4 data challenges and the opportunity for credit unions

Credit unions are facing several unique challenges. As an industry, credit unions have fallen behind competing fintech startups and major retail banks. It is vital for financial institutions to understand the problems they are facing and how they are possible to overcome. In 2018, the credit union industry must work together to push past these challenges and remain relevant in the age of digital transformation.

Challenges

• Disparate Data

Credit unions utilize numerous data sources, and each speaks a different language. If data sources are not integrated properly, then the organization does not have a reliable single version of the truth. One system or even department should not report different numbers than another. It is important to trust the data, or else the decisions that are made as a result of the analysis cannot be trusted either.  

To add to this challenge, most credit unions are stuck in “Excel Hell”. Without the proper tools in place for data standardization/analysis, it is common to turn to manual spreadsheets. However, this is incredibly time consuming, prone to errors, and ultimately only provides a static report.

• Outsourcing Analytics

In the credit union industry today, there is a heavy and unhealthy reliance on sending data from the credit union to 3rd party vendors for analysis. The major concerns with this model are security and data ownership/monetization.

A credit union’s member data is extremely valuable, and trusted to be kept safe by members. There is an element of risk any time data is removed from the firewall and sent to a 3rd party. On that note, credit unions must realize the value of data, and learn to monetize it for themselves. By providing this data to 3rd parties, credit unions are allowing these other companies to monetize their data for their own financial gain, not that of the credit union that supplied the data. Often, credit unions even must “buy back” their data from these 3rd parties in the form of a new dashboard or model. However, it is now feasible for even smaller credit unions to own perform such analysis on their own infrastructure, own their data, and monetize member data for the benefit of the member.

• Lack of Resources

Of course, the disparate data and need to outsource analytics may be a result of lacking resources. To tackle advanced analytics effectively, financial institutions are expected to invest in and maintain a scalable enterprise data warehouse solution, a complementary data lake, a business intelligence team comprised of architects, data analysts, data scientists, and more; easily totaling a sum that is out of range for nearly all credit unions. In 2017, a single major bank’s budget, solely for analytics, was $100 Million. How can credit unions keep up with organization committed to analytics with these types of budgets?

• Time to Market

Credit unions are behind in data analytics. Members expect the types of service and experiences provided by companies such as Amazon, Domino’s, and Starbucks. In 10 years, there have been 18 iterations of the iPhone. Apple remains a successful and innovative company because it is willing to disrupt itself, and reinvent its offerings just months after a new model has been released.

As we’ve seen, traditional data warehousing methods in the credit union have been costly, very time consuming, and largely unsuccessful. Even if it is successful, credit unions cannot afford to invest 3+ years in a data initiative. The industry is already behind, and institutions need to move quickly to remain relevant.

Opportunity

With these challenges, how can a credit union keep up and stay relevant to members? After all, even one of these topics could present serious hardships. Fortunately, credit unions have one unique and incredibly powerful advantage: collaboration.

When credit unions collaborate and utilize a standardized data model, everything changes. Together, credit unions have all the resources required to do amazing things with analytics. Credit unions that speak the same language can work together, rather than on their own data islands. Reports that are built on the same standard can be shared across credit unions, dramatically reducing the time and cost of report writing. When, say, 100 credit unions are working together and sharing reports and advanced analytics applications, each participating credit union is essentially equipped with 100x more reports in any given period of time. This collaboration empowers credit unions to maintain ownership of their data while minimizing risk, the need for additional resources, and the need to ship data to 3rd parties. This advantage only continues to grow with passing time and a growing platform.

The fact is, for advanced and predictive analytics, credit unions lack not only the tools and expertise, but also sufficient data. With an industry standard data model, credit unions are no longer limited by their own data. Credit unions have the opportunity to collaborate and pool data. This means pooling data to enable increased confidence in analytic models, and even real-time benchmarking capabilities.

Overcoming any of these challenges has proven to be extremely difficult for any individual credit union. Tackling these and achieving advanced analytics capabilities is not something that can be expected of a single financial institution. However, collectively, credit unions are in a great position to digitally transform and revolutionize the credit union movement.