May 19, 2022

All data is man-made. Here’s why that’s a problem.

Marbles falling into place

“All data is man-made.”

Of all the many memorable pearls of wisdom Clay Christensen, the world’s foremost disruptive innovation authority, shared over the course of our 27-year friendship, this one recurs with more frequency and import than others.

I wouldn’t have predicted as much. It almost feels like a throw-away, or an innovation bumper sticker. But it’s not.

Proust wrote that the key to discovery was “learning to see with new eyes.” I think that Proust and Clay were using their distinct expertise and language to say something similar: What we “see” is a constructed reality, a product of both the lenses we peer through and our on-hand information.

What I often see is very smart, successful executives mistaking data for reality. That isn’t a flaw with the data. It’s data doing its job of making messy, unstructured, complex phenomena accessible, intelligible, and useful. As such, data is essential, and we’d be paralyzed without it, but it is man-made. Data is created only when humans capture, organize, and represent observable phenomena. Plenty of stuff happens in the natural world that nobody records, so most activity creates no data.

Data is essential. Managers need data to make sense of the world, not to mention perform the basic tasks of keeping up with customers and competitors, tracking performance, evaluating opportunities, and making investments. Data is at the core of virtually every important managerial decision, and rightfully so.

But, managers in large companies spend so much time immersed in the data that they routinely forget the modeled reality is just that: a model. It’s not real. It’s incomplete. But data carries no disclosures or warnings that would remind managers of these shortcomings.

That’s why, to truly disrupt, to create new markets or transform existing ones, companies need to look for a fundamentally different kind of data.

The data difference

At a basic level, established companies generate data when they track their own activity, as well as that of their customers, suppliers, and competitors—both internal operations and external interactions. This operational data—the exhaust of commerce—accumulates inside corporate walls and populates internal reports and analyses. Large companies are “data rich.”

Start-ups, by contrast have very little data, and the data they have is often anecdotal, experiential, messy, and narrative in structure. Entrepreneurs will share stories about initial customers—the challenges they face, the tradeoffs they make—and the compensating hacks they develop. Entrepreneurs develop their initial propositions in this messy environment, often co-creating with customers through repeated experiments and refinements. Sample sizes are small, statistical significance is nonexistent, market sizes are uncertain, and profitability is often unproven.

The contrast between “start-up data” and “enterprise data” is plain to see—both in its quantity and its character. What’s less obvious are the implications of these wildly disparate sets of data and how these differences frame options and inform choices.

It’s important to note how data differs from other corporate assets. Data is far more powerful than the seeming innocence and objectivity of numbers on a screen:  Data constitute internal expressions of external realities.  In large measure, the data we use determines the world we see.

Samsonite, American Tourister and Hartmann were among many established luggage manufacturers competing for the billions spent annually by American travelers. Competition was ruthless and innovations were continuous. Why, then, was it a pilot of Northwest Airlines who developed the first of the now ubiquitous roller board?

Probably the same reason that it was a dentist—not a toothbrush manufacturer—who thought to put batteries in toothbrushes.

Citibank, Wells Fargo, Chase, and Bank of America dominate retail banking in the US, and yet it was Goldman Sachs who built a personal loan business, Marcus, that has attracted four million customers.

And none of the banking giants developed PayPal or Venmo.

A year ago, my Lippincott colleagues and I were working with one of the world’s largest fast food chains in an effort to energize their breakfast sales. Benchmarking against category leaders, they’d identified opportunities to improve their coffee, English muffins, and egg sandwiches. They were experimenting with expensive technology to make their drive-thrus “smarter” and more interactive. Yet, when we immersed ourselves in the circumstances of their customers’ mornings, we found a far different reality than that revealed by their internal analyses and competitive benchmarking.

It turns out that many people actually had pretty crummy mornings, and there was a real opportunity to help people have great mornings with a breakfast that not only fueled them up but also fortified their spirit with human touches. Micro conversations with associates at the drive thru, placing the food in the bag with care, assembling a breakfast sandwich with a discernable consideration for the customer’s experience all added material value to the morning.

None of this data was hard to find, all of it was actionable and valuable, and none of it was found in the extensive data stores of our Fortune 500 client or their multi-million dollar investment in quick serve restaurant software tools. In essence, we immersed ourselves in “start-up data” to uncover market creating opportunities.

What's the lesson for large-company leaders who are hungry for growth?

Check your data.

The data that’s vital for sustaining current operations and detecting customer preference changes and competitive moves or spotting opportunities for brand extensions, efficiency plays, or acquisitions, is not the same data that’s useful for creating a future that’s different from the past. It’s not just enough to sip the “think like a start-up” Kool-Aid. You actually need to gobble down start-up data as well.

Creating a future different than the past requires more than the exhaust of commerce or historical consumption data. Market-creating innovation requires data that takes the shape of stories, not statistics.

If old companies are going to succeed in unlocking new growth, they need to reacquaint themselves with “start-up” data: the messy, unfiltered stuff that reveals the progress people are struggling to make in the ordinary, but important, moments of their personal and professional lives.