We have so much data at our fingertips. Every touch, interaction, click, email, webpage view. It all results in data. Even when we walk from one room to the next our phones are counting the steps, movement, changes in latitude and longitude. We are measured to within an inch of our lives.

Some of this data is captured and reported back to cloud based servers scattered across the globe. Some of it isn’t. But do we know? Do we care?

I was speaking with John Dobbin yesterday about the Data Paradox. We have more data than ever before, but less understanding of what to use it for. We spend our time analysing dashboards and combing through spreadsheets in search of that elusive insight. Sometimes as a marketer I feel like Coleridge’s ancient mariner:

Water, water, everywhere,
And all the boards did shrink;
Water, water, everywhere,
Nor any drop to drink.

Data visualisation goes a long way towards solving this challenge. Done well, it can bring your data to life – tell a story – and foreground important details. But with almost every visualisation I see, I am always asking myself, “why”. Why is this important? Why did a change occur? Why didn’t a change occur?

Take a look at my recent TwitterCounter graph below. It shows follower/ following counts over the last month. You can see there are a couple of spikes in terms of follower numbers. But you can also see that “following” numbers remain on an even trajectory. Just the simple act of looking at this graph reminded me of the actions that I had and had not taken over the last month. It made me check back to see what I was doing on March 7.

And on March 11, clearly I did something to arrest that growth. But the following week I was growing again. Not as steeply, but strongly.

twittercounter

Correlation vs Causation

Again the question of “why” raises its head. What I am interested in is not the correlation but the causation. At the book launch of Martin Lindstrom’s new book, Small Data, he suggested that it is the small data that drives causation and that big data shows the correlation. So with this in mind, I looked to the small things.

  • Ahead of the first spike in follower growth I started using Meet Edgar to more consistently tweet. Prior to that it was randomised and scheduled or ad hoc. It was not a function of what I was saying, but the fact that I was saying it.
  • The second spike built on the earlier week but benefited from my appearance on DisrupTV with GE’s Ganesh Bell and Constellation Research’s Guy Courtin.

While the big data revealed the trend and the results, it was the small data. The personal data. The insight, that actually revealed the causation. As Martin Lindstrom suggested, and as I have written previously, small data – the known unknowns of the marketing world – tell the story we are waiting to hear. The question is whether we are listening for a story or searching for data.