What is Actionable Analytics?

This is my passion and yet, when I say the words “Actionable Analytics” eyes glaze over and the blank nodding starts. I thought it would make sense to take a moment and break it down so we can all go forward on the same page!

Definitions (Merriam-Webster):

  • Actionable – able to be used as a basis or reason for doing something
  • Analytics – the method of logical analysis

Google Search Results

  • Used as a set of buzzwords for several Business Intelligence Tools
  • Gartner & Forbes have touched on it lightly
  • Heavily used in marketing

However, when we put these together we somehow end up with some sort of neural short circuit. It seems plain, even mundane to say that we need to use the analytics to make business decisions, or take action. I think that it might have to do with math – somehow the minute there is math there is a shutdown of the mental conveyor.

Analytics – Action = Math

And in general, people seem to be afraid of math. But the math that is hidden in analytics is actually not all that scary, and new analytics methods hide more and more of that math. In fact, scientists and statisticians are largely unable to even explain the math behind neural networks. What this means is that less and less of the math will even be visible – so my hope is that with math hidden we can focus on the Action part after we have analytics.

So, you may now be asking, what types of Action should be taken on Analytics? Here is a quick action scenario to give you a head start on this mental exercise!

A medium size retailer is struggling with hit-and-miss promotional results

  • Data: Sales transaction data, promotion timelines and details
  • Analytics: Market Basket Analysis
  • Analytics Results :Low margin items are being boosted by the types of promotions currently being offered, and customers are only buying the sale items . The analysis shows that the customers are more likely to buy certain high margin items when a different promotional pairing is used.
  • Action: The retailer offers a paired sale on low and high margin items the next month and plans to have the analyst compare the results with previous promotions to validate the new analysis and refine promotions moving forward.

Now really, was that a huge stretch? You can repeat this exercise for any number of industries, market segments, data types, etc. The key is in asking the right questions all along to lead to an action – in fact, there should be some idea of what the action is before the analytics exercise is even fully baked. To wrap it up, here is a non-actionable scenario I was exposed to in my previous corporate life:

Measure the productivity of software developers according to the lines of code and number of version check-ins they perform

  • Data: Versioning History, Code Metrics
  • Analytics: Basic modeling to clustering analysis, etc.
  • Analytics Results: A “score” or comparative value assigned to a software developer.
  • Action: NONE First of all, this does not translate between projects and languages (developer X working on product A is not comparable to developer Y working on product B, etc.) Secondly, management action based on lines of code would be highly suspect, outdated, easily gamed, and downright wrong.

Could this scenario have been turned into an actionable one? YES! But what had to happen is that hard questions needed to be asked (they were), and answered (most importantly) up-front and throughout the project to ensure the outcome is steered to a usable set of analytics, not just a result that sits collecting dust in a powerpoint presentation.

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