It's hard to imagine a hotter job now than the data scientist. Supply trails demand and, as a result, there's no shortage of myths around them.

But is there any real difference between traditional statisticians and what we now call data scientists?

I asked my friend Melinda Thielbar, a research statistician developer at JMP (a SAS company). Her first answer to the question was "Yes, about $10,000 in salary." No argument from me there, but I probed deeper and asked, "Would it be fair to say that the former have typically worked with existing datasets while the latter have been more involved with the retrieval, analysis and generation of (usable) data?"

I found her response fascinating:

We started as statisticians. Then, we became "data miners." Now, it's "data scientists." Just like toothpaste now comes in about 14 different flavors, even though their main function is to clean teeth.

A quick model

Ultimately the distinction between the two may not matter. Consider the following two fictitious organizations:

• ABC: A progressive organization that embraces data discovery and data-oriented thinking.
• XYZ: A dataphobic organization that relies exclusively on policies, politics and tradition.

Let's say that the two organizations operate in the same industry and country. ABC hires a pure statistician and empowers her to challenge conventional thinking and existing internal practices. She uses best-of-breed analysis and dataviz tools to unearth new insights into customer and employee behavior. She follows the mantra "go where the data leads you." Inside the company, non-statisticians carefully listen when she presents her findings, and they implement many of her recommendations.

On the other hand, XYZ hires a data scientist who immediately meets with resistance. He must jump through hoops to access internal corporate data. IT forbids him from using contemporary statistical and dataviz applications. He builds proxies for what he cannot access. He attempts to break down organizational data silos. He finally takes a few meetings with key players in different lines of business (re: marketing, sales, HR, R&D and finance). The LOBs want no part of his findings. They see no reason to change.

Moniker aside, which organization do you think will be more successful?

Simon says

Titles only matter so much. Results hinge upon actually doing something with data, not merely on making a high-profile hire.

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