad, encompassing tactical intelligence for remedial action as well as strategic intelligence that informs planning, cloud migration, and transformational activities. Both are necessary and both should be derived from the underlying data collected as part of the observability process.

Then, within those two broad actions, you need to create the coverage applicable to your environment. This will almost certainly involve one or more of what we call the "Majors" that will often provide broad coverage and act as a focal point. You will then augment that will additional layers to deal with specific elements of your environment, such as a targeted observability tool for your cloud-native environments.

The critical factor, however, is that you must look at all of it as a unified and integrated whole. Allow the market to force you into looking at it in narrow, specific buckets and you'll doom yourself to silo hell and service failures.

Introducing our IT Observability & Intelligence Platform Sector Coverage

To help you take this broader view, we will focus our coverage on what we call the IT Observability & Intelligence Platform Sector. We will not make any attempt to distinguish between monitoring, observability, APM, AIOps or any other label. We will only look at this sector through the prism of a vendor's ability to help you create either understanding or intelligence around some element of your environment.

This is a very crowded sector with many niche players catering to specific elements of the tech stack, so expect our coverage to continue to evolve over time. We will be seeking out what we call Market Movers and offering their profiles over time. For now, here's how we're presently seeing the market.

Notes: If a vendor isn't listed below, it either means that they are not on our radar and we are not aware of them, or we have reviewed them and do not believe they are viable as an enterprise technology option. All vendors are listed alphabetically. Numerous Market Mover Profiles are currently under development and will be linked here upon completion.

The Majors

These are the big players. You know who they are and can decide for yourself if they're a fit. We don't generally expect significant innovation here, but will cover it when we see it.

• AppDynamics (Cisco)

• DataDog

• Dynatrace

• New Relic

• Solarwinds

• Splunk (Cisco)

Market Movers

Market Movers are enterprise tech providers that we believe are delivering noteworthy, disruptive, and innovative solutions, potentially within a specific niche. They are worthy of consideration.

• Amelia*

• BigPanda*

• Chronosphere*

• DRYiCE iControl (HCL)*

• EasyVista EVObserve*

• Galileo*

• PerfectScale

*Market Mover Profile pending

The Watch List

These are companies that are on-radar with us. We are either still evaluating them or waiting for a briefing, but we believe they are worth a look.

• Edge Delta

• Honeycomb.io

• LogicMonitor

• Logz.io

• ManageEngine

• Moogsoft (Dell)

• Observe

• ScienceLogic

• Sumo Logic

If you believe someone should be on our watch list, please email us at info@thedxinstitute.com

In addition to being Waterloo Data’s Chief Strategy Officer, I am an industry analyst, futurist, author of three books, and a professional speaker. But what I'm about is helping enterprise leaders transform themselves and their organizations for the future. 

I believe that we are on the cusp of a fundamental shift in how the world works. I’m not talking about Armageddon, and I’m not worried about computers taking over the world in some SciFi sort of way, but over the next few decades everything you and I know about how society and organizations function is going to change — dramatically. 

Technology is fueling this disruption, transformation, or whatever you want to call it, and it is enabling some fundamental shifts in how the world works — and what we, as leaders, need to do to prepare for what is already happening.

As most of us realize, a driving factor in our fundamentally shifting world is data. It is the fuel that powers emerging technologies and drives the creation of differentiating business value.  Data is a precious resource, but frustratingly, one that most enterprises are not fully capitalizing.

Generative AI and other recent developments are finally causing enterprise leaders – up to the board of directors – to pay attention. It’s creating a gold rush mentality and enterprises are finally recognizing that to compete and thrive in this coming era, they must become data-driven enterprises.

They’re quickly learning that it isn’t easy.

To help, I created this eBook highlighting the 8 Crucial Steps to Becoming a Data-Driven Enterprise.  

Part 1: The Data Foundation Elements

Chapter 1: The Ability to Link Data to Business Value

How do you become a data-driven enterprise? There are many elements, but the first is unquestionably the ability to link data to business value.

Like the rest of the chapters in this eBook ,this chapter on linking data to business value will be short, sweet, and inspired by my recent article in The DX Report, Why It’s Finally Time to Become a Data-Driven Enterprise — and Why It’s So Hard. The goal is to give you a simple and digestible roadmap to begin your journey to becoming a data-driven enterprise. Let’s get to it.

