HBR recently published a remarkable statistic that says that between 2013 and 2017, marketing leaders have largely seen data analyst talent stagnate.
Why is that and how do we fix it?
One upon a time I worked with a wicked talented group of data analysts. Seriously, these are the kinds of professionals that could come up with algorithms to save the planet.
The only hitch is that they were siloed like you wouldn’t believe. They were laser-like focused on the data so they kept their process “clean” by not allowing other disciplines to add to or be involved in data analysis or delivery. On hand hand, I can see their point. Often non-data professionals bring bias to the table.
What happened with this team, like anything in business though, is that their deliverables were very one-sided. In other words, when they worked alone they often struggled to solve actual business problems. They would present data, not insights.
Like HBR presented in a recent article, you have to have multiple viewpoints in order to solve business problems – particularly when dealing with data. In fact, they cite a remarkable statistic that says that between 2013 and 2017 that marketing leaders have largely seen data analyst talent stagnate.
They go on to say…
Some marketing analysts excel at math and coding, and some excel at framing issues, developing explanations, and connecting to business implications. A far smaller set excel at both. Companies either need to wrap these variegated skills into one person through training and accumulating different types of experiences, or, more likely, assemble a team that is sufficiently facile with the techniques that they can interact productively, ensuring that there is some mechanism to match the approach (and the analyst) to the problem. This match requires senior talent, with the breadth of perspective to align analytical resources and business problems.
What I love about HBR is that they are very solutions-oriented. The remainder of the article they go into the solve for this problem including bringing in cross-functional talent to data teams to 1) help define the problem in the first place, 2) map data to business problems and 3) synthesize data into insights relevant to solving the problem.
That last point is the killer idea, in my opinion. Insights are very different than “data solutions.” Insights bring together critical perspective, behavioral background of the subject matter, and – most importantly – what to do about it.
What I’m trying to do at Morrison is to build analyst talent into our strategy team so that it’s not about one perspective. Sure, the process is messy and it can sometimes cause lengthy discussions but that’s the point.
I wholeheartedly respect and encourage “clean” processes to be built into data analysis. I would never want to influence the outcome of data one way or another. However, to be useful we have to take that analysis, lay it out, and decide what to do with it.
That’s the true promise of data talent.