Data literacy before advanced analytics

I like baking, mainly bread baking but often enough I bake cakes, I usually prefer the simple ones that don’t have much icing however. For me the most delicious of cakes start with the basics, butter, eggs, flour, sugar. After you’ve nailed those basics ingredients you can start getting more adventurous by adding carrot, walnuts or anything else you fancy.

The connection stems from a trend in the world of analytics, one where companies are reaching for the pipping bag before mastering the basics of a moist and well baked cake. The alleged cake here is advanced analytics.

There’s a place for Machine Learning, Predictive Analytics and other advanced techniques for sure, but companies can only leverage those techniques by first improving data literacy across its ranks. Sure, I’m guilty as anyone else of being tempted by the shiny new thing and it’s hard to ignore the appeal of analytic teams in organisations like Google, Netflix or Spotify. However, such projects are often being carried out by a small group of individuals and the transition or handover to the rest of the company is hard to come by. It’s not unheard of analysts walking away with the project as most of it sits in their head and others around wouldn’t know where to start.

It’s also worth bearing in mind that most companies don’t have the same pool of resources available as companies above mentioned, either in budgetary or physical terms and often what companies lack across the board is data literacy and not the ability to predict from where the next purchase should come from.

Knowing the basics is of increasingly importance as tools like Tableau make real self-service a tangible goal for many organisations. Therefore, arming workers across the board with basic skills that result in a lean and nimble organisation, leveraging the synergy between basic and advanced analytics will be a key differentiator in the very near feature. Learning why you cannot take an average from an average, or why you calculate a Profit Ratio as SUM([Profit])/SUM([Sales]) and not [Profit]/[Sales], or even how to calculate a weighted average interest rate are fundamental for the workers across every department.

As Tableau and BI in general muddles the water between point and click and pure coding, the question is not one or the other but the power resulting from that combination. Being able to then convey that in business terms removes the responsibility from a selected group of individuals and expands it across the company.

A few ideas to up skill your teams, create guidelines, we often create style sheets around colours and design. Why not add some notes around business rules to aid those working with the data. Consider workshops focused only on calculations. If your users are already proficient in using a tool like tableau to create a chart, the natural step is to help them understand calculations and aggregations. These can be run in short bursts over lunch or at the end of the week for instance.

Whatever you do, make sure it’s supportive and at the end of the training you will have motivated individuals looking forward to work with the data available to them.

Thank you for reading