How do you maintain data quality across systems?

2 Implement data quality checks and controls

The second step to maintain data quality across systems is to implement data quality checks and controls throughout the data lifecycle, from collection to analysis. You need to design and enforce data quality rules and standards, such as data formats, schemas, values, and ranges, and apply them to all the data sources and systems that you use. You also need to automate data quality checks and controls as much as possible, using tools and techniques such as data validation, cleansing, transformation, deduplication, and enrichment.

3 Monitor and measure data quality performance

The third step to maintain data quality across systems is to monitor and measure data quality performance on a regular basis, using the criteria and metrics that you defined in the first step. You need to collect and analyze data quality indicators and reports, such as error rates, completeness ratios, consistency scores, and timeliness measures, and compare them with your expected and acceptable levels of data quality. You also need to identify and prioritize data quality issues and gaps, and track their root causes and impacts.

4 Resolve and prevent data quality problems

The fourth step to maintain data quality across systems is to resolve and prevent data quality problems as soon as they are detected or anticipated. You need to implement data quality improvement actions and initiatives, such as data correction, update, deletion, integration, and standardization, and verify their effectiveness and outcomes. You also need to prevent data quality problems from recurring or spreading, by addressing their root causes and implementing preventive measures, such as data governance, documentation, training, and feedback.

5 Collaborate and communicate with data stakeholders

The fifth step to maintain data quality across systems is to collaborate and communicate with data stakeholders, such as data producers, consumers, owners, custodians, and analysts. You need to establish and maintain data quality roles and responsibilities, such as who is accountable for data quality, who is responsible for data quality tasks, and who is consulted or informed about data quality issues and actions. You also need to foster a data quality culture and awareness, by sharing data quality goals, results, best practices, and lessons learned.

6 Review and update data quality practices

The sixth step to maintain data quality across systems is to review and update your data quality practices periodically, to ensure that they are still relevant, effective, and efficient. You need to evaluate and benchmark your data quality performance, using the criteria and metrics that you defined in the first step, and identify and acknowledge your data quality strengths and weaknesses. You also need to update and optimize your data quality practices, by incorporating new data sources, systems, technologies, standards, and requirements.

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?