The Trellance Data Blog

A Day in the Life of a Data Analytics SVP: Making Use of Your Credit Union’s Data

Posted by Mark Portz on Aug 24, 2017 12:17:42 PM

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If you are a credit union still waiting on the data analytics sidelines, you’re already too late. Data analytics is not a fad – it is a major opportunity for credit unions to gain deeper insights and improve decision making to create a strong and competitive future. However, it is not always clear where credit unions should begin. To help answer these questions, John Best recently spoke with Clay Yearsley, SVP of Data Analytics at Texas Trust Credit Union about getting started on the analytics journey, the skills needed, and the value of data in the podcast, “Catching a Unicorn – Discussing Data Analytics with Clay Yearsley”.

Getting Started with Analytics at your Credit Union:

There is a reason for using the word “journey” to describe the investment in data analytics. A financial institution cannot simply purchase a shiny new computer or software system and immediately have every question answered and every problem solved. As Clay expresses in the podcast while discussing the analytics journey, “It’s a practice that you build, and a data warehouse is a vital piece in that practice.”

Put differently, by John Best, CEO, Best Innovation Group, “Analytics is a discipline, and not a product.”

Ultimately, there are a number of options to consider when beginning the journey.  As stated by Clay Yearsley, “There are a lot of different pathways to take, but the biggest thing to beginning this, is to begin.” To learn more about the various options, and the pros and cons of each, read the “Beginning the Journey: Credit Union Data Analytics” Whitepaper.

Uncovering the Myths and the Value of the Data

Data is an incredibly valuable asset for financial institutions. However, many credit unions are experiencing the problem that data itself is not easily digested, and therefore, is perceived as not being valuable. However, when properly organized and presented, data is very possibly the most valuable asset your organization owns. In fact, as John Best states in the podcast, “Data is money. Period. And for every bit of data that you have, it is worth something to the organization, and will translate to the bottom line.”

A common fear about data analytics is the price. As Clay and John discuss on the podcast, a good data warehouse does not break the bank, and absolutely pays for itself. An efficient data warehouse allows storage of historical data from all source systems enabling valuable insights from trending all member data. On top of that, the data is accessible at any time and frequently updated. As Clay points out, there is value in not “begging” vendors to get data that should be owned by the credit union.

Finally, the value of time and staffing also comes into play in the discussion about starting the data analytics journey. There is a misconception that a data warehouse is a project that eats up all your staff’s time, or oppositely, that the data warehouse will free up so much staff time that certain jobs will no longer even be needed. However, as discussed in the podcast, committing to data analytics does not have to overconsume or replace any employees. It is important to make sure people in the organization know your goal is not to make them expendable, but to help by adding even more value to the team. The goal is to accomplish more with the same amount of staff. A successful analytics project frees up some time that staff may be using to manipulate excel files, when they could be using their brains and strategizing - without impacting their job security. Shifting time from report creation to report analysis, among other ways to better spend time, will create lift within the organization.

The Skills for Credit Union Data Analytics

Even with the right technology, the question remains: What are the skills needed for a SVP of Data Analytics? According to Clay, these are the seven skills he finds most valuable in his role:

  1. Curiosity – In order to gain deeper insights from the data, it is helpful to be curious and as “why” of the data.
  2. Credit Union Knowledge.
  3. Core System Programming Skills.
  4. Suppressing your Ego – In other words, to be successful, you have to understand or discover that you don’t know everything.
  5. SQL skills – Though there are a number of innovations and developments that have reduced and may eliminate the need for most people to learn SQL, it can be very valuable to understand SQL when there is a need to discover the answers to a new business question.
  6. Good spreadsheet skills – While the goal is to ultimately escape the “Excel Hell”, spreadsheets are still seen as a necessary evil in credit union departments around the country, and it is sometimes valuable to know how to manipulate the data in a spreadsheet to get answers.
  7. Design skills – To truly help others understand the value and insight available in the data, it has to be presented in a way that looks more appealing than plain rows and column. A basic eye for design helps get others interested in the data and see the trends more easily.

On the topic of skills required for data analytics, it is important to note that it is not necessary to have every skill in-house.  As Clay mentions in the podcast, he’d love to hire a data scientist, but for nearly all credit unions, it’s simply not realistic to hire someone full time for such a role. Clay notes this isn’t a bad thing, and it’s where, “Credit unions can do what credit unions do best”. Collaboration between credit unions can allow access to multiple data scientists, at a shared/reduced cost, and only on an “as-needed” basis. This means it’s possible for credit unions to tap into superior knowledge and skills, without paying the high costs.

To listen to the entire podcast, visit 

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Topics: Analytic Data Model, Data Analytics, Leadership, Podcast