How This Works: Peeling Back the Layers of Data, Like Layers of an Onion

onion.jpeg

Here, in a nutshell, is what usually happens.

We start working with a client and they ask a question that they think has a fairly direct answer. We do the work, and come back to them with our findings.

Pretty straightforward, right?

It is. Right up until they start thinking about what those findings mean, and how they’re going to execute on them in their day-to-day business.

That’s when the more interesting (and challenging) work begins.

They have an answer in their hands to the question they’ve been wanting to know. Along the way, however, they have also glimpsed what else might be possible to know.

And that’s when they go, Hmm.

Here’s an example. We helped a client answer their initial question, which was to identify the competitive set of wines for a particular label, according to price point and location, from the perspective of consumers. We delivered that information and, now that they have it, their obvious next question is, how does this help us sell more wine?

Hmm.

So we talked about it. There were two significant surprises of the research: their competitive set is broader than they assumed, and the map of interest was not where they thought it was. Additional factors included distribution and availability of their wines through ecommerce.

Naturally this raised questions about what to do next. We expect that: you build on what you’ve learned and you make progress, a step at a time.

What was less expected was the desire to go back to the data, again and sometimes again, once the initial layer of the onion has been peeled. You have an answer to your first question, and that’s raised another question, and another one after that.

Our Enolytics data model is built to withstand these layers of questions.

It’s an iterative process. We know you can’t know everything the first time around. It’s even likely that the initial question wasn’t the right question.

But that’s why you start somewhere. There has to be a first question, in order to get – eventually and ultimately – to the right question.

We can also say with increasing confidence that clients want to know more things, and other things, than what they originally thought they wanted to know. That’s why our dashboard subscriptions are generating more and more interest, so that clients can mine their data set at their own pace and over time. They can peel their own layers.

What’s your first question? Where can we start?

I’m looking forward to hearing what that means for you.

Thank you, as always, for reading.