If data isn’t governed properly, your credit union could be at risk. Consider these best practices for a well-managed data governance strategy.

The foundation for a successful analytics program is data. Without accurate, quality data, it’s nearly impossible to make informed business decisions. As data becomes an even greater asset for the credit union, the ability to monitor, manage and control data’s integrity becomes even more mission-critical. Establishing a data governance strategy and data quality procedures within the credit union ensures dependable data flows throughout the credit union.

The lack of data governance within the credit union can have serious impacts within every area of the organization. The Data Warehouse Institute estimates that data quality problems cost U.S. businesses more than $600 billion a year.(1) In most credit unions, the volumes of data have multiplied over time and the data governance structure has not kept pace with business change or the credit union’s growth. Many credit unions also rely on legacy systems that cannot be updated or integrated with new sources of data.

In my last piece, I covered why data governance is critical to the credit union’s analytic strategy. Now, we’ll share some best practices on how to create a data governance program through a combination of data committees, corporate policies, and accountability.

Establish a Data Governance Committee: Data governance is not just an IT issue – everyone in the credit union is responsible for maintaining accurate, accessible, and secure data to support the credit union’s business objectives. With the help of dedicated employees, create a trusted data governance committee that is tasked with establishing data review processes and data quality standards. The data governance committee will ensure responsibility, accountability and sustainability of data best practices. They will oversee the preservation, availability, security, confidentiality and usability of the credit union’s data. The data governance committee can be a powerful force for setting the tone for data quality within the credit union and for establishing the internal top-down support to ensure that employees are properly educated and trained on data literacy and how the data is collected and then used.

Develop and Enforce Policies and Procedures: Creating an ongoing set of rules, policies and procedures for collecting and managing data ensures that the credit union’s data strategy and business strategy are aligned. Policies help prevent employees from violating data quality guidelines while helping the credit union meet regulatory requirements. Such policies must include a comprehensive set of rules governing the proper collection, use and disposal of the credit union’s data. Ensuring that everyone in the organization is adhering to policies is important to data’s success.

Set Data Governance Mission and Objectives:  What do you want data governance to accomplish? For most credit unions, the objectives include: enabling better decision-making, reducing operational waste, meeting the needs of current and future members, educating management and staff to adopt common approaches to data issues, reducing costs, and ensuring data quality throughout the organization. Analytics strategies are meaningless if the data powering them is unreliable. Placing data governance at the heart of your data and analytics strategy will ensure quality data translates directly into better business value.

Agree on Key Terms and Definitions: As a best practice, a component to an effective data governance program hinges on the development of strong and consistent data definitions and terms. It is important to data’s success to establish standardized data definitions across the organization that everyone understands and adheres to. The implementation of a data dictionary can be established within the database system, making it a priority to ensure that all data terms and business glossaries are the same. For example, if three different people in your organization are asked “what is a member”, those three people should provide the same, consistent response.

Perform a Data Quality Audit: Data quality audits can be time-consuming, but it is a valuable activity that certifies the data’s accuracy. Technical quality issues such as inconsistent structure, missing data, typos or other errors in the data fields are easy to spot and correct. However, more complex issues should be approached with more defined processes. Starting small in profiling data quality is recommended. I generally advise clients to start with a focus on a very narrow data set — perhaps a certain line of business, or a subset of member data. Make sure you establish a standardized method to share your findings and key steps to correct inaccurate data. Develop a process whereby business users can report data quality issues and then work with the data governance committee to research the error’s source and develop a resolution.

Developing a successful data governance strategy requires careful planning, the right people, ownership and appropriate tools and technologies. The key to an ongoing data governance program is to take an incremental approach, ensure buy-in across all business and IT departments, and hold key business units accountable.

If your credit union needs support in establishing a data governance program, The Knowlton Group can help. We believe that the best data and analytics program starts with a great strategy, a clearly defined roadmap and implementation plan, and a methodology for ensuring data’s accuracy.

1. Data Quality and the Bottom Line- The Data Warehouse Institute