Analytics: It's All about People

Read more at http://www.msightanalytics.com/blog

In a recent Dilbert strip (April 24, 2015), Dilbert declares: “I found the root cause of our problems… It’s people. They’re buggy.” The thing is, even with all the hype around analytics, this is so true that it almost isn’t funny.​​​

There are two key disconnects in many analytics initiatives today: the business feeling that the data scientists do not understand the business needs, and the data scientists feeling like the business is not listening to what the data have to say. And the gap is not closing nearly as fast as we would like.

What we have is a failure to communicate.

Many data scientists have been hired, and many great algorithms have been developed, many of which have died a lonely death without ever being appreciated. In less dire situations, some analytics is being consumed, but it is far from optimal. What we all need to realize is that analytics is really just a methodology, and a data scientist is just a person with the appropriate skills to apply the said methodology. In reality, however, the business and the data scientists often have practical expectations from each other that are not very well articulated, resulting in friction that leads to distrust and/or indifference. With that said, I have a message for the business leaders and another for the data scientists.

To the business leaders: If great analytics happens in the forest, and no one is there to make decisions with it, does it make a business impact? The answer is no. Hiring data scientists does not make analytics happen. In fact, hiring data scientists is not one of the first things I would recommend to any organization starting out with analytics. Analytics is not just-add-water—there must be a culture and an ecosystem along with the right processes and functions in place to make it all work. Unless the organization was built data-driven from the ground up, building an analytics capability will always involve a degree of retrofitting. Until the people are ready to receive analytics, it will not be received.

To the data scientists: Building the best model is your job, but you must keep in mind that it is not the end objective–it is simply a means to the end. You have a specific skill set for which people are willing to pay, and your objective is to help people do better at whatever they are trying to do better. There is always a person at the end, and often in between; care for all people involved, and build a positive relationship with all of them. Build models for others–whether you like it or not, being an expert holder of a skill set means that you have a responsibility as a consultant in some form and thus a responsibility to manage your relationship with the end client, with very few exceptions. Without people ready to embrace your work, even your most beautiful algorithms will sit idle. Strive to connect with the people involved, and you will find that not only you will have an entirely different relationship with your client, but also you will approach the analysis differently.

Being successful in analytics, whether you are a business executive or a data scientist, is not at all about the capability to do analytics. It is about people working together and relating to each other. You have a much better chance of success by doing basic analytics with the right people, processes, functions, and culture, than by doing great analytics without them.​​​​

 

P.S. I’ve used the term “data scientist” here for mere convenience. What it should really be called is another discussion!

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