The Decision Maker

All of the Data

Posted by Aaron Wang on Apr 21, 2013, 9:34:29 AM

Credit unions often dismiss Big Data as something suitable only for Big companies with Big Budgets as I mentioned in my last blog.

However, “Big” does not necessarily mean absolute database size. “Comprehensive” may be a better definition. A fundamental Big Data concept is an organization (of any size) with access to ALL its data has the opportunity better and faster decisions.

In the past, traditional data sampling techniques were employed to provide the factual grounding for decisions. Using a randomly selected subset of data, a statistical model was constructed to represent reality. This method was followed because it was usually not possible to analyze the complete population of data.

Technology has evolved to the point now where all data can be made available for analysis. This has dramatically expanded the range and speed of analytical options. Analysts now have the means for finding relationships in the data that easily overlooked in the past. By applying widely available hardware and software solutions to these comprehensive datasets, organizations can find innovative ways to reach their goals.

A frequent sticking point for many credit unions is, however, the simple mechanics of accessing all the data. Creating comprehensive datasets from the many data silos in the organization seems like an overwhelming task. Big Data aspirations can easily fizzle out if a credit union perceives the data integration task is too costly and time-consuming.

The good news is data integration technology has also evolved. Affordable solutions are available to ease the effort involved in breaking down the silos. Also, the effort can start small and build up over time.

Here are three steps to get started:

  1. Pick the low-hanging fruit: Choose one or two systems for the first data integration project that apply to high value initiatives in the credit union. For example, core processor and credit card data could be combined to create an “increase wallet share” program.
  2. Build internal support: There are an increasing number of articles in the business press about Big Data. Generate excitement within the organization by relating this information to the proposed data integration project.
  3. Conduct an IT Readiness Assessment: Work with the Information Technology area to assess the state of hardware and software readiness for an integration effort. Some low cost fixes could pave the way for a streamlined implementation.

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Topics: Data Analytics