Big Data efforts have a problem — There aren’t enough people out there who know how to take advantage of it. Consulting firm McKinsey projects “a need for 1.5 million additional managers and analysts in the United States who can ask the right questions and consume the results of the analysis of Big Data effectively.”
So what makes a good data scientist? The Wall Street Journal asked Hilary Mason, chief scientist for the URL shortening service bit.ly. She described three key characteristics:
They can take a data set and model it mathematically and understand the math required to build those models; they can actually do that, which means they have the engineering skills; they are someone who can find insights and tell stories from their data. That means asking the right questions, and that is usually the hardest piece.
Turning data into usable information is the toughest part of data science. Gathering data and putting it into charts is straightforward enough, but drawing conclusions from it and forging a plan for the future requires real brain power.
Since data science is a relatively new discipline, it isn’t often taught at the university level. So, companies have to home grow their own talent, and that’s not easy. Finding even one person with computer/database smarts who also has a strong business sense is a challenge. Finding 1.5 million of them will be really hard.