The rise of data scientists
How are we going to sift through data that is doubling ten thousand years of recorded history every two days? What tools do we need? How is this information going to flow through marketing organizations to even make use of it?
As with many challenges, it's almost always a people problem.
In October 2012, the Harvard Business Review presented a defining article on this subject with "Data Scientist: The Sexiest Job of the 21st Century," by Thomas H. Davenport and D.J. Patil. In this article, Davenport and Patil describe the emerging role of the data scientist:
"More than anything, what data scientists do is make discoveries while swimming in data. It's their preferred method of navigating the world around them. At ease in the digital realm, they are able to bring structure to large quantities of formless data and make analysis possible. They identify rich datasources, join them with other, potentially incomplete data sources, and clean the resulting set. In a competitive landscape where challenges keep changing and data never stop flowing, data scientists help decision makers shift from ad hoc analysis to an ongoing conversation with data."
The article begins by profiling LinkedIn data scientist Jonathan Goldman, a PhD in physics from Stanford, who, in 2006, was able to identify, collect, merge, scrub, analyze, and build insights that helped change the way LinkedIn successfully engaged and built its user base. Guys like Chittilappilly and Goldman see data the way that chefs see ingredients -- the cake is there, it just needs to get put together.
But marketers haven't traditionally had the deep math, coding, and data skill sets to work this way -- and it takes years of training and experience to be able to get to that point. That's a problem for this industry.
"Many marketers even lack the essential understanding of where that data comes from, its implicit presumptions and dependencies, and how to extrapolate from the quantitative to qualitative or business conclusion," said Daniel Jaye, CEO and founder of data powerhouse Korrelate. "Case in point, most advertisers can analyze a marketing campaign and come up with a conclusion, but if they don't know that the data they analyzed is limited to only a specific subset of users, they may misapply the data or present erroneous results to the chief decision makers."
"In ten years, marketing organizations will have embedded in their planning, strategy, and execution teams people who are comfortable with the entire pedigree of the data they leverage, its curation, and how to dig deep and create business insights and conclusions using SQL or whatever tools we have at that point. They won't necessarily be statisticians, but they'll be familiar with things like causal calculus and the difference between training and scoring in machine learning," Jaye said.
Jim Sterne, founder of the Digital Analytics Association, who turned me on to the Harvard Review article agrees this role is an inevitability:
"With enough data and enough smarts, we are more and more capable of anticipating needs and filling them. What's missing is not the data nor the technology, but the educated, creative, inventive Data Scientists who understand granular data and see the big picture at the same time. If you find one, do not let him or her out of your sight!"
Five action items to data driven nirvana
Not only is aligning your operations and processes around data the right thing for your company, but it's the right thing for you, too. The era of data is here whether you like it or not. You can't just bury your head in the sand like an ostrich and hope disruptive change passes over you like an afternoon storm. Even if you're not "in the data department," unless you're four or five years from retirement, you will need to get comfortable in a data setting or find your career track being leapfrogged by younger and more data-friendly colleagues.
This doesn't mean you have to learn to wrangle databases or code JavaScript applications -- but the time is now to start positioning yourself as an active member of the data community. You will be interfacing with data scientists in the near future on all types of projects and they will be looking to your industry expertise and experience to help them help you.
