Turning Data into Dollars in 5 Steps

    Information is money. A lot of information should mean a lot of money. Unfortunately, most organizations are still struggling with the idea of how to turn data into profit. Everywhere you turn you hear about the benefits of Big Data, how it’ll change everything and open a new world of opportunity, yet most companies are sitting on a pile of data they just don’t know how to capitalize. So how do you do it?

    ICC, the business and IT solutions company that correctly predicted the 2014 Oscars and designs collaboration solutions for today’s workforce, has outlined five steps that can help organizations avoid being part of the estimated 70 percent of companies that fail to achieve business value projected from their Big Data initiatives.

    “Any major technology initiative by your organization demands a sound strategy aligned with your business goals,” says Steve Grover, vice president of Business Analytics at ICC. “It is not simply the latest shiny object to be enjoyed for a while, then, tucked away discreetly when it doesn’t fulfill expectations that were never properly defined.”

    In this slideshow, Grover offers some guidance for IT and line-of-business professionals when looking to extract value from their data initiatives.

    How to Monetize Data in Five Steps - slide 1

    Turning Data into Dollars

    Click through for five ways organizations can extract value from their data initiatives, as identified by ICC.

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    Step One: Find the right business case

    Data analytics is all about measuring and monitoring and, based on what you learn, making changes to the way you do business. “If you are not willing to make necessary changes based on what you learn from the data, then you have the wrong business case and you will be wasting your time,” says Grover.

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    Step Two: Assemble the right team

    If you think of analytics as a football team then it requires an owner, a coach, a quarterback and a center. The rest of the team forms around this nucleus. The owner is the primary business sponsor and must be a VP or C-level executive. The coach should be a consultant or firm that specializes in analytics. The quarterback is a line-of-business person who has the expertise to keep the project focused on business outcomes and the center of the line is held by IT. The coach works on a daily basis with the quarterback and the center to produce an effective outcome.

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    Step Three: Get the requirements right

    There are three critical sub-steps to gathering requirements effectively: First, understand the business questions and the metrics (and associated dimensions) required to answer those questions. Second, prototype and validate that the solution will answer the questions to achieve the benefits.  And third, make sure the data required for the production solution is actually being captured by the source system!

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    Step Four: Build incrementally

    The days of being handed $2 million and waiting a year for results are over. New analytic capability should be delivered on an ongoing basis – every three to four months minimum. “If any of your partners suggest otherwise, it may be time to make a change on the team,” says Grover.

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    Step Five: Begin with the end in mind

    Analytics solutions are living processes, the more changes and additions requested of an analytic solution, the healthier it is and the more valuable it becomes. Conversely, if no one is requesting changes, the solution is likely dead – perhaps because no one is using it. “If you begin with the end user in mind, asking ‘Who is going to be using your data?’, ‘Who is going to benefit from it and how?’ the project will be much more successful,” says Grover. “Remember that data is not, in itself, a solution, but a tool that enables the solution.”

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