How do you document data quality and integrity standards for your team?

1 Define data quality and integrity

Before you can document data quality and integrity standards for your team, you need to define what data quality and integrity mean for your specific context and goals. Data quality and integrity are broad concepts that can have different dimensions and criteria depending on the type, purpose, and use of data. For example, some common dimensions of data quality are completeness, validity, accuracy, timeliness, consistency, and relevance. Some common criteria of data integrity are authenticity, reliability, security, and traceability. You can use these dimensions and criteria as a starting point, but you should also tailor them to your team's needs and expectations.

2 Establish data quality and integrity goals

Once you have defined data quality and integrity for your team, you need to establish clear and measurable goals that align with your data objectives and outcomes. Data quality and integrity goals are statements that describe the desired level of quality and integrity for your data, and how you will assess and monitor them. For example, a data quality goal could be "To ensure that all customer data is complete, accurate, and up-to-date", and a data integrity goal could be "To verify the authenticity and reliability of all data sources and transactions". You should also specify the indicators, metrics, and thresholds that you will use to measure and evaluate your data quality and integrity goals.

3 Identify data quality and integrity issues

The next step is to identify the current and potential data quality and integrity issues that your team faces or may face in the future. Data quality and integrity issues are problems or errors that affect the quality and integrity of your data, and can have negative impacts on your data analysis, reporting, or decision making. For example, some common data quality issues are missing values, duplicates, outliers, inconsistencies, or inaccuracies. Some common data integrity issues are unauthorized access, modification, deletion, or corruption of data. You should also identify the root causes, sources, and consequences of these issues, and prioritize them based on their severity and urgency.

4 Implement data quality and integrity controls

After identifying the data quality and integrity issues, you need to implement data quality and integrity controls that can prevent, detect, or correct these issues. Data quality and integrity controls are methods, tools, or processes that can enhance the quality and integrity of your data, and reduce the risks and costs of data errors. For example, some common data quality controls are data validation, cleansing, standardization, enrichment, or deduplication. Some common data integrity controls are data encryption, backup, audit, or recovery. You should also document the steps, roles, and responsibilities involved in implementing these controls, and how often and by whom they will be performed.

5 Document data quality and integrity standards

The final step is to document the data quality and integrity standards that you have defined, established, identified, and implemented for your team. These formal documents should define the dimensions and criteria of data quality and integrity, as well as the goals, indicators, metrics, and thresholds. They should also address any issues, root causes, sources, and consequences related to data quality and integrity. Moreover, they should outline the controls, steps, roles, and responsibilities for maintaining quality and integrity. Lastly, these documents should be accessible, understandable, updated regularly, and aligned with organizational or industry standards.