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Tom Paradis
Hypothesizing For Dollars: a Caveat
12/6/2012, 12:15 PM

“We need a data scientist.”

Odds are you’ve heard this discussion recently, either among people in your organization or by someone you know in the business world.   More and more, this term has been spoken by executives to the point of becoming the new cliché, joining the ranks of “leveraging big data”.  And that is not to say that these are meaningless budget items; quite the contrary, they are crucial instruments that can mean the difference between industry leader and industry laggard.
 
But do you need a data scientist?
 
The answer is not as clear as every blog post or internet article makes it out to be.  Other questions should be asked first.  For example:  What is occurring in your business process?  What are you trying to accomplish?  What have you already learned?  What do you already have in place? These questions usually answer the previous one implicitly. 
 
  • You’re a small company with a couple thousand customers and dozens of data points looking to learn more about your customers so that you can expand.   Resources are likely better spent on acquiring data regarding your customers’ behavior outside of your site.  
  • You have a mountain of data and no reporting whatsoever.  In this case, resources would be best used setting up a data warehouse, cube, etc. so that your relevant information can be easily accessed by stakeholders before determining what to do next.  Give your interested parties concise reporting and base level insights first.  Crawl, walk, run.
  • Your company has numerous complexities with partially crossing business processes that create noise in any sort of basic “causation” analysis.  If this is the case, you might already be in a “big data” environment, and a data scientist might indeed be a net positive acquisition for your company. 
 
Let’s say that you find yourself confident that indeed you do need a data scientist.   These folks are a scarce resource in today’s job market, and cost a small fortune.  Don’t fret.  There’s a strong possibility that someone within your company is close to what you really need.  You have a small team of data analysts?  They likely know SQL and R, and maybe more.  One of them is probably a programming language crash course away from becoming your data scientist-lite, with a huge head start both in business knowledge and learning the complexities of your company’s data.  Similarly, you maybe have an engineer on staff who has an affinity for mathematical problem solving and an eye toward machine learning.  They too, might be closer than you think to donning the data scientist lab coat.  
 
Remember also that analysts can provide a lot of clarity and “gold-digging” on their own.   While the two are very similar in their use of data to “find money”, the data scientist truly is attempting experiments by testing hypotheses.  Just as with other sciences, the results of experiments are often fruitless. Analysts, meanwhile, are finding areas of opportunities, gaps, and trends that are easier to unearth.
 
This is not to understate the value of the true data rocket-scientists that are developing world-class algorithms, improving sentiment analysis, or just bringing us closer to Skynet out of curiosity’s sake;   your business very well may need them.  With that said, taking a closer look at where your business currently is and what specifically you are trying to learn and accomplish will help you decide the correct course.
 

 

Tags: data , analysis , insights , BI
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