There is a lot of hype about the new position of “Data Scientist” (see January’s Fortune magazine). I am always happy for analytics professionals to get recognition and excitement about what we do and I’m not really hung up on what title is used. However, if you take the time to think about it, the roles and responsibilities of a data scientist are virtually identical to what analytics people have been doing for years – a fact that seems to get lost at times.
Whether you grew up a “data miner”, an “analyst”, a “predictive modeler”, or something else, we all have always had the same goal – take a bunch of data (often data that is bigger and harder to deal with than makes you comfortable), explore it, and drive business value from it in creative ways. Today, data scientists may be focused upon some new data sources and their tools of choice may differ, but those differences are tactical. In the bigger, strategic picture, we are all doing the same thing. Any highly talented analytic professional can easily pick up how to program in a different environment and make use of a new data source. If you can program complex macros in SAS and are a SQL wiz, you can learn Java and MapReduce. The opposite is also true.
The fact is that the tools and data utilized don’t define who you’d want doing your analysis for you. Just as data doesn’t add value unless it is used correctly, technical proficiency doesn’t add value unless it is applied correctly. Technical skills are just table stakes if you want to find a great analytic professional. The traits that define the best in the business, regardless of title, are softer skills. The best analytic professionals in the business, whether they call themselves a data scientist or something else, have things like business savvy, creativity, solid presentation and communication skills, and a very good intuition.
I want to focus on creativity for a moment since it is the factor above that is most often missed and most often surprises people. There is a lot of creativity involved in figuring out how to best approach a problem. It is also required when working around the inevitable data problems and assumption violations that are found as an analytic effort progresses. There is even more creativity in figuring out how to interpret, position, and act upon the findings of an analysis. Someone with only strong technical skills, but no creativity, won’t be a great analytic professional, whether they call themselves a data miner, a data scientist, or something else. The very best analytic professionals are artists as much as scientists.
Two painters can paint the same scene using different types of paints and styles. Both paintings can be amazing, yet totally distinct and unique. Similarly, two great analytic professionals can use different approaches to the same problem and each have compelling results. That’s because there is artistry all through the analytics process. The artistry is in how you define the problem, design the analysis, work with the data, and show the results. It is a mistake to ignore the
creativity needed in an analytic professional. It is a good idea to probe whether or not a candidate for a job has some sort of music, art, or other creative activity in their background. The talents that enable those activities are also required to generate great analytics.
My point here is that you don’t really want someone who only has technical skills. You need something more regardless of what you are calling the analytic professional you are hiring. You need someone who can paint a compelling picture with data. You need a data artist.
So, keep your data scientists and data miners who only want to focus on the technical aspects of their job. Send me a data artist instead!
Originally published by the International Institute for Analytics