This is an excerpt from an eBook developed for Waterloo Data. You can get the full ebook courtesy of Waterloo Data here.

Transformation and modernization are top-of-mind issues for every enterprise IT executive. But whether it's replatforming legacy applications or migrating them to the cloud, enterprise IT leaders routinely suffer from run-away costs, unforeseen complications, and out-of-control environments on the other side of the modernization process.

Yet, as an enterprise IT leader you have little choice but to forge ahead. The outcomes your business counterparts demand, coupled with the ever increasing risks incumbent with legacy environments, mean that you need to find a way to transform and modernize your tech stack.

The irony is that while much of the focus during modernization and transformation efforts is on the application, the real source of challenges is often not the application at all.

What if the real issue is that you're not paying enough attention to the unsung hero — and liability, if you don't address it: your infrastructure.

I'll even go so far as to say that you will never truly succeed at modernizing and transforming your IT application stack — nor avoid the crazy, unexpected costs, the lack-of-control challenges, or the unforeseen performance issues — until you have first modernized and transformed your infrastructure.

The question that holds enterprise leaders back is understanding what infrastructure modernization really means, and how to do it amid all the other pressures and constraints you face.

The good news is that it is possible to free your applications from their legacy shackles and rapidly (and cost effectively) transform your infrastructure by changing the way you look at infrastructure management in the first place.

You need to move past looking at it as a moribund practice akin to maintaining your plumbing or flossing your teeth. Rather than avoiding and minimizing it, you need to see it as the gateway to the visibility, transparency, and governance you need.

The essential missing element is the smart utilization of infrastructure data. It's why I call this approach smart infrastructure management — and it will change how you approach your modernization and transformation efforts.

Why You've Skipped Over — and Then Struggled With — Infrastructure Modernization

"Hey, Charlie. Can I talk with you?"

Her name was Donna, and as she spoke, these words sent a shiver down my spine. At the time, I was running IT Operations for a $1 Billion healthcare firm, she was the head of business operations — and she was never happy.

We had built a well-oiled machine that just ran. All the time. Without fail (well, almost!). But she would always find the smallest thing to nitpick. Without fail. What frustrated me most was that this was a wildly different situation than when I had arrived two years earlier. At that time, our infrastructure was failing several times a day and our singular goal was to finish each day without a fire burning out of control.

But as we modernized our infrastructure and stabilized our operations, it did what it's supposed to do: it faded into the background.

The full research is available courtesy of Galileo Suite. Click here to download it.

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"And those are all cows?" I said incredulously.

I uttered these words at the moment I truly understood the impact that digital transformation was going to have on everything.

I was on a speaking tour through New Zealand about a decade ago when I joined a friend and her husband for a small dinner party at their house. One of the other guests was a dairy farmer — and I was sure we'd have nothing to discuss. Until he told me about the level of automation he used to manage his cows.

My mind was blown. I had to see it for myself.

The next day, we paid him a visit and he showed us his control room, call it a Bovine Operations Center. There, he was able to track everything that happened on his dairy farm to a shocking level of detail. It was amazing.

And it was also a harbinger of what was to come.

While the concept of digital transformation has been bandied about for a long time now, most organizations have failed to fully grasp what it means. More importantly, they've failed to understand that it really represents an organizational capability that organizations must build in order to sustainably bridge the gap between IT's two competing mandates of efficiency and innovation.

As I introduced in my recent analysis, the key is for enterprise IT leaders to build what I call a Digital Transformation Platform (DXP). This collection of technologies is the engine that allows an organization to stitch together their efficiency estate — the Line of Business and functional apps that enable business process optimization — and connect it to a set of data- and experience-driven apps and workflows that I call their innovation estate.

However, this stitching together process is a delicate dance. The complexity of the enterprise tech stack can be overwhelming, laden with an abundance of opportunities for unintended consequences. Stitching together systems, processes, and data to create dynamism and adaptability requires an intricate understanding of how all the moving parts fit together in service of the organization's goals.

In short, it requires a control room — much like the one I found all those years ago at a New Zealand dairy farm. In the DXP, that control room is the architecture stack.

The Slow, Smooth, Fast of Your DXP

There's an innovation-related catchphrase that I just hate: Move fast and break things.

