Data Quality Standards

1. Accuracy: Ensure data is correct, reliable, and free from errors. Verify data entry processes and data sources for correctness.

2. Consistency: Maintain uniformity in data representation across different sources and datasets. Standardize data formats, units of measurement, and data conventions.

3. Completeness: Ensure that all required data is present and there are no gaps or missing values. Identify and address any data gaps in the datasets.

4. Timeliness: Make sure that data is up-to-date and available when needed. Establish processes for regular data updates and refreshes.

5. Uniqueness: Avoid duplicate records or data entries in your datasets. Implement deduplication techniques to identify and remove duplicates.

6. Validity: Ensure data conforms to predefined formats, rules, or constraints. Apply data validation techniques such as range checks, pattern matching, and referential integrity checks.

7. Integrity: Maintain relationships between different data entities and ensure referential integrity. Implement foreign key constraints and other techniques to preserve data relationships.

8. Relevance: Collect and store data that is relevant to your business needs and objectives. Regularly review and update data collection processes to ensure relevance.

9. Accessibility: Provide easy access to data for authorized users while ensuring data security. Implement data access controls and user authentication mechanisms.

10. Security: Protect data from unauthorized access, modification, or deletion. Implement data encryption, access controls, and other security measures.

11. Traceability: Track the lineage of data from its source to its final destination, including any transformations or modifications. Implement data lineage tracking tools and techniques.

12. Understandability: Ensure data is easily understood by users, with clear definitions, metadata, and documentation. Provide data dictionaries, glossaries, and other documentation resources.

13. Granularity: Determine the appropriate level of detail for data based on its intended use. Balance the need for detailed data with storage and processing considerations.

14. Precision: Ensure data is represented with the right level of precision and rounding. Choose appropriate data types and decimal places for numerical data.

15. Data Representation: Use consistent encoding, character sets, and data formats across all datasets. Standardize on common standards such as UTF-8 for text encoding or ISO 8601 for date and time representation.