The Decision Maker

Planning for Big Data

Posted by Aaron Wang on Jun 3, 2013 10:00:29 AM

Credit unions interested in launching Big Data initiatives frequently don’t know where to start. This problem was highlighted in a recent McKinsey Quarterly article which prescribed a simple solution: planning.

“The answer, simply put, is to develop a plan. Literally. It may sound obvious, but in our experience, the missing step for most companies is spending the time required to create a simple plan for how data, analytics, frontline tools, and people come together to create business value.”

The three pillars of the plan are data, analytic models, and tools.

Data

The plan starts with an evaluation of available data and prioritizing what data is most important for running the business. Important data sources are often physically isolated from each other in “silos”. The plan must highlight silos that will deliver the most impact if they are integrated.

Analytic Models

Integrating the data is just the first step. The next step is to decide what variables will be analyzed. Since there are usually many variables, organizing them into models is helpful. In this sense, a model is simply a description of a cause and effect relationship.

For example, members who have loans at other financial institutions are prime candidates for a loan recapture program. By combining integrated internal data with data purchased from a credit rating agency, these members can be identified and the most likely candidates for such program can be sent offers.

Tools

Planning for tool acquisition and implementation is the final component. Tools must not only have the power to handle the selected analytic models but they must also be easy to use by a wide variety people in the organization. Many Big Data efforts fail to reach their potential because only power users are able to leverage an overly-complex toolset.

In the course of planning these three crucial elements, the authors stress some important caveats.

Be prepared to make cost-benefit trade-offs. The cost of integrating all organizational data would probably be prohibitive for most credit unions. The senior team needs to take care to balance the potential strategic benefits of the Big Data initiative with cost. Defining and prioritizing high-value information over the “nice to know” variety is critical.

Never overlook the need for user acceptance. Rigorous stakeholder analysis to identify and engage the right people is effort well-spent. All too often, a seemingly great project fails because users of the new system were not brought into the planning process.

Mere training is not enough. The evolution to a data-driven culture demands more from an organization than a simple design-build-train process. Top to bottom, the organization needs to understand the value of data in everyday decision making.

From the start, senior management must be able to clearly articulate the vision to the organization and provide multiple avenues of support to give each contributor the best opportunity to learn, change, and grow.

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