I hate it for two reasons. First, it implies that speed must always come at the expense of order and control. Not only is that completely untrue, it's also just plain lazy. It simply indicates that you don't care enough to not break things.

But the second reason I hate the phrase is that for most enterprise organizations, breaking things isn't an option. There are mission critical business processes and functions that simply must run — reliably and consistently. Period.

As a result, this ethos tells most of the enterprise world that innovation is a hopeless endeavor.

The thing is, nothing is further from the truth.

There's another phrase that I think makes a ton more sense. I learned it from my father when it was the catchphrase for the SWAT team he ran during the 1984 olympics: Slow is smooth. And smooth is fast.

Whenever I think of that phrase, what comes to mind is a naval ship or an elite military unit. They aren't running around flailing like a wild bunch of cowboys breaking things. In fact, it's just the opposite. They are methodical. They are orderly. They are controlled. Everything has a place. Everyone has a role. Everyone understands the mission.

And because of that discipline, they function as a smooth operating unit. It looks slow compared to the uncontrolled, flailing breakage, but it is actually very fast.

When we think about innovation within the enterprise, this is the image that should come to mind. The goal isn't to ignore the reality of your efficiency-focused line-of-business and functional stack. They exist for a reason and they're vitally important. But in order to create the speed and agility of an elite team, what you need is smooth. And the way you get to smooth — which is what gets you to fast and adaptable — is by building an architecture stack that connects your two mandates together.

The Control Room of Your DXP

The architecture stack's job is two-fold:

• Clearly articulate the organization's strategic imperatives via a business architecture that describes organizational assets, the current (and potentially future) state of the tech estate, and their interdependent relationships.

• Provide a means of ingesting, triaging, assessing, and prioritizing incoming demand in the context of that architecture.

For any team that must be able to rapidly respond to unknowable future circumstances, a critical preparatory step is to ensure that everything begins with a known state. That means that everything has a place, everyone has a role, and key foundational actions are well-practiced.

It is from that state that a team can then respond to an unforeseen situation — because they do not need to waste valuable time and energy getting everyone and everything on the same page before they can act. They can simply respond, to either a threat or an opportunity, in real-time and with confidence that everyone knows the starting state.

The role of the architecture stack of the DXP is to create this state of readiness.

Comprised of business architecture, value stream management, and portfolio management tools, this part of the DXP exists to help an organization catalog its assets in the context of its mission and business goals. This business-focused, goal-alignment is all critical as the greatest challenge facing most organizations is that their technology-driven projects end up taking on a life of their own.

In the midst of all the technical complexities, the why of the effort is lost. A robust architecture stack continually links technology initiatives to the business outcomes they are meant to support.

Moreover, having this control room visibility ensures that enterprise leaders can undertake those initiatives with full visibility to both their outcome-impact and also their potential impact on other business outcomes and technology initiatives.

Finally, when used effectively, these systems enable the continual balancing between the needs of the efficiency and innovation estates. As enterprise leaders receive inbound demand, the architecture stack enables them to assess it in the context of the organization's overall mission and desired outcomes, as well as against the competing needs of efficiency and innovation.

It is only by having the full visibility and readiness that the architecture stack provides that enterprise leaders can make these decisions quickly and with confidence.

The (Often) Missing Foundation of Digital Transformation

When people talk of digital transformation efforts failing, it is most often because they failed to understand two things. First, that it shouldn't be viewed as a project or distinct effort. As we've discussed, it's a capability that you must build and nurture over time.

But secondly, they fail to recognize that the greatest cause for transformational failure is the lack of readiness before they began.

Building an architecture stack that creates visibility and aligns your organization around a common mission and set of desired business outcomes — and then provides a means of allowing that architecture to guide your ongoing efforts — is the critical foundation of any digital transformation effort.

The state of readiness it creates must exist prior to the start of any transformational effort.

It is this error of omission that dooms most digital transformation efforts to failure before they even begin.

But the converse is also true — and it's why the architecture stack is the critical starting point of a DXP: If you have built an effective business-focused architecture stack and capability, you will have laid the foundation for success and set yourself and your team up to respond to whatever may come.

So, what do you think? Agree? Think I’m completely off? Let me know!

Leave a comment

And don’t keep this conversation to yourself. Invite your friends and associates to weigh in!

