Jun 15 '22

Value at the Intersection of People, Process and Technology

One credit union shows how prioritizing a data-driven culture starting at the top with leadership and being infused across their organization helped foster the adoption and acceptance of a robust data analytics ecosystem.

It is not enough for a credit union to simply have robust data analytics capabilities. In order to discover untapped business value through use of data analytics, credit union leaders need to build a data-driven culture, one in which the technology is infused into your employees’ daily work and decision-making processes. One in which employees have inherent curiosity and growth mindsets. One in which data drives every single decision.

The Path to a Data-Driven Organization

For credit unions, the ability to analyze data—particularly predictive data analytics or using data to forecast future outcomes—is a critical component of a successful digital transformation. But the path to becoming a data driven organization is a difficult one.

While 92% of surveyed Fortune 1000 companies said they had invested in the technology to leverage data analytics capabilities, only 29% are seeing results, and even fewer would describe themselves as “data-driven.”

In order to assess analytics readiness and how readiness results in successful analytics adoption and infusion throughout an organization, Filene engaged with Kinecta Federal Credit Union on a special research project to understand how they’ve successfully built a robust data analytics ecosystem.

Kinecta began its analytics journey using the “business intelligence” label in 2014. Today, Kinecta has a dedicated staff of six on their data analytics team and 40 unique data systems (internal and external) integrating together to process over 380 million data records each day.

Bhavesh Shah, Vice President, Data Management Strategy, joined Kinecta roughly two years after this project began. He walked into what most credit unions have, a functioning data warehouse. While incredibly important in laying the foundation for data, it was time to begin pulling and creating value from this data and enhance a culture that embraced it fully.

Infusing Data Across an Organization

For most credit unions, the investment in, and prioritization of, specific analytics projects are designed to achieve operational outcomes such as member growth or deepening member relationships. As Kinecta’s program grew, it became apparent that to build the robust program they envisioned, they would have to prioritize a data-driven culture to foster the adoption and acceptance of using data analytics, starting at the top with leadership and working all the way down and across the organization.

92% of organizations report that the primary inhibitor to achieving data analytics success was organization culture.

Filene Fellow for the Center for Data Analytics, Cheri Speier-Pero, explains, “organizations that have a strong leadership culture of being a data-driven organization, and expecting all associates and employees to have the capacity or the appreciation for analyzing data in order to make recommendations and push decision outcomes forward—that’s the night and day change where analytics can really drive successful performance.”

Bhavesh shared that this is one area where Kinecta had an advantage–strong leadership. Keith Sultemeier, CEO at Kinecta, not only recognizes how essential data is to their organization’s success but also encourages the research collaboration with Filene to ensure the best course to move their program forward.

As CEO, Keith’s role became clear to act as a change agent for infusing data analytics across Kinecta. “You will not derive value from data until you successfully marry technology with strategy, invest in people and culture, and connect your business intelligence infrastructures with organizational process,” he says.

It’s Not Technology. It’s the People.

While leadership’s commitment is critical for the long-term success and investment decisions, building a data-driven culture is still multi-faceted. Once leadership is bought in, the next step is to build an inquisitive and curious mindset across the organization.

Kinecta initially identified 35 “power users” across their organization to get trained on data analytics tools and help them use data to answer the questions most relevant to their business unit.

“Creating that buy-in from across the organization is critical and sometimes that means helping to translate the data in a language your colleagues understand, and by that I mean, how data relates directly to their work,“ Bhavesh said. “We have been able to use data to answer important questions that are top of mind for our business units, like the top factors why people are leaving the credit union—and then identifying campaign opportunities based on winning data points to overcome that challenge.”

Building Your Data Analytics Ecosystem

Building your ecosystem takes time and it will be an ongoing process, but Kinecta has broken down the five strategies that will help you build your own data analytics ecosystem.

  • Invest in human capital and training—not after the plan is built, but while you are building your ecosystem. Identify and nurture “power users,” ensure data and business unit employees collaborate, create training modules for staff.
  • Ground business decisions in data. In order to generate value from data analytics, credit union leaders must build a data-driven culture in which analytics are infused into decision-making processes. Every decision, every time.
  • Be clear on each budling block and how you are going to reach the next steps. Keep the big picture in mind, quantify success along the way and tie this into an action plan that uses data to address strategic business initiatives.
  • Get commitment and buy-in from your leadership team. Strong leadership must commit to shaping a data-informed culture. Encourage employees to experiment and learn with a framework around data governance and management.
  • Develop a long-term strategy before work begins. Don’t start working on enhancing your data just because others are doing it. Understand the business needs.