The term “data scientist” is a somewhat nebulous one, although pundits and analysts generally seem to agree it means someone who understands data-analytics technology and the math behind it, can use that knowledge to solve data problems, and (often) see how that data offers deeper insight into a business process. In a recent discussion on Twitter (#BIWisdom), Dresner Advisory Services president and founder Howard Dresner sparked a discussion about the definition of a data scientist. While those Twitter denizens following that discussion offered a variety of different definitions, Dresner suggested that data scientists walk a thin line between science (i.e., the ability to discover a process related to data analysis and make it repeatable) and art (the ability to take relatively immature technology and make it work in creative and new ways). “The reality is data scientists already exist under different names: DBSs, statisticians and data mining engineers,” he wrote in a follow-up blog posting. “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.” When it comes to data work, he added, training businesspeople in the intricacies of analytical platforms could be easier than training technologists “with the requisite business skills.” But those business skills are necessary: without them, the individual wrestling with the data will have precious little ability to see how the data will ultimately affect their company’s future course. The bottom line, according to Dresner, is that data scientists—or people thinking of becoming data scientists—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.” Data analysis: evidently not a job for the faint of heart. Dresner is well known within the business-intelligence and Big Data community for his predictions and overall feel of the market. His annual Wisdom of Crowds Business Intelligence Market Study offers insights into how organizations are thinking about—and using—data analytics. Image: jannoon028/Shutterstock.com Data Scientists Need Business Experience: Dresner
The term “data scientist” is a somewhat nebulous one, although pundits and analysts generally seem to agree it means someone who understands data-analytics technology and the math behind it, can use that knowledge to solve data problems, and (often) see how that data offers deeper insight into a business process. In a recent discussion on Twitter (#BIWisdom), Dresner Advisory Services president and founder Howard Dresner sparked a discussion about the definition of a data scientist. While those Twitter denizens following that discussion offered a variety of different definitions, Dresner suggested that data scientists walk a thin line between science (i.e., the ability to discover a process related to data analysis and make it repeatable) and art (the ability to take relatively immature technology and make it work in creative and new ways). “The reality is data scientists already exist under different names: DBSs, statisticians and data mining engineers,” he wrote in a follow-up blog posting. “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.” When it comes to data work, he added, training businesspeople in the intricacies of analytical platforms could be easier than training technologists “with the requisite business skills.” But those business skills are necessary: without them, the individual wrestling with the data will have precious little ability to see how the data will ultimately affect their company’s future course. The bottom line, according to Dresner, is that data scientists—or people thinking of becoming data scientists—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.” Data analysis: evidently not a job for the faint of heart. Dresner is well known within the business-intelligence and Big Data community for his predictions and overall feel of the market. His annual Wisdom of Crowds Business Intelligence Market Study offers insights into how organizations are thinking about—and using—data analytics. Image: jannoon028/Shutterstock.com 
