Data Quality Management is a crucial aspect of implementing Data Vault, as the success of analytics and business decision-making directly depends on data quality. Data Vault provides built-in mechanisms for data quality control, which are complemented by external tools and processes.

Key Aspects of Data Quality Management

1. Data Integrity

Data integrity means that all relationships between hubs, links, and satellites remain consistent.

  • Relationship Control: All business keys in links must exist in the corresponding hubs.
  • Uniqueness Check: Business keys in hubs must be unique.

Example:
If Link_PassengerFlights contains a relationship with PassengerKey, which is missing in Hub_Passengers, this indicates an issue in the source data or the loading process.

2. Data Completeness Check

Data completeness ensures that all required data from sources is loaded into the warehouse.

  • Identify missing attributes or records during the loading phase.
  • Use logs to track the success and errors of data loading.

Example:
If CRM provides customer data but lacks their date of birth, this can be recorded as NULL in the satellite and reported to the quality control team.

3. Duplicate Handling

Data duplicates can compromise model integrity and lead to incorrect conclusions.

  • Ensure hubs do not contain duplicate business keys.
  • Regularly perform uniqueness checks on data.

Example:
If two sources provide the same customer with different business keys, the hub can merge them into a single entity while preserving a unique surrogate key.

4. Valid Value Check

Certain attributes must conform to specific ranges or formats.

  • Use validation rules to verify data.
  • Create reports on rule violations and send them for correction.

Example:
If a source lists a customer's date of birth that exceeds the current date, the system should record the error and exclude this entry from loading.

Built-in Data Vault Mechanisms for Quality Management

  1. Change History Storage:
    Satellites record all attribute changes, allowing tracking of the source and moment of errors.
  2. Data Source Identification:
    Each hub, link, and satellite specifies the data source, helping to quickly locate the problem's origin.
  3. Load Date:
    The LoadDate and EndDate attributes determine which data was relevant at a specific time.

Tools for Quality Control

1. SQL Queries

Create scripts for regular data integrity and quality checks:

  • Check the uniqueness of business keys.
  • Identify NULL values in mandatory fields.

2. Monitoring Tools

  • DataOps.tools: Automates data checks, creates quality reports, and visualizes errors.
  • Talend Data Quality: Provides tools for data profiling and issue resolution.
  • Apache Superset: Used to create dashboards with data quality metrics.

3. Automated Control

Integration with ETL/ELT tools (e.g., dbt) enables data quality checks during the loading phase.

Example of a Quality Management Process

Task: Identify customer data with incorrect birth dates.

  1. SQL Check:

    SELECT * FROM Sat_PassengerDetails WHERE DateOfBirth > GETDATE() OR DateOfBirth IS NULL;

  2. Actions:

    • Record erroneous entries in a log.
    • Notify data owners about the need for corrections.

  3. Result:
    Ensure only correct data is included in analytical reports.

Recommendations for Effective Quality Management

  1. Regular Checks:
    Schedule automated tasks for data analysis.
  2. Feedback:
    Establish a process for notifying about data issues and tracking their resolution.
  3. Quality Metrics Visualization:
    Create reports and dashboards for real-time data quality monitoring.

Conclusion

Data quality management is not a one-time process but an ongoing activity that maintains the reliability and integrity of the warehouse. Combined with Data Vault's built-in mechanisms and modern automation tools, it ensures high reliability and trust in the data used for analysis.