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Despite every tech company rushing to anoint themselves the great enabler of digital transformation, we have collectively put little thought into how to build a technical capability that can support digital transformation efforts over time — what I call the Digital Transformation Platform.

Authentic digital transformation is not a project or even an ongoing effort, it’s an enterprise capability that requires such a platform to support it.

However, developing a digital transformation platform (DXP) is challenging because it’s not something you can buy — you must assemble it in the context of your organization’s needs. As a result, it may also be the single most important action an enterprise IT leader can take to unleash innovation and transform their organization into a digital enterprise.

The concepts I’ll share with you today — like much innovation — came slowly over time, then all at once. But when it hit me, I knew that it was going to be central to my work — and yours.

Today’s DX Report is going to be a bit long so I can go into what it means, why it’s so important, and how you can act upon it.

I’ll also be introducing a new format for The DX Report based on some of what I’ve discovered as I’ve been plumbing the depths of what it means to build and leverage a DXP — and the bets required to make it happen.

Understanding the premise behind a DXP is critical because I believe it is the primary driver of technology-based innovation in the enterprise, serving as the missing link that connects the two sides of the enterprise IT brain — efficiency and innovation.

The Digital Transformation Gap Between Efficiency and Innovation

There is an oft-reported (and very misleading) stat pundits love to float that 70% of all digital transformations fail.

First off, I think it’s a hyped-up, bogus stat. But I’ll get to that in a future article.

However, the truth in that bad stat is that digital transformation efforts are genuinely difficult to execute and often get mired in a whole host of issues that drag them down, bring them to a crawl, and cause their scope (and budget) to endlessly expand.

But while the stat is easy to throw around, no one is really addressing the root of the issue. Digital transformation is difficult because it sits between IT’s two competing demands: efficiency and innovation.

Since the very beginning, the primary role of technology within the enterprise was to reduce costs and drive efficiency.

Almost your entire stack is made up of functional and line-of-business apps that exist to streamline business processes and create cost-saving efficiencies. To improve those results, IT organizations have spent vast amounts of resources to standardize and harden those business processes. And the resulting productivity and efficiency gains have transformed enterprise IT into a $300B+ market.

But all that hardening and standardization has come at a cost to the other, more recent IT mandate of innovation.

Over the past couple of decades, IT’s mandate has expanded and shifted from a sole-focus on back-office efficiency to enabling customer-facing and experientially-driven competitive differentiation.

During this time, the digital experience became the primary driver of competitive value in the enterprise, and understandably, organizations turned to IT to deliver it.

The challenge is that driving technology-fueled innovation requires the complete opposite of what you need to drive never-ending efficiency from functional and line-of-business apps. Innovation requires speed and adaptability so that you can experiment, respond to market shifts, and adjust to rapidly changing experiential demands.

All that structure and rigidity became an impediment to the mandates of innovation-focused digital transformation.

As a result, one of a few things happened. Either organizations undertook a traditional, efficiency-focused technology project, and called it digital transformation. Or they attempted to truly transform things, but ran smack into the brick wall of a tech stack purpose-built for efficiency.

The fundamental problem wasn’t a “failure of digital transformation,” but a failure to recognize that it wasn’t a “thing” to begin with. What enterprise leaders needed to do, instead, was to build an organizational capability that would enable them to bridge the gap between their competing mandates.

That technology capability is the essence of what I call a Digital Transformation Platform.

It is a collection of technologies that an enterprise must assemble to enable them to innovate by:

• Sensing and responding to market shifts and changing strategic priorities

• Rapidly deploying and orchestrating innovation and experientially-focused automation

• Seamlessly integrating data across the enterprise

• Holistically monitoring the tech stack from an experience-in perspective

• Creating new experiential interfaces to meet customer, employee, and partner expectations

• Driving continual customer, partner, and employee adoption of these new technologies, automations, and interfaces to ensure a return on their innovation investments

And it enables all of this innovation without disrupting the core efficiency that functional and line-of-business apps deliver.

While digital transformation will always be a business and organizational change action, executing it without building a supporting technology capability (a DXP) will doom it to failure.

However, the converse is also true. When you have built this capability, you will have created an innovation power source that will drive your organization forward long into the future.

Unlocking Your Innovation Power Source

Before I go much further, I need to let you in on a little secret. You already own and have deployed many of the components of a Digital Transformation Platform.

