Reporting – Where are we now?
What is your reporting process to measure KPI’s and to measure productivity within your department? What percent of time is spent on preparing the reports? How much time is used on analyzing the data? How often do you change your reporting requirements? Reporting and measurement can be quite frustrating. Generally, Excel is used in credit unions as the primary reporting tool. While it's a great tool for certain analyses, it is not ideal for reporting enterprise wide. Credit unions are taking notice of the “Big Data” or "Business Intelligence” (BI) themes that are surfacing. One credit union said that, "Not only can we not get the data that we need, we would not know what to do with it". We suggest that credit unions take care of first things first, and that begins with enterprise reporting.
Reporting – Where should we be?
Leading credit unions understand the importance of effective dashboard reporting. Assuming that you have an integrated, single source of truth for all your data (data warehouse), intuitive and drillable dashboards will propel your BI initiatives. A dashboard is an easy to read, often single page, user interface, showing a graphical presentation of the current status (snapshot) and historical trends of an organization’s key performance indicators to enable instantaneous and informed decisions to be made at a glance. Dashboards are developed and architected to be updated automatically, without having to cut and paste data. One example of a “quick win” is to implement dashboards at the branch and MSR/FSR level, including daily, weekly, or monthly goals. Credit unions have reported improved sales efforts and increased sales productivity.
Analytics - Where are we now?
One of the buzz phrases outside of the credit union space is “predictive analytics.” Based on conversations with credit unions, they are still trying to analyze historical data to manage their credit union. So, being predictive may be out of reach. Most credit unions say their biggest obstacle to effective reporting and analysis is getting the data. Data silos are a problem. Some credit unions have developed a so-called data warehouse, but in fact it is a database requiring significant resources to manage and maintain. Data architecture is a key to any BI initiative. Some questions to consider: Why use a data warehouse? What’s the best methodology to use when creating a data warehouse? Should I use a normalized or dimensional approach? What’s the difference between the Kimball and Inmon methodologies? Does the new Tabular Model in SQL Server 2012 change things? What’s the difference between a data warehouse and a data mart? Is there any hardware I can purchase that is optimized for a data warehouse?
Analytics – Where should we be?
Once you have a handle on your historical information, moving to predictive analytics will help your credit union leapfrog the competition. Amazon sells more online than its next 12 largest competitors combined. Why? They use data as a competitive advantage. Once your credit union has established an effective BI platform (data warehouse, dashboard reporting, analytics), you will be able to connect your analytics into your strategic objective and eventually move into predictive analytics. Loan analytics should be a focus of your credit union. Many models used by credit unions attempt to be predictive based on historical data. Joe Breeden of Prescient Models describes in his book, Reinventing Retail Lending Analytics, the algorithms, problem designs, and validation techniques that provide the most robust solutions for these loan portfolio. It also extends this framework to economic capital and loan-level modeling.