How do you prioritize data quality?

1 Assess the impact

The first step to prioritize data quality is to assess the impact of data errors, inconsistencies, or gaps on your objectives, outcomes, and stakeholders. For example, you can ask yourself questions like: How critical is this data for your analysis? How will data quality affect your results and recommendations? How sensitive is this data to your audience and their expectations? How costly or risky is it to fix or ignore data quality issues? By answering these questions, you can identify the most significant and urgent data quality problems that need your attention.

2 Define the dimensions

The second step to prioritize data quality is to define the dimensions or criteria that you will use to measure and evaluate data quality. There are many possible dimensions of data quality, such as accuracy, completeness, consistency, timeliness, validity, reliability, and relevance. However, not all dimensions are equally relevant or important for every data set or use case. Therefore, you need to select the dimensions that match your purpose, context, and standards. For example, you can use a data quality framework, such as the one proposed by the Data Management Association (DAMA), to guide your selection.

3 Apply the techniques

The third step to prioritize data quality is to apply the techniques or methods that you will use to assess and improve data quality. There are many possible techniques for data quality management, such as data profiling, data cleansing, data validation, data integration, data enrichment, and data monitoring. However, not all techniques are equally feasible or effective for every data set or situation. Therefore, you need to choose the techniques that suit your resources, constraints, and goals. For example, you can use a cost-benefit analysis, a risk assessment, or a prioritization matrix to help you decide.

5 Implement the feedback

The fifth step to prioritize data quality is to implement the feedback or suggestions that you receive from your stakeholders or users regarding data quality. This is important to ensure continuous improvement, learning, and adaptation of your data quality management process. For example, you can use surveys, interviews, or focus groups to collect feedback on data quality. You can also use data quality metrics, indicators, or benchmarks to monitor feedback on data quality. You can then use this feedback to identify new or changing data quality issues, priorities, or solutions.

6 Review the process

The sixth step to prioritize data quality is to review the process or approach that you use to prioritize data quality. This is important to ensure consistency, efficiency, and effectiveness of your data quality management process. For example, you can use audits, evaluations, or reviews to assess the strengths, weaknesses, opportunities, and threats of your data quality management process. You can also use best practices, standards, or guidelines to compare and improve your data quality management process. You can then use this review to refine or revise your data quality priorities, dimensions, techniques, results, feedback, or process.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?