And, if you’re finding more success than your peers in executing transformational efforts and driving innovation, it’s most likely because you’ve organically done much of what I’m about to explain.

The ability of a Digital Transformation Platform to drive innovation is less about the specific tools you deploy, but in how you assemble and leverage them to your advantage. The real key here is intent.

The challenge for most organizations is that they purchase a piece of technology that may be a part of a Digital Transformation Platform — an observability tool, for instance — but then deploy that tool with a sole focus on improving efficiency.

It’s not the tool that’s the issue, it’s how you’re weilding it.

When you use that observability tool (to keep with our example) to create experiential visibility, to enable rapid environment change, and to increase the velocity of transactions and workloads through your stack, it suddenly becomes a source of innovation and transformation.

Likewise, you cannot look at any part of the DXP stack in isolation. To function as a DXP, all the components need to work together and be viewed as a whole.

The Buy-and-Build Model of Your DXP

I know that was a long setup, but this is a mindset shift more than anything else, so it’s important to understand the foundations of what it means. And there’s one last piece that you need to get your head around — the how of building and deploying a DXP.

I’ll go through all of the components of a DXP in a moment, but there are two important things to understand about them.

First, you cannot buy one. If some vendor attempts to sell you an out-of-the-box digital transformation platform, run for the hills. The entire point of this process is to create competitive advantage for your organization — that’s not something you can buy off-the-shelf.

That said, this also isn’t about building everything from scratch. The second point is that you’ll be following a “buy-and-build” model.

You’re going to assemble most of the building blocks using commercial enterprise IT software, then use various parts of those tools to build a unique set of automations and workflows on top of them — mostly using low-code and no-code interfaces.

With the last preamble out of the way, let’s dive into the components of your DXP.

The Components of the DXP

Think of the DXP as an innovation engine. You take inputs on one side and spit out sustainable innovation on the other. To acheive this, each input must pass through three DXP stacks:

• The planning and architecture stack

• The innovation stack

• The adoption stack

As their names imply, each stack performs a critical function in converting demand into innovation. It’s important to note that these stacks do not necessarily represent discrete collections of tools, meaning that some tools may exist within multiple stacks or within various levels of a stack, based on specific functions.

The Planning & Architecture Stack

The starting point for all inputs into the DXP is the planning and architecture stack, which is typically comprised of one or more of the following components:

• Business architecture

• Value Stream Management

• Portfolio Management

Often leveraged in a purely tactical fashion (or skipped altogether), this class of tools is critical to the functioning of a DXP because it is the place that triage, assessment, and alignment of incoming demand to the in-production tech stack must happen.

In order to strike the balance between efficiency and innovation, you must take an architectural view of your entire environment and continually align and tune it to meet changing needs and demands.

The key here is to leverage these tools to create visibility around both sides of the equation. You then utilize that visibility to take targeted action that drives innovation by mixing your efficiency-oriented tech estate with new elements that create differentiation.

Fundamentally, this process requires a business focus and the ability to contemplate strategic needs and drivers in the context of your current environments. This business focus is essential so that you can effectively connect those strategic goals with the business outcomes you seek to deliver.

The output of this process should be a set of initiatives that you execute within your innovation stack.

The Innovation Stack

The innovation stack is the multi-layered heart of the DXP. It includes various components that stitch together your functional and line-of-business apps, enable you to create a layer of automation and orchestration on top of them, and often provides an experiential gateway to interact with them in a differentatiated or consolidated manner.

This stack typically consists of:

• Data Integration

  • Data Integration Platforms

  • Customer Data Platforms

  • API Design & Management

• Monitoring & Management

  • AIOps

  • Observability

  • Application Performance Management

  • Digital Experience Monitoring & Management

• Business Process Discovery & Management

  • Process Mining

  • Business Process Management

• Orchestration, Workflow & Automation

  • Workflow Automation

  • Business Process Automation

  • Low-code / No-code Application Development Platforms

• Experience & Engagement

  • Conversational AI

  • Low-code / No-code Application Development Platforms

The innovation stack helps you meet experiential and transformational demand by pulling in data and integrating business processes from your core technology estate, and then adding layers of orchestration, automation, and experience to create new applications or interfaces that create competitive advantage.

