Although data is one of the strongest assets a credit union has (yes, it’s potentially more valuable than loans), it can be difficult to leverage it. This is compounded by critical data governance. Data governance is the formal management of data access, quality, and security throughout its lifecycle. It is part of organizational data maturity efforts and works best when aligned with data strategy. Although it seems obvious, actively creating a data governance program is not.
Data governance as a spice rack
Let’s think about it differently. Think of data governance as a spice rack. The standard structure of a spice rack is a rack of small containers with the names and details of its contents. The recipe will call for a specific amount of spices when preparing a dish. The spice containers are selected and the quantity is extracted and added to the rest of the ingredients. There is nothing unusual in this scenario.
However, the recipe calls for four teaspoons of black pepper, and the contents are red when the container labeled “black pepper” is opened. Something is wrong. Let’s assume you didn’t catch the error. You continued to add the four teaspoons of red “bell pepper” to the recipe. Unfortunately, the red “pepper” turns out to be cayenne. So not only did the flavor intensity increase in the dish being created, it probably became rather unpleasant. This makes anyone sharing the dish with you wary of other dishes coming from your kitchen.
The same is true with data. Using our spice benchmark, credit union data lives in a venerable spice rack. It is hosted in the same place (with the central system, data management tools, etc.) and is extracted to be combined with other data to create reports and information on the performance of the credit union.
If the “black pepper container” of data is selected and the data coming out is not black pepper but cayenne pepper, a formal data governance program can be introduced to help:
1. Identify what the new substance is;
2. Determine how it got into the wrong container; and
3. Develop policies and procedures to make sure it doesn’t happen again.
Data governance is a continuous quality loop that works to define, prioritize, audit, and establish policies and procedures. It also works to maintain processes that enable effectiveness and efficiency.
It encompasses the people, processes, and information technology needed to create consistent and appropriate data management and information understanding across the organization, breaking down the boundaries created by organizational structures.
Building the fundamental structure of a data governance best practice includes six areas:
1. Use case: The business problem the credit union is using data to solve.
2. Data domains: The data needed to solve the use case.
3. Prioritization: Classification of data into critical and non-critical categories.
4. Documents: The documentation needed to record data sources, users, definitions, and other important information involving the data.
5. Quality: The process of data auditing for quality and error mitigation.
6. Policies and Procedures: The documents and workflow that help achieve the goals of the data governance program.
Here are three common pitfalls that occur when building a data governance program:
1. Not having a formal program. Many credit union leaders believe their data governance program will grow organically. This is not the case. The best place to start is to understand what the vision for enterprise data is and gain a clear understanding of member-centric use cases.
2. Lack of leadership. Credit unions can treat data governance as an IT initiative and not assign credit union management to this effort. A Chief Data Officer – who can be the CEO, COO, CMO, or CFO – should take the reins of the project to provide a holistic view of data.
3. Focus only on data. Many data governance efforts fail because the focus is only on data quality and tool implementation. Data governance is much broader than that. It is an iterative and continuous process that helps the organization achieve its goals.
key to victory
Data governance is the mandatory groundwork that needs to be done for business intelligence to be successful. Data doesn’t have to be perfect to begin with. However, credit unions should establish a prioritized roadmap for 99% data integrity. Without good data, reporting, analysis, and forecasting, the credit union will end up with significant blind spots that can render analysis obsolete.
Anne Legg is the founder of THRIVE Strategic Services, a San Diego, Calif.-based company that assists credit unions with data transformation, and author of “Big Data/Big Climb,” a credit union handbook for data transformation.