How do you keep data clean in your data warehouse?

1 Define data quality standards

Before you load data into your data warehouse, you need to define what constitutes good quality data for your business needs and goals. Data quality standards are the rules and criteria that specify the expected level of completeness, validity, consistency, accuracy, and timeliness of the data. You can use data quality dimensions, such as those proposed by DAMA International, to measure and evaluate your data quality standards. You should also document and communicate your data quality standards to all the stakeholders involved in the data warehouse project.

2 Implement data quality checks

Once you have defined your data quality standards, you need to implement data quality checks to ensure that the data meets those standards before and after loading it into your data warehouse. Data quality checks are the processes and tools that verify, validate, and correct the data according to the data quality standards. You can use different types of data quality checks, such as data profiling, data cleansing, data auditing, and data monitoring, to identify and resolve data quality issues at various stages of the data warehouse lifecycle. You should also automate and integrate your data quality checks with your data warehouse workflow to make them more efficient and effective.

3 Establish data quality roles and responsibilities

Data quality is not only a technical issue, but also a business and organizational issue. Therefore, you need to establish clear data quality roles and responsibilities for the people who are involved in the data warehouse project. Data quality roles and responsibilities are the tasks and duties that each person or group has to perform to ensure and improve the data quality in the data warehouse. You can use different frameworks, such as RACI or RASCI, to assign and communicate the data quality roles and responsibilities. You should also provide adequate training and support for the people who are responsible for data quality.

4 Monitor and report data quality metrics

Data quality is not a one-time event, but a continuous process that requires constant monitoring and reporting. Data quality metrics are the quantitative and qualitative measures that indicate the level and trend of data quality in the data warehouse. You can use different methods, such as dashboards, scorecards, or reports, to monitor and report your data quality metrics. You should also align your data quality metrics with your business objectives and expectations, and use them to evaluate and improve your data quality performance and outcomes.

5 Adopt a data quality culture

Data quality is not only a technical process, but also a cultural value that influences the behavior and attitude of the people who work with the data warehouse. Therefore, you need to adopt a data quality culture that fosters a shared vision, commitment, and accountability for data quality among all the stakeholders involved in the data warehouse project. You can use different strategies, such as data quality awareness, education, recognition, or incentives, to adopt and promote a data quality culture. You should also encourage feedback and collaboration among the data quality stakeholders, and continuously review and update your data quality standards, checks, roles, and metrics.

6 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?