Data Integration and Monitoring and Management tools sit at the foundation of this stack because they, respectively, allow you to create connectivity and monitor the flow of data and workloads between systems.

Likewise, low-code or no-code interfaces and platforms are an essential element of DXPs because they provide the ability to rapidly develop and change applications, workflows, and experiential interfaces. Moreover, they allow you to democratize these activities to improve both velocity and relevance.

And, like the planning and architecture stack, merely having these various components is not enough. They must be leveraged in a cohesive fashion to create innovation without disrupting your efficiency-focused tech estate.

The Adoption Stack

The nature of a well-executed DXP is that it enables an organization to rapidly introduce a high volume of change into the organization.

Changing market conditions, shifting customer expectations, and rapidly evolving competition all drive the need for this level of change — but it is incredibly hard on people.

Continually introducing new technologies, changing applications, and reinvisioning interfaces can have a significantly negative impact on both productivity and customer and employee experiences. As a result, it’s imperative that your DXP include a set of tools that help you ensure the adoption of these investments as you deploy and change them.

The adoption stack typically includes components such as:

• Digital Adoption Platforms

• End User Experience Management

• Product Experience Platforms

The adoption stack gives you the tools to continually tweak and adapt the innovations you’re deploying based on the real interactions your customers, employees, and partners are having with them.

Again, the key is to look past the mere tactical gains (e.g., reducing calls to a support function or improving a service metric). Instead, you should use the data and insights this stack provides to optimize your efforts and create a feedback loop to the planning and architecture stack, enabling you to continually refine your transformational efforts.

Operationalizing Your DXP

As I stated at the beginning of this analysis, you probably already have many of these components someplace in your environment.

Merely having these various technologies is not enough. But then how do you transform them into a DXP?

Doing so is a three-step process that I’ll cover in depth in a future DX Report, but here’s the summary:

• Create an innovation pipeline. You need to stop looking at digital transformation as a thing and see it as an innovation capability. As such, you’ll need to build an innovation pipeline in which you see your DXP as a system that transforms demand into innovation. This approach requires that you build a discipline, structure, and culture around this process. It will be multi-dimensional and rely on disciplines such as business architecture and experience architecture.

• Fill in the Holes. While you may have most of the pieces of a DXP already, there’s a good chance your missing some essential components. While I’m not saying you necessarily need one of everything — focus on your business needs — you need to adequately address the capabilities you need in each stack.

• Instrument & Measure Innovation. To ensure the long-term sustainability of your DXP and ensure that it functions as an innovation pipeline, you need to anchor it to specific innovation-driven metrics. The greatest challenge you’ll face is that the default position for almost every tool in a DXP is to focus on efficiency gains. Don’t get me wrong, you’ll get efficiency as a byproduct of implementing an effective DXP, but the goal must be differentiation-generating, experiential innovation. Instrument and measure it as such.

The Bets You Need to Make & The DX Report’s New “Bet-Based” Format

Ok, that’s the basics of building a Digital Transformation Platform. Frankly, we’re just scratching the surface here, so I’ll be periodically digging into various components and elements in future issues of The DX Report.

But as I’ve been mulling and researching these concepts, something occurred to me. The difference between the status quo and driving authentic digital transformation — the difference between having a grab bag of tools and building a DXP — is all about the bets that an enterprise IT leader must make.

Each decision, every investment, is a bet. Hopefully it’s a bet grounded in solid research and analysis, but essentially you only have so many chips to play and you can only bet on so many technologies, practices, and ideas.

The bets you make — and if they turn out to be good bets or bad bets — is what determines your success, organizationally and personally.

Having been in your seat, and in talking with so many enterprise IT executives over the last decade, I know that placing those bets can feel overwhelming. The stakes are huge and everything is in a constant state of flux.

My hope is that the concept of a DXP can serve as a frame of reference as you place those bets. I believe that the most impactful bets you’ll be placing are those that involve the DXP — because they are the ones that will create innovation opportunities.

But I also want to help by changing how I approach my analysis. Going forward, most issues of The DX Report are going to directly address these bets. I’ll define the bet, then offer up “the briefing” — my analysis of the situation with the pros and cons and any significant considerations you should take into account.

Finally, I’ll give you my recommendation, playing the role of your investment advisor. But instead of buy, sell, or hold, I’ll be recommending that you invest, pass, or wait. And then I’ll tell you why.

