Understanding the How and Why of Credit Union Data Governance

We’ve all undoubtedly heard the call and read the articles about the, in no uncertain terms, dire need for data governance, but rarely does anyone address “why,” so let’s start there. The banks, fintech disruptors and even the credit union across town with its recently expanded charter are all putting pressure on your credit union – pressure to increase dividends and service quality, while at the same time cutting interest rates and expenses, and pressure to improve product offerings to better meet the needs of consumers.

This new world of competitors, and even now the cross-town rivals, employ a secret weapon in their relentless pursuit of wallet share: Data. And not just data but actionable data, which allows for better, faster decision making and process automation. Data governance is the discipline that allows your credit union to use data to meet your members’ needs at scale, make smarter decisions and grow efficiently. Simply put, data governance is the critical fuel for the financial success of your membership and the future of your credit union. A future governed by data – that is the “why” of data governance.

In a November 2019 article, we addressed a principled-based approach for data governance. In this article, we will address more of the tactical “how” of data governance.

Success starts with culture, and data governance without a culture to support it will most likely fail. One of the key factors is the influence and role of leadership. Leadership must embrace and believe in an intelligent, data-driven future – a future that is only afforded by data governance. There must be recognition and an agreement across leadership on the importance of data governance and its ability to allow the credit union to service its members better. Without this agreement, data governance will be dismissed, underfunded and possibly undermined. This effort should be chartered as a program and signed off on by the leadership.

However, leadership alone cannot change a culture – there must be an organizational conviction behind it. This means a credit union serious about data needs to view it as a cultural change. Leadership may exemplify the desired culture, but it also needs to put conscious effort into cultivating the culture, which means engaging HR and influencers throughout the organization to embrace data as a cultural shift. It also means making a concerted effort throughout the credit union, with the organization as a “we” making the cultural shift to becoming a data-driven organization, for which data governance is a key requirement.

Once there is agreement among leadership, there must be an appointment of one person to lead a program for data governance. For purposes of this article, we’ll call this person the Data Czar. This cannot be a committee or multiple people, as that will dilute accountability and velocity. The success of this program must also be a consideration on the Data Czar’s performance review. Not taking this important measure will allow for ambiguity of priority and leave open the opportunity for revisionism.

Ideally, this person would be appointed by the CEO and report on progress to the leadership team on a bi-monthly basis. This ensures the person entrusted with data governance has the appropriate authority to request resources, empowerment in decision making and accountability for the result. In considering this Data Czar, consider someone from the business side of the organization. While a person from IT is an obvious choice, a person from the business side often understands the context of the data better, which will be key in decision making and establishing priorities.

It is important to note that this person must have the time. Often people will be selected without reducing their other commitments. When this happens, often the daily operational demands will overrule the progress. This phenomenon is common and will most likely happen without making the necessary responsibility adjustments. The Data Czar must also have leadership qualities to positively influence others and change an organization’s culture.

With controls put in place to effect change, leadership next needs to identify a system to be the single source of truth. Organizations will frequently encounter 40-100 information systems in a credit union’s ecosystem. Often these systems will present differing data, which can be gamed to present conflicting accounts of a credit union’s health and performance. To put an end to this, the organization must rally around one system where the data is always accurate. This will create a very easily observable check on the progress of the data governance program and stop differing views of the truth in an organization.

Establishing a single source of truth will not be easy and in most cases, will be an outright failure. This early failure is largely due to data that is of poor quality or even flat-out wrong. However, with an earnest commitment from leadership on the importance of the data, and the demonstration of how the credit union’s success is directly intertwined, everyone from the organization can work together to improve this system. With the entire organization understanding the value of the system’s accuracy, and a proper channel for reporting issues, the Data Czar can complete the forensics and identify any issues before they lead to bad decisions. Reasons for bad data entering the system can range from poor controls on the input, to bad translations or transformations from other systems, to inconsistent calculations and definitions.

As part of this data journey to good data governance, the Data Czar will need to work with other departments to establish data engagement rules. These rules start simple with basic definitions (how does the entire organization define an engaged member, profitable member or any other commonly used terms?). This is critical for the next step: Establishing the agreed-upon calculations and equations for key metrics and all derived values. These can vary wildly between systems, departments or even people inside a department. If there is no agreed-upon specification, these calculations alone can account for many of the most confounding and hard-to-troubleshoot data issues. Lastly, there must be an agreed-upon schedule of how and when systems move data in the credit union’s ecosystem.

Finally, perhaps the least interesting step on the journey to good data governance is completing documentation and training. Documentation must have all the requisite technology diagrams like data flow, but it also must include the artifacts of all the previously listed considerations. It is a best practice to place this documentation in a living document like a wiki that can be accessed by the entire credit union staff. This creates accountability and ensures the documentation stays up to date.

Saving this documentation to a Word document somewhere on network storage almost guarantees it will always be out of date and of low value. This goes together with training employees. Training, not only on the technical “how” and “where” of the new system, but also in the new vocabulary and nomenclature, is critical. Survival of data governance demands a mutual understanding of data sources and derivations supported by a common language. Without this commonality, a credit union can quickly fall back into old habits of misunderstandings, miscommunication, bad data and even worse decisions.

In our previous data governance article on principles, we accentuated that good, quality data is a journey. This journey that an organization, credit union or otherwise, takes is never truly complete, as no organization has “perfect” data. However, starting on this journey with a commitment to data governance and making it part of the culture will allow a credit union leader to compete in this age where data is the new oil. Organizations that choose not to take this journey will increasingly find margins shrinking tighter and tighter until they can no longer service their membership.

Ray K. Ragan, PMP is the co-founder of Clear Core, a data cleaning and transformation provider focusing on increasing the value and accessibility of data for financial institutions, in Tucson, Ariz.

Timothy “Buck” Strasser is the founder of Clear Core in Tucson, Ariz.