Originally published in the CU Times 8.19.22
While data is one of the most robust assets a credit union owns (yes, it’s potentially more valuable than loans), it can be challenging to leverage. Compounding this situation is the critical governance of the data. 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 the data strategy. While this sounds obvious, actively creating a data governance program is not.
Data Governance as a Spice Rack
Let’s think about this differently. Let’s think about data governance as if it were a spice rack. The standard structure of a spice rack is a holder of small containers with the names and details of its contents. The recipe will call for a specific amount of spices when making a dish. The spice containers are selected, and the amount is extracted and added to the rest of the ingredients. There is nothing unusual in that 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 amiss. Suppose you didn’t catch the error. You continued to add the four teaspoons of the red “pepper” to the recipe. Unfortunately, the red “pepper” turns out to be cayenne. So not only has the flavor intensity increased in the dish that is being created, but it has probably become rather unpleasant. It makes anyone sharing the dish with you wary of other dishes that come from your kitchen.
The same is true with data. Credit union data lives in a venerable spice rack using our spice reference. It is housed in the same place (along with the core system, data management tools, etc.) and is extracted to be combined with other data to create reports and insights about the credit union’s performance.
If the “black pepper container” of data is selected and the data that pours out is not black pepper but cayenne, 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. Build out policies and procedures to make certain it doesn’t happen again.
Data governance is a continuous quality loop that functions to define, prioritize, audit, and set policies and procedures. It also works to maintain processes that enable effectiveness and efficiency. It encompasses the people, processes, and information technology required to create consistent and proper handling of data and understanding of information across the organization, eliminating the boundaries created by organizational structures.
Building the foundational structure of a best data governance 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: The ranking of data into critical and non-critical categories.
4. Documentation: The documentation needed to record data sources, users, definitions, and other important information involving the data.
5. Quality: The process of auditing data for quality and mitigating errors.
6. Policies and Procedures: The documents and workflow that achieve the goals of the data governance program.
The following are three common pitfalls that occur when creating a data governance program:
1. Not having a formal program.
Many credit union leaders feel that their data governance program will grow organically. This is not the case. The best place to start is to clearly understand the enterprise data vision and the member-centric use cases.
2. Lack of leadership.
Credit unions may treat data governance as an IT initiative and not assign credit union leadership to the effort. A chief data officer – who can be the CEO, COO, chief marketing officer, or CFO – should take the reins of the project to provide a global lens of the data.
3. Focusing solely 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, continuous process that helps the organization achieve its goals.
Key to Victory
Data governance is the mandatory groundwork that must be completed for business intelligence to be successful. Data does not have to be perfect at the start. However, credit unions should establish a prioritized road map for 99% data integrity. Without good data, reports, analysis, and forecasting, the credit union will be left with significant blind spots that can render analysis obsolete.
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