We are surrounded by skilled people. We are also surrounded by unskilled people. How do we manage such diversity? How can we be sure that our Data setup is the one you need? Discover my thoughts throughout this post, being the first one of a series of posts explaining the foundations of this blog. Want to read it? Avant!
Already as an experienced worker, in my first job as a BI and Analysis “manager” (at that moment I had no clue what a manager was because I never had good managers), I was first required to make an evaluation of their set up on the Data and Analysis department, although they felt very proud of their Business Intelligence efforts. The first question I asked, ironically, was: “Ah, do we have such department”? I was presented a Data Scientist, a Campaign Manager, a guy that was creating some dashboards in Excel, and a self-denominated UX expert. That was the marvellous team. After talking individually with them for a couple of days I came back to my Manager and said to him: “Sorry, you have no team. You have a set of skills but no cohesion between them. You built your Data strategy based on silos!”. “What’s your recommendation?”, he said. The answer was the easiest one I’ve given in my life: “Burn the silos!“.
Is my Data and Analysis setup the right one? Sorry, I would say: NO. I can bet my next month’s salary that there are important flaws in the way you’ve organized your Data, BI, and/or Analysis team.
Before starting, let’s make very clear what is probably the most important topic when building your Data team: there is no unique and/or magical solution. Every Data team should be built according to the Business needs, the company culture, the skills available at that moment, the available budget, and a long etc. I’m going to give you the classical consultant answer to the question of how to organize the Data team: “Depends“. If you came here wanting to seek a magical solution, this is definitively not your place. Further more, this is not your best job. Building up the best team possible is a tough task, and, for sure, not an easy one. It will require a lot of trial and error and a lot of adjustments.
The basic outcome: what do you expect from your Data team?
I was told many times that the Data and Analysis team should give added value to the company. If you think that a Data or Analysis team should bring knowledge then I must say you’re partially wrong. Knowledge is the path, not the mean. The ultimate end of tracking, analyzing, extracting knowledge, etc., is to create action! A feature in the website should not be tracked or analzyed if these actions bring no action to the business. The team should be able to trigger a change in the business when delivering knowledge based on an analysis. If there is no action out of these efforts, then the whole Data strategy is totally worthless.
Simplifying the lifecycle of a Data team, we could say that there are four main steps in an analysis strategy:
In first place we have the business needs. The team should be able to gather them, understand them, and forecast what they could look like, in order to deliver Actions proactively. This implies clear and crystal communication skills with the business owners. Once the need is clear, we proceed to evaluate the availability of the necessary data for the potential analysis (in a separate post I’ll come back to this point). Then we analyze, out of which we obtain knowledge. Afterwards we come out with actions: we need to make sure that every single analysis done, every single report delivered, any single data extracted is actionable, in the sense of provoking some change, some further thinking on the management level, or, in the last-but-not-least case, a change of operations and/or strategical decisions. If an outcome is not actionable then, simply, should not take a single minute of the team’s time. This is where many companies and set ups fail, and actually creates the sensation that there should be no further investments on the Data and Analysis team. As well, I’ll come with further insights in following posts.
Now that, in a high-level point of view, we understand how the lifecycle of Data works it’s time to understand that this process, as it’s exposed right now, it’s based on silos. Each step is traditionally done in independent ways: the web analyst specifies and validates tracking, the data scientist probably determines that a regression analysis is the most suitable to mine the data, the campaign manager states that some campaigns should be stopped, the reporting manager tries to gather-and-show all this information (after it’s being generated), etc. All this process, again, has been built over a silo basis. With the process implemented this way is virtually impossible to extract real action out of it.
Here and now is where the Manager needs to step in, in order to wrap up these processes into a single one. Then, and only then, we could start talking about having a successful set up of our Data and Analysis team. When doing this for the first time, is very common to fall into one of the two following mistakes:
Excessive focus on Tracking+Analysis: