Manage Learn to apply best practices and optimize your operations.

Do predictive analytics projects require data scientists?

Expert Mark Whitehorn explains what skills are required for predictive modeling -- and whether business users can do the work of data scientists.

Do you need data scientists on predictive analytics projects, or can business users do their own predictive modeling with current tools?

Data scientist is really just a job title. Before I answer your question, let's look at what that title implies and what skills a data scientist -- or someone doing data science work under a different title -- should have.

According to the Harvard Business Review, data scientist is "the sexiest job of the 21st century." But the term data scientist also has been described in more cynical ways on the Internet, and I would agree that defining it precisely is difficult. One of the best definitions I have yet seen is that a data scientist is "a better software engineer than any statistician and a better statistician than any software engineer."

As for skills and experience, I think coding ability and an understanding of how numbers behave are both vital, as is curiosity. Duncan Ross, director of data science for Teradata's international operations, wrote in a 2012 blog post that "insane curiosity" is the most important trait of data scientists. "In many walks of life," he continued, "evolution selects against the kind of person who decides to find out what happens 'if I push that button.' Data science selects for it."

No matter what their actual job title is, all true data scientists have started playing with some data at 8 p.m. and suddenly find it's 3 a.m. and they're still at it.

But equally important is the ability to communicate with people. If you uncover a vitally important piece of information in a set of data but are incapable of imparting it to others or convincing them of its import, it will have no impact -- and therefore, it will be as if the information were never discovered.

And that brings me to your question regarding predictive analytics projects. What you need for a successful predictive analytics project are intelligent, dedicated people who have a background in analytical techniques and can think up new, innovative approaches to problem solving.

Anyone who claims to be a data scientist should be able to demonstrate these traits, but there is no reason why they can't be found in business users. In my experience it depends far more on the people than on the job title. Of course, background and experience help, but data science is so new that many data scientists seem to be adopting the title without the requisite experience.

Next Steps

Learn the differences between data science and business analyst jobs

Self-service push drives new data analytics project processes

Prevent data volumes from swamping predictive analytics projects

Dig Deeper on Predictive analytics

Join the conversation


Send me notifications when other members comment.

Please create a username to comment.

if the predictive analytics problem is well defined like predicting churn on a customer or default on a credit account, data mining tools allow for savvy data professionals (data architects, data analysts, BI architects) to easily build and test models and keep trying with adding more data and variables until they like the model's performance. On the other hand if the problem is not clearly defined and the selection of variables is all over the place, a more formal training in data sciences would certainly be required. On the other hand savvy business users who are used to carrying out rocket science in Excel, armed with the knowledge and understanding of what predictive analytics can do, should be able to conceive problems in innovative ways that can be solved using predictive analytics. The role of a data scientist as typically defined has to be broken up in 3-4 different job roles and they will easily get the job done
Great points, NSheikh - I think that you're right, but as Mark says in his answer, a lot of people aren't easily making the distinction between data-savvy professionals and true data "scientists."
Next generation of Predictive Analysis will be,and already is, fully automated. A marketing analyst will be able to run and build new models. It is "Predict by Click".