Eight years ago, Grow Financial identified a need for better data maintenance and management.
The credit unions analytics department has evolved from a three-person cleanup crew to a two-team, cross-organizational resource.
Nearly a decade ago, Grow Financial Federal Credit Union ($2.5B, Tampa, FL) faced a problem to which many other credit unions can relate. Each department within the organization collected large volumes of member data, which helped the credit union make more informed decisions in the moment. However, this decentralized data structure created internal challenges when querying.
CU QUICK FACTS
Grow Financial FCU
Data as of 12.31.18
HQ: Tampa, FL
12-MO SHARE GROWTH: 3.3%
12-MO LOAN GROWTH: 6.1%
We were getting different answers to the same questions depending on who asked it, how it was asked, and who they asked, says Emily Nichols, the credit unions vice president of analytic services.
To solve these issues, Grow needed to centralize its data reporting to answer questions and make better business decisions. In August 2011, Grow started its journey toward centralization. At the time, responsibility for data reporting fell under IT. But because the scope of the project was so large, Grows CFO asked Nichols to own the transition herself under a soon-to-be-formed analytics department.
For two months, Nichols interviewed business owners across the organization to understand the organizations appetite for data. How did business owners query data? How did they use it? What data did they need but not have?
It helped me identify two things, Nichols says. One, where to start. And two, how many people I needed, and the skill sets I required.
She also learned which departments had the largest data needs: consumer lending and collections. In fact, one particular report for these teams consisted of so much data that it took them two days to build it.
In January 2012, Grow founded its analytics department with Nichols at the head and two analysts reporting to her. With this foundation set, Grows data analytics and reporting transition was ready to begin.
The Startup Process
When Grow officially launched its analytics department in January 2012, its focus was limited.
Rather than casting a wide net across the organizations various data sets housed within different departments, which risked overwhelming the team with too much, too soon, the analytics team started by digging through core data to check for accuracy and develop standard definitions for reporting.
It was at this step that Grow uncovered the answer to an ongoing loan origination conundrum. The lending and finance departments had been reporting inconsistent information regarding the total number of loan originations at the credit union. Turns out, neither lending nor finance was reporting the correct number. Understanding why was an important charge for the fledgling analytics team.
The Right Name Every Time
Emily Nichols, vice president of analytic services at Grow Financial says setting and sticking to shared data definitions helped the credit unions analytics program succeed.
Everyone has a different definition of the same field, Nichols says. There can be five or six definitions of the same term.
She suggests two best practices to align leaders across the organization.
- Bring together business leaders from the beginning.
- Lay out definitions and what they mean.
As it quickly found out, data was stored in ways that werent always straightforward or logical, at least to Nichols and her team.
Uncovering and understanding the business logic was one of the largest hurdles the analytics team had to overcome in cleaning the data. For instance, if someone wanted to know how many loans the credit union opened on a specific date, searching by Open Date might have made sense. But it wasnt correct. Because of nuances in the system, the Open Date was liable to change for a variety of reasons and wasnt necessarily the date the loan originated. So, the team has now clarified that the Original Date is the date of origination.
Nearly 12 months into its core data cleanup, Grow decided it needed to broaden its scope. The credit union had an immense amount of data collected from outside its core processor. By marrying additional sources of data into once centralized repository ARCU by Jack Henry Grow found it could more efficiently and easily manipulate data for reporting, which it visualizes using Tableau.
Grow has 15 data sources in its depository today, and it has designs to capture more in the future. At the top of the wish list is better member psychographic data, Nichols says. She also sees the need for fraud system data. Overall, she wants the breadth of data to serve a singular purpose.
Our goal is to be able to understand a member from all the touch points they use to interact with us, she says. If somebody responds to us via Facebook or sends us an email, how can we tie that interaction together with the products and services they have with us?
A High-Level Evolution
The goals of Grows analytics team have evolved since 2012, becoming more high level in nature. To meet these goals, the department has grown in number and split in scope.
Grows analytics department now counts eight analysts, plus Nichols, across two teams.
One team is focused on operational analytics. Through portfolio analysis and trend analysis, these employees help business units understand how their business is performing and set short-term goals. Nichols is working to embed several of these employees within specific business units so the analysts can better understand a department and its data and be immediately available to answer questions.
The second team is focused on organizational strategy. These analysts mine data to help senior staff unearth and analyze market and macro-economic opportunities and trends. Grow doesnt employ a data scientist, per se, but an analyst within this group does analyze large pools of data and pick out significant points of interest for the credit unions decision-makers.
Grow introduced this second analytics team less than 12 months ago, and the credit union is still learning how to best deploy the talent. However, it is clear the group will keep the relevancy of the credit union at the forefront of its operations.
Deploying two teams to meet the organizations analytics needs is another step in an iterative process, Nichols says, and the department will continue to evolve in the coming years. As an organization, Grow has a continuous need for data, and the analytics department will be the center of it all by supplying real-time analysis to decision-makers and researching long-term strategic directives.
Financial services is changing, and expanding the role of its analytics department is helping Grow keep up.
Without someone focused on the future we can get bogged down in the day-to-day, Nichols says. When that happens, we lose the ability to identify opportunities in time.
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