A Skeptic’s Perspective on Data and Analytics in Digital Transformation

Jake Freivald
June 19, 2019

It’s tough being a marketing VP who’s also a skeptic.

On the one hand, I’d love to believe in all of the hype surrounding “digital transformation.” After all, it makes sense that improvements in the generation, collection, and use of data will help companies reinvent themselves, blow away their competition, and create new industries that we never saw coming. We’ve seen it. We know it’s possible.

On the other hand, for every company that successfully reinvents itself, there are probably dozens who have lost customers, employees, and revenue because they didn’t stick to their knitting. I have to wonder how many buzzwords we’ll need to see sprout, wither under the heat of implementation, and die before we accept the fact that buzzword compliance usually doesn’t bear fruit.

So let’s get serious. Let’s talk about what digital transformation is meant to be, and think about ways that we might be able to actually achieve at least some of its goals, and think about how not to ruin our companies and careers in the process.

Getting Practical

It starts with ideation. However this ideation proceeds, it includes executives deciding that the company could be radically different than it is, in ways that make it more successful (for values of “successful”).

Perhaps the earliest version of digital transformation is the Yellow Pages: A company that sold a physical book became a company that provides information for free and makes money through advertising or value-added services.

We’ve seen examples of companies that sell physical objects (aircraft engines, CPUs, whatever) transitioning to selling services (airplane engine-hours, CPU-hours, whatever-hours). Any company that has gone from selling X to selling X-as-a-service (XaaS) has undergone a digital transformation.

We’ve also seen companies that got paid for an action (e.g., a treatment for diabetes at a hospital) transition to getting paid for an outcome (e.g., controlled diabetes as measured by your lack of time spent in a hospital).

These three types of companies all used information – about engagement rates, about number of hours in service, about outcomes – to identify and charge for things that their customers want, rather than for things that are a means to what their customers want.

They’ve used data to tie a direct line between outcomes and revenue, thereby connecting their success to their customers’ success.

Your ideas for digital transformation will be different, and may be less obvious at first, but they will share the reliance on data to drive a deeper connection to revenue and customer success.

Getting Deeper

The examples as I’ve put them forth remind me of the way butterflies were described to me in elementary school: “The caterpillar creates a cocoon around itself and becomes what we call a ‘chrysalis.’ Then, after a few weeks, it breaks out of the cocoon and becomes a butterfly!”

Sounds simple. But what Miss Geoghegan described to me as a youth was an incredibly complicated process hidden beneath the word “chrysalis.” Every cell and protein of that chrysalis (both the cocoon and the pupa inside it) knew (in some analogical, deterministic sense) what the goal was, and understood every single individual action that needed to happen to achieve it. If any part of the transformation took longer than it was supposed to, or only half-achieved its goals, no butterfly would break free at the end.

I think this is the real lesson of digital transformation: To transform your company, you need more than a vague “I want to be a butterfly!” vision. Yes, you need the broad strokes, but you also need the detail. Every cell in the chrysalis has to be driving the process toward making the butterfly, and there’s little room for distractions or error. So it is with your company.

And – bear with me, I’m about to jump metaphors – this is why there’s so much need for storytelling to make digital transformation successful.

Getting Visceral

Without thinking too hard, and without focusing too hard on nitpicking the prose, read the following two paragraphs.

Paragraph 1:

The girl’s mother came home.

Paragraph 2:

Sinead clung to her Molly doll as the train pulled into Connolly Station. She last saw her mother nine months ago, when she had been deployed to Afghanistan. Did she look the same? Would her mom recognize her? But when the train crawled to a stop and the doors opened, Sinead saw her mother step onto the platform, dressed in camouflage just like the picture – and she beamed. “Sinead!”

If you’re like most people, the second paragraph gave you a more vivid image. You related to it better. Maybe it even affected you a little bit emotionally.

This is probably true even though, statistically, you’re unlikely to be named Sinead, have a Molly doll, have met your mom coming home from deployment at the train station, or worried that your mother wouldn’t recognize you.

In other words, the second paragraph almost certainly isn’t like you, but you probably related to it better anyway.

Why? Data.

Making the Leap With Storytelling

The paragraph about Sinead contains a lot more information for you to absorb, but it wasn’t hard to do. Consider the data points: her name (Sinead), her doll’s name (Molly), the reason her mother was gone (deployed), her anxiety about seeing her mother again, her mother’s mode of transportation (train), how her mother was dressed (camouflage), her mother’s reaction (happy).

There are data points you probably absorbed without even thinking about it: Sinead is a young female, the setting is probably modern-day, and photos are shareable from halfway across the world.

There are even data points that you might not recognize at first, but that might become important once you understand them: Connolly Station is in Dublin, Ireland; Molly was the name of a legendary Irish fishmonger, Molly Malone; Sinead is a common Irish female name.

All of this detail is data. But it’s data that’s easy to absorb, because it’s part of a story. You understand the context, the data in its context, and the data’s meaning. If you imagined yourself as Sinead’s sibling, you might even have imagined what you would do for her during those moments.

Companies engaged in digital transformation need this kind of visceral detail, too, and the need for it can’t be understated.

A company that transforms from a book publisher to a digital service has to have a clear vision for how its revenue model will change, how its customers will be affected, how its customer base will change, and how margins and costs will change.

A company that manufactures a hard good and wants to offer it as a service has to understand how it will charge per hour, per distance, or per whatever-makes-the-most-sense, and how that changes service models and support.

A company that sells you a preventative service and wants to offer it on the basis of its efficacy needs to understand how that affects sales, usage by customers, costs, and more.

It’s worth noting that a single out-of-place detail can undermine the story you’re telling, too. When Franz Kafka put a sword in the upraised hand of the Statue of Liberty in his early novel Amerika, it was pretty clear that he didn’t know what he was talking about. Your data needs to be of high quality, or your executives and employees won’t believe what you’re trying to tell them.

Pulling it Together

In digital transformation, then, you need to create a clear picture of something your company is not, and make all of your employees (and maybe partners and customers, too) believe in it so much that they get engaged in the process of becoming that thing.

That requires both the vision and the story, the broad strokes and the details.

And giving it to your executives isn’t enough. Everyone needs to work toward the same goals – just like the cells in the chrysalis – to ensure that the transformation takes place. They need to know what to do, reliably and viscerally, so that everyone is engaged in becoming the new company.

So the details – the data – need to be clear, pervasive, trustworthy, and easy to consume.

That’s why data and analytics, at scale, is at the heart of data transformation.


We’ll be blogging more about digital transformation and the role of data and analytics in it. Drop a comment if you want to take the conversation in a particular direction, or reach out to us on Twitter or Facebook. We’d love to hear about your efforts, your struggles, your successes, and your questions.

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