How do you use benchmarks to improve data quality?

1 Define your data quality dimensions

The first step is to define what data quality means for your specific project and context. Data quality can be measured along different dimensions, such as completeness, consistency, validity, accuracy, timeliness, and relevance. Depending on your data sources, objectives, and requirements, you may need to prioritize some dimensions over others, or use different criteria and metrics to assess them. For example, completeness may refer to the percentage of missing values, consistency may refer to the adherence to data standards and formats, and validity may refer to the compliance with business rules and constraints.

2 Identify your data quality benchmarks

The next step is to identify your data quality benchmarks, which are the values or ranges that you want to achieve or maintain for each dimension. Benchmarks can be derived from various sources, such as industry best practices, regulatory guidelines, customer expectations, historical data, or external data. For example, you may use a benchmark of 95% completeness for your customer data, based on the average of your competitors or the minimum required by your contract. Alternatively, you may use a benchmark of 10 minutes for the timeliness of your sales data, based on the frequency of your data updates or the demand of your stakeholders.

3 Compare your data quality to your benchmarks

The third step is to compare your data quality to your benchmarks, using appropriate methods and tools. This can involve performing data quality audits, using data quality software, or applying data quality indicators and dashboards. The goal is to identify and quantify the gaps and deviations between your actual data quality and your desired data quality. For example, you may find that your customer data has 90% completeness, which is below your benchmark of 95%, or that your sales data has an average delay of 15 minutes, which is above your benchmark of 10 minutes.

4 Implement data quality improvement actions

The final step is to implement data quality improvement actions, based on the results of your comparison. This can involve correcting, enhancing, or transforming your data, using data cleansing, enrichment, or integration techniques. It can also involve modifying or optimizing your data processes, policies, or systems, using data governance, stewardship, or architecture principles. The aim is to reduce or eliminate the data quality issues that prevent you from meeting or exceeding your benchmarks. For example, you may improve the completeness of your customer data by filling in the missing values, using data from other sources or imputation methods. Or you may improve the timeliness of your sales data by streamlining your data pipeline or increasing your data bandwidth.

5 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?