Analytics provides the data, leaders negotiate the truth

Enterprising Analytics

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.

Analytics is a negotiation about what facts mean

We all fall back on the first discipline that helped us understand the world. For me, it is history. To paraphrase Pieter Geyl, history is an argument about what facts mean. This perspective has helped me through my entire career, but the domain in which it has been most applicable is that of analytics.

Analytics, like history, is about what facts mean. But, because analytics occurs within the context of a business or service enterprise, social interaction must proceed beyond an argument. Debate, discussion and discourse are good, but at some point there will come a moment where an authority figure will make a call about meaning. It falls to the strategic CxO to derive work from strategy.

At the enterprise level, analytics is a negotiation about what facts mean. Unlike many words adopted by the profession of management, the word negotiate has had a stable history. Negotiation is a form of communication that reaches toward a mutually acceptable outcome. Good negotiation is principled negotiation, where we focus on “basic interests, mutually satisfying options and fair standards.” Because analytics is about how we use data to inform decision-making, “getting the facts right” becomes a key issue.

What has happened versus what is true

But what are facts in this context? The original sense of the term was about something that has happened, an occurrence, an event. But it changed its form in the mid 17th century, when it came to mean something that was true. It’s a considerable leap to go from “What has happened?” to “What is true?” The timing of the shift in meaning is important.

This was the era of the beginning of modern Western philosophy, when people challenged the medieval basis of explanation. The people who helped move fact from what has happened to what is true include such giants as Thomas Hobbes, Rene Descartes, John Milton and Blaise Pascal.

While not quite so dramatic, in our present era of management we are going through a similar threshold state as we put distance between us and the philosophy and practice of the mass-production era. And it may be that the role analytics plays in that ongoing evolution will help us distinguish between what has happened and what is true.

Give me the numbers

This issue is not new. The entire world, it seems, has heard the refrain “lies, damned lies and statistics.” The origin of the phrase is lost, but the Lord Courtenay put it well in 1895:

“After all, facts are facts, and although we may quote one to another, with a chuckle, the words of the Wise Statesman ‘Lies, damned lies, and statistics,’ still there are some easy figures the simplest must understand, and the astutest cannot wriggle out of.”

If such an observation was made “with a chuckle” in 1895, I can’t imagine the strategic CxO laughing much 121 years later. I’ve seen senior data scientists spit the dummy when laymen management professionals challenged their facts. And for ”facts,” read ”truths.” I’ve also seen senior management professionals choose the facts that prove their investment case. And every junior analyst has a story about the manager who asks for ”numbers that show x.”

At the enterprise level, analytics is a negotiation about what facts mean. It may seem that I’m pushing the point too far. After all, we have ”data lakes,” not ”fact lakes.” We ”mine data,” but we don’t ”mine facts.” We employ ”data scientists,” not ”fact scientists.” Though on this last point I would just settle for any scientist.

Data are not facts, facts are not truths

The relationship between facts and data is a serious issue for the strategic CxO. William Davies wrote a great little article for the New York Times on this topic, in which he said this:

“How can we still be speaking of ‘facts’ when they no longer provide us with a reality that we all agree on? The problem is that the experts and agencies involved in producing facts have multiplied, and many are now for hire.”

His article focused on the troubling implications of our ”post-truth” world, and in particular the issues underlying the Brexit and the alt-right. All this might seem distant from the challenges facing the strategic CxO. She might be excused thinking this isn’t a big issue. But after thinking it through, she’ll quickly come to realize that it is perhaps one of the biggest issues she faces.

As Davies says:

“We are in the middle of a transition from a society of facts to a society of data. During this interim, confusion abounds surrounding the exact status of knowledge and numbers in public life, exacerbating the sense that truth itself is being abandoned.”

The strategic CxO is an arbiter of truth for the enterprise. Truth, like many social challenges, is negotiated. And because enterprise analytics is about learning, negotiating what facts mean is a task that can’t be avoided.