- Data scientists: The top dogs in big data. This role is probably closest to what the McKinsey report calls "deep analytical talent." Some companies are creating high-level management positions for data scientists. Many of these people come out of math or traditional statistics. Some have backgrounds or degrees in artificial intelligence, natural language processing or data management.
- Data architects: Programmers who are good at working with messy data, disparate types of data, undefined data and lots of ambiguity. They may be people with traditional programming or business intelligence backgrounds, and are often familiar with statistics programs. They need the creativity and persistence to be able to harness the data in new ways to create new insights.
- Data visualizers: Technologists who translate analytics into information a business can use. They harness the data and put it in context, in layman's language, exploring what the data means and how it will impact the company. They need to be able to understand and communicate with all parts of the business, including C-level executives.
- Data change agents: People who drive changes in internal operations and processes based on data analytics. They may come from a Six Sigma background, but also have the communications skills to translate jargon into terms others can understand.
- Data engineer/operators: The designers, builders and managers of the big data infrastructure. They develop the architecture that helps analyze and supply data in the way the business needs, and make sure systems are performing smoothly.
"The people who do the best are those that have an intense curiosity," says Patil, whom Forbes magazine credited, along with Cloudera founder Jeff Hammerbacher, with inventing the term data scientist. Previously Patil worked at LinkedIn -- his titles included head of data products, chief scientist and chief security officer -- helping develop that company's data science team and strategy.
Patil has a Ph.D. in applied mathematics. Sacheti has a Ph.D. in agricultural and resource economics. And yet, the qualities of curiosity and creativity matter more than the level and type of academic credential, Patil says. "These are people who fit at the intersection of multiple domains," he says. "They have to take ideas from one field and apply them to another field, and they have to be comfortable with ambiguity."
The people who do the best [in big data] are those that have an intense curiosity. D.J. Patil, Greylock Partners