Last Friday at the weekly tweethat I host on Twitter (#BIWisdom), I asked: What is the definition of a data scientist? An important topic, wouldn’t you agree, given that an article in this month’s Harvard Business Review deemed it the “sexiest career of the 21st century” and businesses are developing courses for how to become a data scientist even while job ads appearing on the Web are still wildly disparate in describing the data scientist role.
Some tweets at Friday’s chat took the approach of defining a data scientist according to nuances:
- A data scientist has a specific idea of finding an answer to a problem; a “data enthusiast” just wants to try things.
- A data scientist has to be an analyst who can test data hypotheses in a controlled environment to draw patterns and insight.
- A data scientist produces relevant insight that makes data actionable.
- A data scientist must understand technology, business and the organization’s inventory of available and relevant information sources.
And there was the Silicon Valley spin on it: A data scientist is an analyst that lives in California.
Another member of the #BIWisdom tribe took a resource-based view in defining a data scientist as someone who provides the capability to increase the value of an organization’s information assets — and then added a tweet that this capability currently is rare.
Trust is also currently rare. A tweeter commented that data trust requires breaking down data silos and fostering a collaborative culture. That grabbed the tribe’s attention and led to the comment that “maybe we need Data Scientology, in which organizations relive painful data experiences from the past as a way to assess trust.” This was retweeted 12 times!
Communication is the art of the science role, added a chat participant. A recent Engine Yard blog agrees: “If you are awesome at technology and math but can’t communicate, being a data scientist is probably not for you.”
Big Data seems to require data scientists because the technology lacks maturity. Hence it requires as much art as science. Technology needs to mature to make processes repeatable — that is, more science than art.
The reality is data scientists already exist under different names: DBSs, statisticians and data mining engineers. But the new title gives them great marketability. We clearly don’t have enough data scientists, given the challenges of the technology, and at some point the lines between tech innovation/maturity and available skills will intersect. It will be easier to train a business person and transform that individual into a data scientist than to train a technologist and imbue him with the requisite business skills.
Everyone agrees that data scientists are an integral part of business intelligence. But, as the LinkedIn and Facebook guys who coined the term in 2008 — D.J. Patil, and Jeff Hammerbacher — point out in the recent HBR article, there is “little consensus on where the role fits in an organization” or “how data scientists can add the most value.”
I think one of the wisest observances in our tweetchat Friday was that data scientists may be “the start of a trend towards roles being defined by both biz and IS as a way to start bridging that divide strategically.”
A challenging role indeed! A data scientist needs more than business analysis, data analysis and communications skills. Such an individual also will need a really deep understanding of the company’s business because consequences definitely will arise from the scientist’s queries and insights. It could even lead to completely transforming a company.
Bottom line: People considering becoming a data scientist should make sure they possess the personal attribute of being lionhearted for they will need to present data insights with a strong awareness of what could be the business — and career — outcomes of that information.
Howard Dresner is president, founder and chief research officer at Dresner Advisory Services, LLC, an independent advisory firm. He is one of the foremost thought leaders in Business Intelligence and Performance Management, having coined the term “Business Intelligence” in 1989. He has published two books on the subject, The Performance Management Revolution — Business Results through Insight and Action, and Profiles in Performance — Business Intelligence Journeys and the Roadmap for Change. He hosts a weekly tweetchat (#BIWisdom) on Twitter each Friday. Prior to Dresner Advisory Services, Howard served as chief strategy officer at Hyperion Solutions and was a research fellow at Gartner, where he led its Business Intelligence research practice for 13 years.