The Trellance Data Blog

Lagging Contenders: How Credit Unions Can Catch Up in Data and Analytics - Part 2

Posted by Peter Keers, PMP on Aug 15, 2017 11:16:00 AM

This is Part 2 of 2 in a blog series on how credit unions can catch up in data and analytics. In Part 1, we discussed which questions credit unions need to be asking to get off the bench, the issue with data silos, and what it will take to move forward with data analytics. In Part 2, we will further discuss the concept of big data, staffing for data analytics, and creating value from the data.

"A recent McKinsey & Company report emphasizes the fact that many industries are achieving only a fraction of their “digital potential”. However, the report observes, “In the United States, the information and communications technology sector, media, financial services, and professional services are surging ahead…”. This means other players in the marketplace served by credit unions have a big head start.

Credit unions that have been sitting on the sidelines can wait no longer. To get off the bench, these organizations need to ask:

  • What are the basic questions about the organization’s strategic direction that cannot be answered today?
  • How can existing data be better “generated, collected, and organized”?
  • What data outside the organization would be useful?
  • What skillsets are missing internally and to what degree can they (or should they) be outsourced?
  • Once “insights” are uncovered from analytics, what are the practical steps to leveraging them to create value?"


Outside Chance

Pioneering credit unions that adopted data and analytics innovation years ago had a big advantage over credit unions that are just starting now. The early adopters only had to focus on their internal data. Over the years, however, large volumes of valuable data outside the credit union have become more accessible. “Big Data” has exploded onto the scene.

For credit unions, Big Data takes two forms.

  • Structured - External data that is neatly arrayed in rows and columns but comes in very large volumes. Examples are census data and real estate price databases like Zillow. Associating this data with individual members is, in some cases simple, but in other cases, more demanding. When the link is made, however, members can be viewed in an increasingly broad perspective that could highlight otherwise unknown opportunities. For example, Zillow real estate values can uncover mortgage refinancing or HELOC opportunities when linked to internal member loan data.

Also in the structured category are “pools” of data to which credit unions voluntarily contribute. For example, loan data that has been scrubbed of personally identifying information is added to the pool by many credit unions and can be used for such purposes as meeting the forthcoming CECL regulations.

  • Unstructured – Social media sites like Facebook, Instagram, and Reddit generate an immense volume of data that, when properly tapped, can reveal amazing insights very quickly. While census data has a lag time from collection to publication, social media data is immediate. Credit unions successfully using such data could quickly spot attitudinal or behavior movements among members and respond before positive opportunity fades away or negative trends get out of hand.

“Beginner” credit unions need not jump into Big Data scene right away. However, they should be prepared to be “fast followers” because there will be competitive pressure to do so.

Power from the People  

Credit unions about to embark on the data and analytics journey are aware of a tough reality: there is very little, if any expertise internally in this subject area. Even an otherwise top-notch credit union IT staff rarely has data and analytics specialists. These skillsets must be acquired by existing personnel (a long process), hiring these skillsets (high demand, short supply, expensive), or outsourcing.

The outsourcing option has a lot of advantages for credit unions. There are two major ones. First, the number of firms offering data and analytics services has blossomed over the past few years. There is a wide array of products and services from which a credit union can implement its data and analytics strategy.

Second, many of these vendors have gained valuable experience in the credit union space over the past few years. Their ability to deliver valuable services at a reasonable price point improves every day driven by the forces of increasing efficiency and competitive pressure.

Choosing a data and analytics vendor will be greatly facilitated by the data inventory done in the previously mentioned silo analysis. Credit unions with a clear idea of what they are trying to accomplish will be much better prepared to assess vendor delivery capabilities.

A final note getting involved with a vendor is that it does need to be forever. A long-range plan to develop internal capabilities with teaching and mentoring provided by a vendor is a great way to gain greater control over the credit union’s data and analytics program.

Getting to the Value

After all this work, there better be a payoff. Unfortunately, some pioneering credit unions found out that an attitude of “build it and they will come” does not work. Just as there needs to be an inventory of internal data, there also needs to be a people inventory. This second inventory extends not just to IT but to the entire organization. A data and analytics culture needs to be developed at all levels of the organization. In many ways, this is the most difficult task. Becoming a data-driven organization is a long-term proposition. However, there are practical steps to make this a reality.

  • Work with a Data and Analytics Strategy Consultant – A great way to get a solid start is to engage an experienced consultant to help craft the comprehensive plan. This ensures that no major area is missed and important details are addressed
  • Look for Small but Valuable Wins – Cherry pick some easy wins for the first projects. Even though these may be modest in terms of overall impact, results come in faster, hearts and minds will be won over by “wins”, and any hiccups with the program can be quickly uncovered and remediated.
  • Enlist Tech-Savvy Workers as Evangelists - Look for employees who “get it” and are more enthusiastic than the average person about the potential for technology to accomplish great things. Younger workers aren’t the only ones who fit this description. Look also for more experienced workers who can be big influencers among their peers.


 The McKinsey & Company report also includes a lot of very “bleeding edge” ideas about the artificial intelligence and robotics impacting the digital potential of organizations. However, while credit unions starting their data and analytics efforts must be aware of these important trends, sticking to the basics and getting started now is critical for long-term viability.

This is Part 2 of a blog series. Click here to read Part 1. Also, don't forget to subscribe to our blog to stay up-to-date on the latest in data analytics for credit unions.

Stay up to date - Subscribe to our educational blog

Topics: Big Data, Data, Digital