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Measuring the success of a credit union's data analytics program is important because it helps the credit union understand the value that the program is delivering and identify areas for improvement.

There are many key performance indicators (KPIs) that credit unions can use to measure the success of their data analytics program. Some of the top KPIs to consider include:

Return on investment (ROI): Credit unions should measure the ROI of their data analytics program by calculating the financial benefit of their data analytics efforts. This can include measures such as increased revenue, reduced costs, and improved efficiency.

Adoption and usage: Credit unions should track the percentage of employees who are using data analytics tools and techniques in their work, as well as the frequency of use. This can help credit unions gauge the level of adoption and usage of their data analytics program.

Data availability: Credit unions should measure the percentage of data that is available and accessible to employees who need it. This can help ensure that data is being used effectively to inform decision-making.

Data quality: Credit unions should track the quality and accuracy of the data they are using for analysis. This can be done by measuring the percentage of data that is complete, accurate, and up-to-date.

Time to insights: Credit unions should track the time it takes to extract insights from data, as well as the time it takes to turn those insights into action. This can help credit unions identify areas where they can improve their data analytics processes and become more efficient.

By tracking these most important KPIs, credit unions can measure the success of their data analytics program and identify areas for improvement.

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