The Trellance Data Blog

The old way is Dead: Reporting & Analytics for Credit Unions

Posted by Austin Wentzlaff on Nov 2, 2015 1:00:00 PM

If you are the one responsible for report writing at your credit union or have ever requested a report, your pain is not unheard.  The old way of reporting is broken, and it has been for years. Business intelligence failed to deliver on its promise.  IT departments are overloaded and users are frustrated.  Becoming an analytic driven organization is often unobtainable because the majority of the time is spent gathering and organizing the data.

We are all too familiar with the current report writing process.  A report developer receives a request and opens a tool such as SQL Server Reporting Services (SSRS) to create a report based on the user’s request.  This is highly dependent on proper requirements and often end users don’t do the best job at documenting their requirements.

To create the report, the report developer has to:

  1. Find the database(s)
  2. Search a copious amount of cryptically named tables
  3. Figure out the joins
  4. Write the SQL
  5. Format the report
  6. Iterate a number of times with the user until complete

This current process of reporting and analytics is a time intensive, human error-prone process and it needs to change. Qlik, Birst and Tableau have made significant improvements in the user experience but at the end of the day these new tools are only as good as the underlying data used to create the report. Unfortunately for credit unions, the underlying data is often complex and without any sort of organization. In order to ease this pain, the credit union industry needs a universal semantic layer. 

A sematic layer resides between a credit union’s data warehouse and the end user, but more importantly, it is a business representation of the data warehouse or transactional database. A sematic layers allows the IT department (or end user) to interact with their data without having to know the complexities of their data warehouse or where the data is stored. It uses familiar business terminology to describe the business environment and allows the user to retrieve the exact data that interests them without using the difficult process described above.

Unfortunately for credit unions, developing such a semantic layer takes a significant amount of resources that they can’t afford.  Fortunately for credit unions, however, the industry is built to collaborate.  A single credit union cannot build a semantic layer, but the industry as a whole can.  By working together, the industry can change the way it interacts with data and start becoming data-driven rather than focused on trying to gather, organize and interpret the data.  By taking the complexity out of report writing, credit unions can now focus on what they are set out to do, serve their members better than any other institution can.

In order to obtain a universal sematic layer, the credit union industry needs to start supporting causes that have set out to support it.  Causes such as Credit Union Service Organizations (CUSOs) and the Credit Union Financial Exchange (CUFX).  No vendor has the motive to create a universal sematic layer for credit unions.  CUFX has and it is seeking out CUSOs with the same ideals to support its cause.

“CUFX is providing that semantic layer for integration standards. In real world applications of our standard, credit unions and vendors are seeing reductions in integration cost and faster implementations,” stated Rich Jones, Marketing and Business Development Executive at CUFX

As mentioned earlier, business intelligence has failed to deliver on its promise.  Becoming a data-driven organization is a necessity for credit unions, but they can’t do it alone.  The industry needs to come together in order to compete with the community banks and larger institution, not to mention the new competitors such as Apple, Google, PayPal, and Lending Clubs.  The industry needs to partner with CUFX and CUSOs dedicated to the cause of strengthening the industry through Big Data & Analytics.

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Topics: Reporting and Analytics, Big Data, Semantic Layer, Data Analytics