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Modern business applications produce ever more relevant and actionable information for decision makers, but in many cases the data sources are fragmented and inconsistent. Despite tremendous advancements at the application layer, nearly all IT initiatives succeed or fail based on the quality and consistency of the underlying data.

CIOs are responsible for making information available to their businesses in a consistent and timely basis, but in most organizations, information management is seen as a delegated set of tasks and is not the CIO’s top priority.

“Key initiatives such as master data management, data virtualization, data quality, data integration and data governance are employed by just a fraction of organizations that should be mastering the science of information management,”

Data scientists are the magicians of the Big Data era. They crunch the data, use mathematical models to analyze it and create narratives or visualizations to explain it, then suggest how to use the information to make decisions.

Rachel Schutt, a senior research scientist at Johnson Research Labs, taught “Introduction to Data Science” last semester at Columbia (its first course with “data science” in the title). She described the data scientist this way: “a hybrid computer scientist software engineer statistician.” And added: “The best tend to be really curious people, thinkers who ask good questions and are O.K. dealing with unstructured situations and trying to find structure in them.”

You cannot accept problems as handed to you in the business environment. Never allow yourself to be the analyst to whom problems are “thrown over the fence.” Engage with the people whose challenges you’re tackling to make sure you’re solving the right problem. Learn the business’s processes and the data that’s generated and saved. Learn how folks are handling the problem now, and what metrics they use (or ignore) to gauge success.

Solve the correct, yet often misrepresented, problem. This is something no mathematical model will ever say to you. No mathematical model can ever say, “Hey, good job formulating this optimization model, but I think you should take a step back and change your business a little instead.” And that leads me to my next point: Learn how to communicate.

In today’s business environment, it is often unacceptable to be skilled at only one thing. Data scientists are expected to be polyglots who understand math, code, and the plain-speak (or sports analogy-ridden speak . . . ugh) of business. And the only way to get good at speaking to other folks, just like the only way to get good at math, is through practice.

The balanced scorecard is one popular methodology for linking strategy, technology, and performance management. Other methodologies, such as applied information economics, combine statistical analysis, portfolio theory, and decision science in order to help firms calculate the economic value of better information. Whether you use a published methodology or develop your own approach in-house, the important point is to make sure your BI activities are keyed to generating real business value, not merely creating pretty, but useless, dashboards and reports.

In the contemporary enterprise, almost everyone will need to have data-science skills of some kind.

“When most people think of a data scientist, they think of a statistician, a guy with ‘analyst’ in his title,’” Hanrahan says. “Or, someone who works in IT and manages the data warehouses. To do these jobs, you certainly needed programming skills; you probably needed advanced statistics skills, or some combination of those skills.”

“At the most basic level, you are a data scientist if you have the analytical skills and the tools to ‘get’ data, manipulate it and make decisions with it,” he says.

How Do I Learn to Ride a Bike if I Don’t Own a Bike? I get it.  A lot of businesses went out and bought the bike.  In fact, some of them bought a bike that would be the envy of a Tour de France cyclist.  Now, they’re trying to learn how to ride it.

Hey, that’s fine.  Smart business people don’t live a linear life.  You get the tools and implement the tools – all while you’re learning the tools. 

There’s no “on button” for Big Data.  You need people who will put their hands into it, manipulate it and find the valuable insights.

SiSense’s survey finds that salaries for data professionals are on the rise across all geographies. The annual earnings of a data professional can range from an average of $55,000 USD for a data analyst to an average of $132,000 for VP Analytics. As many as 61% of the survey respondents reported higher earnings in 2012 compared to 2011, and only 12% reported lower earnings.

Other highlights of the survey findings include: Data professionals are highly educated. 85% of the respondents have some college degree, 39% have a Master’s degree, and 5% are Ph.D.’s. Those with doctoral degrees earn on average 65% more than those with Master’s degrees, who in turn earn 16% more than those with Bachelor’s degrees. On the job experience is even more important than education in determining salary levels. On average, professionals with ten or more years of experience earn 80% more than those with 3 years or less.

At the same time, the survey shows that those with 6 years or less make up as much as 59% of the data profession workforce.