The Data Personality Scale

Given our attendees were all at a conference about data, it was safe to rule out "Data Avoider" for most.

Next on the scale is "Data Curious". This person feels something like this: "sure, it sounds like it could be interesting. I wouldn’t mind learning a little more about that. Maybe it could be helpful. Could it help me understand more about X or Y problem?” We got a strong show of hands from the audience on this personality type, curious to understand more but not entirely certain about what they were getting themselves into.

Then we have the "Data Supporters". These are the people already convinced. Their view is that "Data is AMAZING! I support you going to buy it, use it and make it happen! I am not personally going to touch it, but it’s incredibly valuable and GO!!!".

Finally, we have the "Data Lover", the true data enthusiast who longs for nothing more than to quietly working with data. He or she was sitting in the audience thinking "Why am I here talking about data when I could be in a room somewhere quiet, analyzing it? Why is this person still talking?". The Data Lovers would much rather have received the summit notes in an email, or a flat file.

So which do we need for the future? Which do you need in your current role? The answer is, kind of all of them.

Being curious, means you will see things as if for the first time, and be able to find connections that others won’t be able to - or ask questions that data lovers might not think to, because they are so close to it. Data Curious walk into a data conversation without preconceived notions of what’s possible, and therefore can help uncover things that others considered impossible.

Data Supporters are critical, because without them, data doesn’t receive the airtime, aircover, or budgets it needs to be successful. A data supporter is the storyteller, capable of understanding the promise and the potential in what hasn’t yet been achieved, and to be able to get an organization excited about unlocking the value of data and taking action.

And finally, data lovers have all the information, and they know what to do with it to bring it all together. Without them, things get talked about but nothing gets done. Melding and normalizing disparate data sets is messy and ungrateful work, and the data lover spends his or her time knee deep in that muck, trying to pull out the gems.

Regardless of which of these personalities you feel the closest to, know that each of these personalities have their place, and in the future they are much more connected to one another. It’s great to have all the information… but awful if you have no one to connect it together, analyze it – and then make it impactful in your organization. It’s great to have actionable intelligence – but terrible if no one at your organization is willing to actually do anything about it. Bringing together all these capabilities is critical in order to keep innovating, and to keep driving forward.