How can you monitor and report on data quality using customer data quality metrics?

2 How to define customer data quality metrics?

Before you can monitor and report on customer data quality metrics, you need to define them based on your business goals, data requirements, and quality criteria. To do this, you should first identify the key customer data elements that are relevant for your business processes, such as name, email, address, phone number, purchase history, etc. Then specify the quality dimensions that are important for each data element, such as completeness, accuracy, consistency, timeliness or uniqueness. After that, set target values or thresholds for each quality dimension such as 95% completeness, 98% accuracy, 100% consistency, 24 hours timeliness or 0% uniqueness. Finally select the methods or tools to measure and calculate each quality metric like data profiling, validation, cleansing, matching or deduplication.

4 How to report on customer data quality metrics?

Monitoring customer data quality metrics is not enough if you want to improve your data quality. You need to report on them to communicate the results and findings to stakeholders, such as data owners, users, managers, or senior executives. To do this, you can use a data quality report which summarizes and highlights the key aspects of your metrics. This report can provide an overview of the current state and trends of your data quality and show the impact and value of your initiatives. Additionally, it can showcase the achievements and challenges of your data quality, recommend actions to address issues and improve outcomes, as well as solicit feedback from stakeholders while fostering collaboration and engagement of your team.

5 How to improve customer data quality metrics?

Monitoring and reporting on customer data quality metrics are essential for understanding and managing your data quality, however, they are not enough to ensure optimal data quality. To improve your data quality metrics, you can use a data quality improvement framework that guides you through the plan, do, check, and act phases. The plan phase involves defining the scope, objectives, roles, and resources of your project while also establishing the baseline and targets of your data quality metrics. The do phase consists of executing activities such as data cleansing, enrichment, integration, standardization, or governance. The check phase involves evaluating the results and outcomes of your activities in comparison to the baseline and targets. Lastly, the act phase involves documenting lessons learned and best practices of your project before implementing changes to sustain and enhance your data quality metrics.