How can benchmarking improve data quality maturity?

1 What is benchmarking?

Benchmarking is a systematic and continuous process of comparing your data quality performance with external or internal references, such as best practices, industry standards, peer organizations, historical data, different business units, or different data domains. This process can help you understand how your data quality compares to others in your industry or sector, identify gaps and opportunities for improvement in your data quality processes, policies, and practices, learn about best practices and proven solutions for addressing your data quality issues, and set realistic and achievable data quality goals and targets for your organization.

2 How to conduct benchmarking?

Benchmarking involves four main steps: planning, collecting, analyzing, and acting. In the planning step, you need to define the scope, objectives, and criteria of your benchmarking project. You need to identify the data quality dimensions, metrics, and indicators that you want to measure and compare. You also need to select the sources and methods of data collection, such as surveys, interviews, audits, or reports. In the collecting step, you need to gather the data from the selected sources and ensure its accuracy, completeness, and consistency. You may need to normalize or standardize the data to make it comparable across different sources. In the analyzing step, you need to compare and contrast the data and identify the similarities and differences in your data quality performance. You need to use appropriate statistical techniques, such as descriptive, inferential, or comparative analysis, to draw meaningful insights and conclusions. You also need to validate and verify your findings with the relevant stakeholders. In the acting step, you need to use the results of your analysis to define and prioritize your data quality improvement actions. You need to set SMART (specific, measurable, achievable, relevant, and time-bound) goals and targets for your data quality improvement. You also need to monitor and evaluate the progress and impact of your actions and adjust them as needed.

4 What are the challenges of benchmarking?

Benchmarking can be a great tool for data quality maturity improvement, but it also poses some challenges and risks. It can be time-consuming and resource-intensive to collect, analyze, and compare data from various sources and contexts. It is also difficult to find suitable and reliable sources and methods of data collection and comparison. Obtaining and sharing data from external or internal sources may be challenging due to confidentiality, privacy, or security issues. Comparing data quality performance without considering the differences in definitions, dimensions, metrics, and indicators can lead to misleading results. Finally, it can be demotivating to compare data quality performance with unrealistic benchmarks.

5 How to overcome the challenges of benchmarking?

To successfully complete a benchmarking project, you must follow best practices and guidelines. Start by defining the scope, objectives, and criteria of your benchmarking project and aligning them with your data quality strategy and vision. Then, select the data quality dimensions, metrics, and indicators that are relevant and meaningful for your data quality assessment. Additionally, choose reliable sources and methods of data collection and comparison that are valid and comparable for your data quality benchmarking. Furthermore, it is important to ethically and responsibly obtain and share data from external or internal sources while respecting the confidentiality, privacy, and security of the data and the data providers. Lastly, analyze and compare data quality performance objectively and critically while taking into account the differences in the data quality definitions. Use the results of your data quality benchmarking constructively to focus on the gaps and opportunities for improvement rather than shortcomings.