My hope is that this combination — the DXP reference architecture and this new format for The DX Report — will give you a fresh set of tools to make the best decisions for yourself and your organization.

Ok, that’s it. I want to hear what you think. Tell me where I nailed it — or where I completely missed the mark. I expect I’ll be refining this concept over time, and it will get better the more you chime in and let me know your thoughts.

Finally, this is an important conversation, so share this concept with other enterprise IT leaders you know and invite them to join in!

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In case you missed it, we’re living through a momentous shift in how organizations create value.

It’s at least as big as the shift from an agrarian economy to the industrial age. We’ve entered the era of the experience economy — a shift that began almost twenty-five years ago, but took some fundamental technological development to finally and fully realize.

Today, the experience is everywhere. The customer experience, employee experience, partner experience, and developer experience are now crucial for business competitiveness. And it has reset the nature of value exchange as we make most of our decisions based significantly on the experience we will enjoy.

Foundational to almost every facet of these experiences are the various technologies that support and enable them. It’s all coming down to the digital experience.

But there’s a problem.

Delivering and sustaining an immersive and intuitive digital experience that wins and keeps customers, employees, and partners requires an intricate and complex web of technologies that are becoming nearly impossible to manage, let alone do so effectively and consistently.

But it will be just this ability to deliver seamless digital experiences over the long haul that will ultimately drive success. This fact is bringing two technical capabilities to the forefront: experiential monitoring (sometimes called user experience monitoring) and leveraging artificial intelligence to enable it.

And it’s important to understand the essential role they’ll play, and how to apply them as you compete in the experience economy.

The Criticality of the Digital Experience — and Its Delivery Gap

Whenever we're evaluating a new technology domain or the evolution of a sector, we’re always wary of hyperbole

But in this case, the importance of the digital experience as a lever of competitive advantage is getting hard to dismiss.

For instance, according to a recent study by Sitel Group:

• 38% of consumers say that receiving a consistently positive customer experience provides sufficient motivation for them to pay a premium for a product or service.

• 58% of consumers say the customer experience is one of the biggest influences when choosing one brand over another.

• 73% of consumers said they were prepared to sever ties with an organization following a single poor customer experience.

And these are just a few data points. An overwhelming abundance of data proves the momentousness of this shift in value perception.

This is an excerpt from a research report developed for Dynatrace. You can get the full report courtesy of Dynatrace here.

I remember how excited I was to build my first Network Operations Center (NOC).

It was a new idea at the time (yes, I know I’m dating myself), and boy, did we feel like we were cutting edge. The mere idea that we needed a place and a set of tools to monitor our entire infrastructure (because it’s never really been about just the network) was a big transition at the time.

How things have changed.

Compared to the situation today, what I did was the equivalent of putting my 5-year-old brother “on watch” for when our parents got home (don’t ask what we were doing!) — all he had to do was sit there and yell if they showed up.

Today’s situation for enterprise IT operations leaders is much more complex — regarding the tech stack to be monitored and managed and the stakes involved.

The technology infrastructure now powers virtually every aspect of how an organization operates. But the “how” of it is a jumbled mess. The days of monolithic (and therefore easier to manage) systems are long gone, and tracing workloads as they snake through a spaghetti bowl of connections, transactions, and relationships is not for the faint of heart.

But because everything relies on the ability to manage each of these connections, relationships, transactions, and workloads discretely, it’s up to you to figure out a way to manage it all. Walk the floor of any IT ops conference (virtual or otherwise), and you’ll know that sorting out which tools can best help you with your particular situation is almost as hard as managing your infrastructure in the first place.

That’s why this buyer’s guide exists. As independent technology analysts, we are continually assessing the market. As we’ve looked at the infrastructure monitoring space, we’ve identified seven critical considerations that will help you make sense of your infrastructure monitoring choices and select the best tool or tools.

The 7 Critical Selection Considerations for Infrastructure Monitoring Platforms

Let’s get one thing out of the way up front: no single tool will magically solve all your problems or be suitable for every organization. Every situation is different, and every team’s needs are unique (at least somewhat).

Therefore, the key is to know what to look for to figure out what’s best for you.

The full research is available courtesy of Galileo Suite. Click here to download it.