Digital Transformation Q&A, Featuring Todd J. Fisher

What digital transformation questions keep you up at night? Earlier this month, we hosted a Q&A on Twitter featuring Intraprise's Founder, Todd J. Fisher, to answer your top digital strategy questions. Check out the recap of our top 8 digital transformation questions to unlock valuable insights to apply to your digital strategy:

• Question #1: I hear about digital transformation all the time, but it seems that there are many different ways the phrase is used. What does digital transformation really mean?

• Question #2: What industry(ies) or types of businesses will be the most negatively impacted by the growing demand to digitally transform to survive in the fourth Industrial Revolution?

• Question #3: Where should organizations be investing?

• Question #4: What should organizations be putting in place to survive digital disruption?

• Question #5: What mistakes are organizations making when it comes to data?

• Question #6: What are some common challenges organizations face related to data?

• Question #7: Other than technologists, what does the future of talent look like in a data-driven organization?

• Question #8: Do you have any advice for organizations that need to undertake a transition to the Cloud?

Question #1: I hear about digital transformation all the time, but it seems that there are many different ways the phrase is used. What does digital transformation really mean?
In virtually every way, I share the explanation of digital transformation Tom Siebel offers in his outstanding 2019 book titled, Digital Transformation: Survive and Thrive in an Era of Mass Extinction. I do not agree, however, with Siebel's description of Digital Transformation: "Digital transformation goes by many different names. Perhaps the most familiar is the fourth Industrial Revolution." (pg 18)

After Siebel stated that Digital Transformation is the fourth Industrial Revolution by a different name, he goes on to offer a brief, but very effective description of an evolution theory-based progression through the first three Industrial Revolutions to our current era. By leaning on evolution theory, Siebel illustrated what the evolution looks like on a time series graph. Evolution theory's concept of "punctuated equilibrium" paints a vivid picture of progress over time. Very clever, indeed. The periods of punctuated equilibrium triggered by innovative disruptions so significant that entire industries either adapted with major change or disappeared (i.e., became extinct). The most important attribute of the fourth Industrial Revolution – the continuous creative destruction marked by an exponentially accelerating rate of change enabled by the seemingly infinite synergies resulting from the convergence of cloud computing, big data, AI, and the IoT. It is important to note that doubling computing capacity every two years while simultaneously reducing the cost – Moore's Law – reached a point that effectively democratized high performance computing and reduced by several orders of magnitude what it costs to establish a startup with little capital and compete very effectively with much larger organizations. Access to the required capital necessary to own / access the means of production, in other words, is no longer a significant barrier to entry.

While I consider Digital Transformation to be inextricably woven into the fabric of the fourth Industrial Revolution, I do not agree that Digital Transformation is the fourth Industrial Revolution by another name, as implied by Tom Siebel. Rather, it is the work necessary for a business to adapt to the continuous disruptive change required to survive and thrive in the Fourth Industrial Revolution. Think of Digital Transformation as adapting to the punctuated equilibrium (point in time when a tipping point is reached and what was the “normal” can no longer remain the "normal" and expect to survive the massive changes in all areas of business.

Question #2: What industry(ies) or types of businesses will be the most negatively impacted by the growing demand to digitally transform to survive in the fourth Industrial Revolution?
Good question and seemingly straightforward. The answer is not as easy as naming an industry or even attempting to list a few industries as many thought leaders and publications have done. The complication rests in what is meant by impact and how it is measured. If by "impact" we are referring to the value of an industry, my response is that it's very hard to pin down an answer. I am in the camp of those who believe economists and even the public markets are still grappling with how to properly measure value in an inherently digital economy. Given that reality, we can't say. What I can say is those industries that fail to embrace the accelerating rate of change and its magnitude will fall behind and ultimately either change businesses or fail. The bottom line: Those that cannot or choose not to accept the significance of the nature of exponential technologies and rate of change will be unable to adapt. In effect they will become extinct.

There are a lot of lists floating around in cyber-space that rank which industries, sectors, market segments, businesses, etc. are at greatest risk of being negatively impacted by the need to digitally transform. Many of these lists include the size and likely outcome(s) of the impact. Generally, surveys with a relatively small number of respondents serve as the data used to make and publish the lists¹. I believe such rankings imply precision where it doesn't exist.

