To be “enterprising” is to be eager to undertake or prompt to attempt. To show initiative and be resourceful. These are leadership traits, so to be enterprising is to lead. “Analytics” is how we use data to inform decision-making, in the context of achieving business objectives. These are management practices, so analytics is about management.
“Enterprising analytics” is about being creative, resourceful and adventurous with decision-making to achieve business objectives. It is about the set of leadership and management practices that need to be in place for an organization to make the most of its analytics investment.
Leadership is about overturning established thinking
In his 2014 book The Organized Mind: Thinking Straight in the Age of Information Overload, Daniel Levitin wrote: “Companies can be thought as a transactive memory system.… The company as a whole is a large repository of information, with individual humans effectively playing the role of neural networks running specialized programs. No one person has all the knowledge, and indeed, no one person in a large company even knows whom to ask for every bit of knowledge it takes to keep the company running.”
All business leaders labor under a paradigm. The legacy business paradigm is that of the era of mass-production. As Thomas Kuhn explained in his 1962 book The Structure of Scientific Revolutions, a paradigm is a framework of accepted theories and shared knowledge. The thing about paradigms is that the more they are examined, the more anomalies are discovered.
In a mature intellectual environment, these anomalies are welcomed as signposts toward a more accurate paradigm. The business world is many things, but I’m not sure that “mature intellectual environment” is one of them. It takes a great deal of blood, sweat and tears to overturn established thinking. But isn’t that what leadership is about in the modern era?
Conventional ROI works against analytics investments
The thing about paradigms is that they have a tendency toward significant collapse once fundamental tenets have been called into question. When Daniel Kahneman recounted in his 2011 book Thinking Fast and Slow how he and Amos Tversky overturned Daniel Bernoulli’s long-lived theory of utility, he observed, “You know you have made a theoretical advance when you can no longer reconstruct why you failed for so long to see the obvious.”
A principal challenge in assessing the value proposition of an enterprise analytics investment is in convincing senior leaders to come up with the money. This will lead to a business case, and at the heart of the business case is the financial argument — the dreaded ROI. This is where many an analytics investment comes horribly unstuck. The paradigm in operation here is that of valuing a manufacturing plant or equipment. Simply put, analytics investments should not be judged using the logic developed for evaluating manufacturing investments.
The value proposition of an enterprise analytics initiative is in how well it promotes and sustains organizational learning. That is, in the sense of improving the organization’s ability to function as a transactive memory system. Human memory is an artifact. It is inextricably tied up with ascribed meaning. The same goes for an organization. We might have a lake of data, but to capitalize on that lake we must convert that data into facts, and then agree what those facts mean.
Enterprise analytics is about emergent phenomena
We might agree with all this philosophically, but we still need to fill in the various tables and calculations in the financial section of the business case. Quantification of emergent phenomena can be done, but how common is such a capability in the skill set of the management professional? And is it not even more rare in the financial professionals whose task it is to review cost and benefit estimates for senior decision-makers?
There will inevitably follow a discussion about how intangibles can’t be measured. This is a mistaken belief, if not lazy thinking. The misconception stems in part from a misunderstanding of what measurement is and how it can be applied to human environments. There is also the issue of strong anchoring to the existing paradigm of measurement.
If something matters to us, then we must be able to observe its effects. If we can observe something, then we can express it as a quantity. If we can express something as a quantity, then it can be measured.
Business use cases undersell the enterprise analytics value proposition
The problem is not around the myth of being unable to measure intangibles. It is around the systemic and strategic weakness of misapplying mass-production-era valuation techniques to the business-critical analytics investments of the social era.
So what is a business professional to do? Build a use case around a known service need. This will at least get the analytics investment into the building. Does it undersell the potential of the analytics system at the enterprise level? Yes. Does it encourage a business unit to claim “this is ours” and prevent broader system use? Yes. Are those suboptimal outcomes for the strategic CxO? Yes.
But what other options does a CxO have? It’s easier to shepherd in several analytics platforms than it is to challenge how analytics investment decisions are made at the enterprise level. Or is it? Is this something that we should just shrug and accept? My answer — and I hope this would be the answer of any strategic CxO — is hell no. Because what else is a leader for except to overturn established thinking?