The Decision Maker

5 Steps for Making Analytics Work

Posted by Austin Wentzlaff on Feb 16, 2015, 11:29:00 AM

Credit Union Big Data Analytics Business Intelligence

Analytics is top-of-mind for many credit union executives. Yet, as with all new technologies, there is a concern that it won’t work.  The concern is well justified.  There are many technologies that promise to make organizations more successful but fail to yield much for the company besides higher cost every month. 

The failure of these technologies isn’t always the fault of the technology itself or the company providing the technology. Rather, it is the failure to properly integrate the new technology into the organization.  In the case of analytics, there are several factors that will make or break the technology.  Here are 5 factors to consider when implementing analytics:

  1. Integrate Analytics Across the Organization

Some credit unions look toward analytics to solve only one of their pain points.  A great example is the implementation of Loan Application Analytics.  Although this provides great value, very few analytics providers are focused on only one business area.  So, while the lending department might be benefiting from analytics, the company itself is actually paying for loan application analytics, marketing analytics, financial reporting analytics, and so on.  If a credit union wants to successfully implement analytics, it needs to be expanded into all areas within the business. In this way the credit union becomes more of a data-driven organization which makes it more of an Analytics Competitor.

  1. Use a Variety of Analytics Tools

There are a considerable amount of analytics tools out there, each with their own niche.  Some are very sophisticated and require a well-trained analytics “power user” while others are designed for executives to easily digest the data.  Because of this, there is no “one size fits all” analytics tool.  Credit unions dedicated to integrating analytics across their organization need to consider the diversity of end users and obtain several different analytics tools.

  1. Secure Executive Support

As with all new technologies, executive support is crucial.  Without executive support, the implementation of analytics will quickly come to an end because management will focus solely on cost, rather than value.  Analytics needs to be approached both top down and bottom up.  The end users need to be able to communicate the importance of becoming a data-driven organization and the value it adds to the ongoing success of the enterprise. 

  1. Hire and Develop the Right Analytical Talent

The right talent within an organization is essential.  In the case of analytics, that goes for both those who possess the technological knowledge to create reports and analytics and also those who are reports and analytics consumers.  A true analytics-driven organization cannot survive if those who are intended to use the analytics are not properly trained to do so.  This does not mean that a credit union should hire all new talent but they will need to work on developing existing talent to become more data-driven.

  1. Focus on the Underlying Data

Without clean, integrated data, the answers derived from analytics tools cannot be trusted.  While many credit unions understand the importance of analytics, they fail to appreciate the importance of correct and complete data underneath those analytics. 

The first step in an analytics strategy is the creation of a “single source of truth.”  For credit unions with many disparate data sources (e.g. – core, credit cards, loan origination, etc.), the only way to do this is through the creation of a data warehouse.  Without a data warehouse, it is extremely difficult to test the integrity of the analytics tool’s output.  In many cases, analytics fails at credit unions because they implement analytics  tools first and consider data integrity and integration when it’s too late.

The Analytics-Driven Credit Union

Analytics is becoming increasing more important within the financial services industry.  Many other industries have been disrupted by the shift towards analytics, such as Amazon in retail and Uber in the taxi industry.  The financial services industry is on the cusp of this shift as new data-driven competitors enter the market place riding the wave of new innovations.  A prime example is Apple taking a chunk of the dwindling interchange revenue from credit cards with their Apple Pay

Those that embrace analytics will have the greatest chance of survival in this ever-changing industry. Credit unions need to fully commit to the implementation of analytics to really make it work.

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Topics: Big Data, Data Integration, Data Integrity, Analytic Data Model, Data Analytics