Side note: There are many surveys aimed at specific market segments (e.g., retail banking) across industries that ask CEOs and other senior strategists about readiness, challenges, likelihood of executing on a plan, and even whether digital transformation is an important consideration. I don't have all the survey results in front of me, but they are easily obtained. I recently read a book I pulled from Google Scholar while performing some research. The book cited studies and surveys that suggest up to 70% of all digital transformations fail² for various reasons. What that says to me is that many companies are misunderstanding what is meant by digital transformation as Tom Seibel defines it – i.e., not just an IT project.

Two considerations lead me to make that statement. First, many economists agree that we do not currently have a good way to measure the value of the digital economy. Measuring impact without a way to accurately measure value and viability is difficult, to say the least. Second and more relevant, when thinking about impact to any portion of the economy, it comes down to embracing continuous change with greater uncertainty than prior periods in a structured but flexible manner to enable hyper-adaptability will separate the winners and losers. Technology in this context is less of an issue than corporate culture, leadership, and truly grasping what it means to keep up with change that is accelerating at an exponential rate. I consider organizations and their leadership's ability to grasp just how disruptive a force the rate and magnitude of change represents and reimagining business as a living organism that will change in all sorts of ways to enable hyper-adaptability is a critical measure. Those businesses most able to adapt to change are far more likely to survive in such a fluid digital universe.

It is relevant to note that reimagining business in the manner I intended to convey above includes changing business models, embracing experimentation, encouraging creativity, and generally creating an entrepreneurial environment that supports finding the right form and adaptations at the right time over and over again. These are leadership and people challenges – organizational psychology challenges. Technology is often the easiest piece of the proverbial puzzle. Technology gets blamed for failures and costs that actually arise from inadequate leadership, communications, and commitment.

Question #3: Where should organizations be investing?
Interesting question with a number of ways to interpret it. To offer a more specific answer, I'd like to know if this question is asking about “investment” in the context of time, financial, and human capital to effectively launch a digital transformation initiative? Or, is the question more general and using the term “investing” as a way to reference focus and primarily human capital (strategists) with the objective of shaping a strategy to reimagine a business and all the possibilities for digital transformation direction in order to survive in an inherently digital economy / world?

On the topic of human capital, aka talent, technologists with the requisite skills are in short supply. If your organization's core business is not software engineering and/or cloud computing operations and support, I agree with Tom Siebel's opinion that for your core business functions hire the talent. For functions that are not core, particularly technology-related needs, outsource.

Question #4: What should organizations be putting in place to survive digital disruption?
Tom Siebel's book, Digital Transformation, offers specific guidance to address this very question. I recommend taking a look at his framework. It is a very good starting point.

Question #5: What mistakes are organizations making when it comes to data?
Your question is very good in that it addresses what I believe to be the single greatest opportunity for organizations to create value and adapt with what I believe is, relatively speaking, "the lowest hanging fruit", as the saying goes. That said, your question can be answered from various perspectives and carries with it many potential implications. In the spirit of pragmatism, please indulge my interpretation of your question – particularly its implications to digital transformation.

First, I infer from your question an important acknowledgement or perhaps an assumption that today's organizations must embrace the outsized importance of data in a digital universe. Data is the renewable source of energy powering the digital universe.

Side Note: I recognize that "data" does not automatically mean digital. Data in its purest sense has existed and has been used for thousands of years – yes, thousands of years. Candidly, we need to dispense with a definitional purism. More so now than any time in history, the meaning of words is morphing and a purist semantical perspective is not merely unhelpful, it introduces confusion and can undermine our objective – digital transformation required to survive and thrive as we transition into the fourth Industrial Revolution. For our purposes here, which is consistent with the contemporary use of the term, I define data as [digital] information without context.

Second, I infer from your question that mistakes are measured against the details associated with answers to the following strategic question – one example among many that are no longer confined to the IT department but addressed in the C-Suite:

What does it mean to be a "data-driven organization" trying to keep pace as the digital economy continues to rapidly evolve and change?

Question #6: What are some common challenges organizations face related to data?
Ask Google that question and if you see what I saw, you will see approximately 1,670,000,000 results returned. I took a quick look at the first two pages of results and found many lists describing the top “n” data issues, each list focused on a seemingly unbounded set of data topics. Ironically, the results to which I refer represent their own type of data challenge. Lots of information shared pointing out common challenges but often with little or no context. I cannot overstate the importance of context.

