executives around the table analyzing the data Source: Adobe Stock

So you’re ready to build or even overhaul your credit union’s data governance program. You have already proven that data is a valuable asset to your business and your members. Now is the time to add more processes and procedures to create a framework for data management and analytics practice.

While this guide isn’t meant to be an end-to-end plan for starting from scratch, it will provide you with critical tips when it comes to organizing and governing your data warehouse. Ultimately, your data governance efforts should build confidence in your business intelligence efforts, which helps you make decisions supported by historical data trends.

Incorporate these five data governance tips to get started:

1. Data quality rules

There is a reason why data quality is # 1 on this list. Data quality functions as a programmatic basis for writing, applying and assigning responsibilities to a set of “rules” defined by the company. We recommend that you automate and monitor the quality of your data – typically, this is deployed alongside your data warehouse or data lakehouse. Use metadata and timestamps to track your progress over time so you can measure data flaws versus successful data cleanup.

An example of a useful data quality rule would be “open accounts missing an email address”. Your system is designed to allow new accounts without an email address, but perhaps your marketing department wants to reduce the number of accounts with missing email addresses so they can better communicate with their members.

Marketing suggests running a direct mail campaign by encouraging members to associate their email address with their account to take advantage of an exclusive offer. Let’s say 15% of recipients respond to the campaign and take the desired action. We can now measure the success of the campaign and track the improvement of that specific data quality rule.

2. Clear definitions from each source

Mapping each field and table from each data source into the data model is another critical component of the data warehouse process. Documenting these will help in the future as new fields are added or removed from the source or target system. Overall, this is another way to document your business logic in writing.

Many times, the mapping can be clear and obvious, but other times not so much. If multiple source systems all have a unique “Member ID” field for each member record, you may need to choose one as the general primary key and store the others as associations. When you really get into the weeds, you’ll also need to consider the destination and destination data types. For example, if your “Member ID” field is alphanumeric in the source system, you will need to make sure that the target data model has a field that can contain alphanumeric characters and not just integers.

This process is a collaboration between business users who have an understanding of business logic and any important calculation together with an experienced data or business analyst who can translate everything into SQL or other common database languages.

3. Data dictionary

As an additional source for your definitions, your data dictionary will be a huge resource for documenting specific terms, calculated fields, and other important data assets. Building a data dictionary is especially important as the internal user base gets greater access to dashboards and reports. Supporting deployment to multiple business users with a data dictionary will save your team a lot of time and also future-proof your investment against turnover or knowledge loss over time.

An example in the banking sector can be taken from the Federal Reserve Bank of New York. Take a look at the data dictionary he posted along with an Equifax credit report to provide additional context for anyone who uses the information within the report.

Building a data dictionary from scratch is quite a big undertaking for any company. One way credit unions can avoid addressing this problem on their own is to leverage a CUSO with a credit unions-specific data model that contains many shared definitions. From there, the company can define any custom fields or calculations in addition to what is shared within the industry itself.

4. Referenceable hierarchies

An important aspect of data governance is managing and accounting for who has access to what. At the same time, you will want to make sure that the people who do they need access has it. One way to do this is through the use of specific reference tables.

Let’s say the loan department has an extensive list of different loan types that they use on a regular basis. The loan department also works to bring new products to market, so they need to be able to add new loan types on a semi-frequent basis. One way to give them this access is to develop a process where they can update and upload, for example, an Excel file to a secure location that updates a reference table with the latest loan types.

The loan department does not require administrative privileges to update the database itself, but instead has a predefined process for updating a specific reference table with an easy-to-use spreadsheet. They simply add the new loan type code and loan type description, upload the file, then the database checks the file and updates the reference table without any further intervention from the analysis team.

This approach combines the secure and controlled access requirements of the data governance program, but still gives the loan department the flexibility to perform their daily roles and tasks.

5. Adoption Dashboard

How do your users interact with the dashboard you created? Who are your top five power users? Which dashboards are used the most (and least)?

These kinds of questions will start to arise as your data analytics program matures. We recommend that you know how your internal users interact and interact with the content they have requested in the past.

Building a user adoption dashboard is a way to track usage trends over time when you invest heavily in data analytics. One way to use it is to manage paid licenses. If a user no longer logs in, you can easily review the data and exchange a license with another executive or manager who will be using it more actively.

Return on investment

Investing time and energy in your data governance program pays dividends in the long run. You will better understand historical data trends and increase your ability to act based on the information uncovered by targeted data analysis. Review these tips at the next Data Governance Committee meeting and take control of your data journey.

Judith Bernholc Judith Bernholc

Judith Bernholc is Head of Customer Success at Arkatechture, a data services and consulting firm based in Portland, Maine.

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