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Data Steward<\/strong> \u2013 this skillset is alive and well in most organizations.\u00a0 Almost everyone has a data warehouse, talks about the ETL process, and has had discussions around the business rules of cleaning up and storing their data. What they should be talking about is how to get the data out more quickly and cleanly. A typical project is 80% data wrangling, so don’t skimp on number or quality here.\u00a0The data steward will use tools such as MongoDB, MySQL, Oracle, and if she\u2019s a superstar, she\u2019ll dabble in Python and web scraping and know the difference between JSON and XML. Maybe you’ll give her a raise can call her a data engineer.<\/p>\n Analytic Explorer<\/strong> \u2013 this skillset is a tough one to find.\u00a0 It requires math, statistics, and modeling along with a healthy dose of creativity and skepticism. This is a person who can spin straw into gold or write tomorrow\u2019s news today.\u00a0 His job is to ask the right questions, explore your data, and distill it down to insights that will support your most critical decisions.\u00a0 He\u2019ll use tools such as TensorFlow, R, MATLAB, ArcInfo, SAS, Tableau, and SPSS.\u00a0 If he\u2019s a superstar, he\u2019ll know all about Reinforcement Learning, Bayes, Optimization, and the difference between precision, accuracy, and skill.\u00a0<\/p>\n Information Artist<\/strong> \u2013 This is the role of a creative analytical.\u00a0 Her goal is to sell the results to the decision-maker.\u00a0 And the lack of emphasis on this skillset is one of the reasons analytics is such a failure (and why Apple is such a success).\u00a0 Edward Tufte \u2013 the godfather of data visualization – speculates that the lack of good data design contributed to both the Columbia and Challenger space shuttle tragedies.\u00a0 Think of this person as being as crucial as your sales force.\u00a0 In fact, that is her job \u2013 to sell the right answer. But she’s a whole lot more than a graphic designer. She gets aggregation, normalization and signal versus noise.\u00a0 And she also get mood, white space, and font kerning.\u00a0Excel and PowerPoint may be her go to tools, although she’s more likely to use Photoshop, Moqups, and D3.\u00a0 If she\u2019s a superstar, she\u2019ll be as comfortable talking about the math behind the visuals as she is talking about the psychology behind her design.<\/p>\n Automator<\/strong> \u2013 If the Explorer finds the path through the dark forest to the fountain of youth, and the visualizer designs a beautiful bottle for the elixir,\u00a0 then the Automator turns that path into an eight-lane highway and builds a factory to bottle that stuff as soon as it comes out of the ground.\u00a0 His job is to operationalize the work of the Explorer and Visualizer.\u00a0 He makes sure that results are timely and fast.\u00a0 He adds scale. He might use traditional coding methods like C# or Java or he might fiddle with JavaScript and D3. Or he might even be the guru of Vue.JS or React.<\/p>\n The Champion<\/strong> \u2013 The champion stands with one foot in the land of \u201cgut feel\u201d, and the other planted firmly in the side of \u201cevidence\u201d.\u00a0 She can speak the language of the geeks, and translate it to that of the battle-hardened general.\u00a0 She believes strongly in data-driven decision making, but also recognizes the value of deep domain experience.\u00a0 She\u2019s tireless in her efforts to sculpt the processes of the organization to support analytics. She aims to harvest the brightest insights from the sharp young analysts and the cleverest hacks from the wily old veterans.\u00a0 Her focus is adoption and impact. If she\u2019s a superstar, she\u2019ll make you believe that this analytics thing was your idea in the first place.<\/p>\n<\/div>\n