So, I thought I would start at the beginning so to speak. What are the truly fundamental pieces of an organization's "data situation" that represent the first things to assess and address. These puzzle pieces are what I call data "data fundamentals" and serve as a baseline.

Data Fundamentals

Many organizations must start at the beginning and ask / answer the following fundamental questions to establish a baseline: Simply put…

Note: the semicolon delimited list below corresponds to the list of "data fundamentals" above. For example, "what do you have now" is like asking to perform an inventory. "Where and how is it stored" is relevant to data control, protection, and access. "Business continuity" speaks to data protection and control.

What do you have now; Where and how is it stored; Where did it come from / how was it generated; Can it be accessed; Should it be accessed and by whom for what purposes; Data use rules based on the type, source, and purpose of the data (e.g., privacy, security, regulatory requirements and constraints, organizational values and expectations derived from existing corporate culture); Data access controls; Business continuity (e.g., disaster recovery); Ability to identify dirty/bad data, redundant data, and semantic inconsistencies across different systems (e.g., the same codes used to look up descriptions, rules, etc… stored in various systems but inconsistent definitions - meanings).

Starting at the beginning, as I have suggested above, may seem insultingly basic. I assure you it's not. Like any normal distribution curve, there are outliers on each end of the curve with the bulk filling in the bell, so to speak. And that bulk filling in the bell is comprised of many organizations that simply do not realize what data they have, where the data they do know about exists, and how to access the data in a meaningful way.

Even more sophisticated, data-aware organizations are unfamiliar with the many different formats of data that exist within their organization. The majority of organizations still think about “data'' as discrete, structured information stored as rows in a table of data with each element defined by the column heading. We are no longer constrained by the columns and rows of discrete data elements common in spreadsheets and RDBMSs. Text, images, audio, video, publicly available data sets, etc… can be used by creative and critical thinkers to derive insights and aid in the decision support process.

Question #7: Other than technologists, what does the future of talent look like in a data-driven organization?
Having access to digital technologists and data scientists is good, but it is not enough. Organizations need to know how to extract, organize, manage, and visualize huge data sets of varying formats and contexts. The era of the Expert Generalist (need to find out who coined the term – I do not recall) has arrived, and that increases the value of those knowledgeable in the social sciences, biological science, and liberal arts. Organizations will not only benefit but truly need creative, critical thinkers, who are rabidly curious and action-oriented. Subject matter experts in select domains who also possess knowledge by experience or education across a variety of fields are important and valuable assets. Small teams composed of members with such talent will serve an invaluable role interpreting the results of more complex analysis made up of a broad array and variety of data. For example, social scientists and psychologists familiar with inherent biases help mitigate such biases from creeping into critical analysis that ultimately gives rise to business decisions of all types. Let's consider confirmation bias - a common issue for many organizations. With so much data pulled together and analyzed in myriad ways, unaware of this tendency, decision makers often fit analysis to a desired choice. "My gut suggests…" and this is how the data supports my gut feeling. The book, Noise, is particularly focused on highlighting the problems caused by fitting data to a predetermined choice.

An example of confirmation bias, which is only one of dozens of such innate biases that are part of our human nature, is the proverbial “echo chambers” created by the Attention Economy's algorithms. As information is shared across the Internet using algorithms designed to deliver results predicated on the idea that the user's preference in prior online actions/choices dictate what and where to deliver content in all its forms before the next online interaction even happens. Said differently, the AI algorithms built using the knowledge and know-how derived from the type of talent I discussed above leverage this knowledge to the advantage of the content provider. In my example here, confirmation bias is magnified and results in the creation of the "echo chambers" we now read and hear about in the news. Just as such knowledge and know-how can be leveraged to the advantage of the content provider, such talent is valuable to mitigate erroneous and misleading analysis that can give rise to ruinous decisions. In short, awareness and understanding of such human characteristics, tendencies, and approaches to decision making is more critically important now than ever.

Question #8: Do you have any advice for organizations that need to undertake a transition to the Cloud?
With most changes, the first step is often the hardest. It will definitely help if there is full organizational support for the Cloud transition. Overall this is a topic I’ve spent much time on and have collected what I’ve learned here in an eBook:

¹ Please note: Small survey cohorts can be misleading and often fail to accurately represent the findings from analysis of the small survey population.

² Why Digital Transformations Fail: The Surprising Disciplines Of How To Take Off And Stay Ahead. Copyright © 2019 by Tony Saldanha. Published by Barret-Koehler Publishers, Inc.