The Decision Maker

The Perception Problem

Posted by Aaron Wang on Sep 14, 2013 6:18:15 PM

“We don’t have enough data to make good decisions! “ Have you heard a similar complaint at your office? It may be a valid frustration but the root cause of the problem is off the mark.

The details of the problem are nicely captured in an article by Lawrence Buettner of Wausau Financial Systems. The following are an interpretation of his conclusions as they apply to community financial institutions (CFIs):

  1. CFIs are awash in data but they are unable to use it effectively
  2. They know they need help with these data problems but don’t know where to begin
  3. They perceive a difficulty in balancing the transaction needs of customers and the analytical needs of internal management

The first point breaks down into two parts:

  1. Data is disconnected in separate “silos”.
  2. There is a lack of easy-to-use analytical and data visualization tools.

These two issues are closely related. For example, a CFI might acquire a cutting edge data visualization tool. Yet, if the underlying data is not properly structured, the power of the tool is wasted. Conversely, if the data is staged in a state-of-the-art data warehouse but the proper analytical tools are not used, the full potential of the data is not realized.

Regarding the second point, the best place to begin is with the data itself. CFIs need to take an inventory of available data sources and prioritize them in terms of strategic importance. Many CFIs find that data from their core processor is the most critical. Organizing this data for analytical purposes is often the most effective place to start. The core data can be organized to allow easy integration with ancillary systems in future projects.

The final point of needing to balance operational transaction requirements with analytical needs is a false perception. In fact, the two are entirely complementary. Operational transaction systems are properly oriented toward processing efficiency. Staging this data for powerful analytics involves making a copy of this data and storing the copy in a separate database. The copy is then re-organized to be optimal for analytics. The copying process is engineered to cause minimal impact on transaction processing so CFIs can have both efficiency in daily operations while building an analytical powerhouse.

Community financial institutions definitely face serious issues in using their data effectively. The bad news is that many CFIs perceive these issues are beyond their ability to solve. The good news is that technology innovations have increased the options for CFIs so they can plan and implement effective and affordable solutions.

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Topics: Reporting and Analytics, Credit Unions, Data Integration, Data Analytics