Digital transformation comes down to 4 key areas
Digital Transformation Comes Down to Talent in 4 Key Areas

Over the years we’ve participated in, advised on, or studied hundreds of digital transformations. In doing so, we’ve gained a perspective on just how difficult true digital transformation really is and what it takes to succeed. Digital transformation is not for the faint of heart — the unfortunate reality is that, to date, many such efforts, like transformation programs in general, have failed.

Success requires bringing together and coordinating a far greater range of effort than most leaders appreciate. A poor showing in any one of four inter-related domains — technology, data, process, or organizational change capability — can scuttle an otherwise well-conceived transformation. The really important stuff, from creating and communicating a compelling vision, to crafting a plan and adjusting it on the fly, to slogging through the details, is all about people.

More than anything else, digital transformation requires talent. Indeed, assembling the right team of technology, data, and process people who can work together — with a strong leader who can bring about change — may be the single most important step that a company contemplating digital transformation can take. Of course, even the best talent does not guarantee success. But a lack of it almost guarantees failure.

Let’s explore the talent needed in each of the four domains in turn.


From the Internet of Things, to blockchain, to data lakes, to artificial intelligence, the raw potential of emerging technologies is staggering. And while many of these are becoming easier to use, understanding how any particular technology contributes to transformational opportunity, adapting that technology to the specific needs of the business, and integrating it with existing systems is extremely complex. Complicating matters, most companies have enormous technical debt — embedded legacy technologies that are difficult to change. You can only resolve these issues with people who have technological depth and breadth, and the ability to work hand-in-hand with the business.

Challenging as these difficulties are, an even more critical issue is that many business people have lost faith in their IT department’s ability to drive major change, as many IT functions are primarily focused on “keeping the lights on.” Eventually, however, digital transformation must incorporate institutional IT, so rebuilding trust is essential. This means that technologists must provide, and demonstrate, business value with every technology innovation. Thus, leaders of the technology domain must be great communicators, and they must have the strategic sense to make technological choices that balance innovation and dealing with technical debt.


The unfortunate reality is that at many companies today most data is not up to basic standards, and the rigors of transformation require much better data quality and analytics. Transformation almost certainly involves understanding new types of unstructured data (e.g., a driver-supplied picture of damage to a car), massive quantities of data external to your company, leveraging proprietary data, and integrating everything together, all while shedding enormous quantities of data that have never been (and never will be) used. Data presents an interesting paradox: Most companies know data is important and they know quality is bad, yet they waste enormous resources by failing to put the proper roles and responsibilities in place. They often blame their IT functions for all these failures.

As with technology, you need talent with both great breadth and depth in data. Even more important is the ability to convince large numbers of people at the front lines of organizations to take on new roles as data customers and data creators. This means thinking through and communicating the data they need now and the data they’ll need after transformation. It also means helping front-line workers to improve their own work processes and tasks such that they create data correctly.


Transformation requires an end-to-end mindset, a rethinking of ways to meet customer needs, seamless connection of work activities, and the ability to manage across silos going forward. A process orientation is a natural fit with these needs. But many have found process management — horizontally, across silos, and focused on customers — difficult to reconcile with traditional hierarchical thinking. As a result, this powerful concept has languished. Without it, transformation is reduced to a series of incremental improvements — important and helpful, but not truly transformative.

In building talent in this domain look for the ability to “herd cats” — aligning silos in the direction of the customer to improve existing processes and design new ones, and a strategic sense to know when incremental process improvement is sufficient and when radical process reengineering is necessary.

Organizational Change Capability

In this domain we include leadership, teamwork, courage, emotional intelligence, and other elements of change management. Fortunately, much has been written about this domain for many years, so we won’t review it here, other than to note that anyone responsible for digital transformation must be well-versed in the area. While, we have no firm evidence to support this, it seems that those who gravitate toward technology, data, and process are somewhat less likely to embrace the human side of change. Of course, in our recommendations above, we have urged leaders to seek those with excellent people skills. If you are unable to find them, a good alternative is to put some “purple people,” those able to work on both sides, on the transformation team.

Pulling It All Together

So far, we’ve discussed the technology, data, process, and organizational change capability domains as if they existed in isolation, which of course they don’t. Rather, they are part of a larger whole. Technology is the engine of digital transformation, data is the fuel, process is the guidance system, and organizational change capability is the landing gear. You need them all, and they must function well together.

Consider the “our systems don’t talk” problem, which bedevils most companies and is anathema to digital transformation. But in which domain does it belong? As described above, it is a tech problem — but it also leads to enormous process inefficiencies. Yet it stems from a lack of solid data architecture, and it may involve organizational structure and politics issues that are difficult to change. So one could argue that any domain should take the lead. But the best solution involves the four working together.

Absent a deep understanding of each domain, it is difficult for nearly all business leaders to see the full potential in digital transformation — a contributing factor to many failed digital transformations. But of course, no one individual possesses all the required knowledge and capability. Hence our call to assemble talent in each area.

Finally, work on technology, data, and process must proceed in an appropriate sequence. It is generally accepted that there is no sense automating a process that doesn’t work, so in many cases, process improvement or reengineering must come first. On the other hand, some transformations will feature large doses of artificial intelligence. Since bad data stymies development and deployment of good AI models, in these cases, work on data should come first. Start with your end goals, then develop the sequence of steps best suited to achieving them

Digital transformation can and should be focused on problems of greatest need to the company. Those priorities will also lend a flavor to the talent needed; if the focus is on transforming customer relationships, for example, the data talent on the team may have particular expertise in customer data, the process talent on sales and marketing processes, and so forth. More important, however, is that the talent possesses the four types of expertise we have described and has had previous success at creating and executing on any kind of technology-driven transformation.

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