• 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 they're often familiar with statistics. They need the creativity and persistence to be able to harness 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 they also need the communication skills to translate jargon into terms others can understand.
• Data engineers/operators: The designers, builders and managers of the big data infrastructure. They develop the architecture that helps analyze and process data in the way the business needs it. And they make sure those systems are performing smoothly.
"The people who do the best are those that have an intense curiosity," says D.J. Patil, data scientist in residence at Greylock Partners. Patil probably knows what he's talking about: Forbes magazine credits him and Cloudera founder Jeff Hammerbacher with coining the term data scientist. And earlier in his career, Patil helped develop the data science team and strategy at LinkedIn.
- Tam Harbert
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