What is a Data Scientist, exactly? We’ve seen the term everywhere recently.
- The Harvard Business Review named Data Scientist “The Sexiest Job of the 21st Century”
- Another article claimed “Data Science Is Dead”
- And although attribution is a little murky, we’ve all seen the quote going around Twitter that
“A data scientist is a statistician who lives in San Francisco”
The title seems to be the trendiest buzzword since big data—implying a mythical hero standing astride the worlds of analytics and software engineering, hair whipping in the wind as s/he rides into the tech sunset—but the position itself has been around for years. A Data Scientist is also known as a Business Analyst, Statistician or Information Technology Manager. Semantics aside, it’s a great job title for anyone who wrangles data for a living.
And if the term “Data Scientist” isn’t enough to prove your smarts, your decision to purse it as a career should solidify it for you, since the role is becoming imperative to the business. According to a 2013 PwC survey on business priorities, a CEO’s number one request is for more information and secondarily, for more intimacy with customers. To get there, they need data, analytics, and a person to help tell the story.
Storyteller, Data Scientist, Data Cleanser?
The trouble with hot-button phrases like “Data Scientist” is that the buzz makes light of a real issue: the mountain—make that planet—of data out there that contains nuggets of information that brands could extract in order to get from information to a desired connection with customers. Especially since the data is disorganized—everything from e-mails to social blog posts, weblogs to Tweets. It’s big, fast and messy.
So how does a Data Scientist handle the sheer volume of information, the new types of data sources that are being created every day, the real-time nature of this data, compliance, and a myriad of other obstacles that affect the speed of engaging and responding to customers? If you’re like most you spend more than 80% of your time wrangling data before analysis—not to mention insight and action—can even begin. At least that’s according to the 2013 Big Data Executive survey by New Vantage Partners.
Data Scientists are sitting on a virtual gold mine of information, but you’re prospecting with tools that aren’t sophisticated enough to handle the job. How can you liberate yourself from data cleansing and accelerate time to insight? Time for a smarter tool kit. See more in our new Taming Social Data SlideShare and come back next week for a discussion about how machine learning can help.