Data Quality Assurance Assessment (Integration Design)

Owned by Mike Deskis
May 19, 2023
3 min read

To assess data quality assurance in the integration design phase of the Pricefx data readiness methodology, you can follow these steps:

  • Review Data Quality Requirements: Start by reviewing the documented data quality requirements for the integration project. This may include specific quality targets, data accuracy, completeness, consistency, timeliness, and other relevant quality criteria. Understand the expected data quality standards and metrics that need to be achieved.

  • Evaluate Data Validation Rules: Assess the integration design for data validation rules and checks. Review the design documentation to identify the validation logic and mechanisms included in the data integration flows. Verify that the design incorporates data validation rules to ensure the accuracy, consistency, and integrity of the data being integrated.

  • Assess Error Handling Mechanisms: Evaluate how the design handles data quality errors and exceptions. Review the error handling mechanisms defined in the design, such as error codes, error logs, and error notifications. Ensure that the design includes appropriate error handling processes to capture and report data quality issues encountered during the integration process.

  • Verify Data Reconciliation Processes: Assess if the integration design includes data reconciliation processes. Data reconciliation involves comparing data between source and target systems to ensure consistency and accuracy. Verify that the design incorporates mechanisms to reconcile and validate the integrated data against the source data or predefined business rules.

  • Evaluate Data Quality Monitoring and Reporting: Assess if the design includes mechanisms for monitoring and reporting data quality issues. Verify if the design incorporates data quality monitoring tools or techniques to track the quality of integrated data in real-time or on a scheduled basis. Assess if the design includes data quality reporting capabilities to generate data quality metrics, dashboards, or alerts.

  • Consider Data Profiling and Cleansing: Evaluate if the design incorporates data profiling and cleansing techniques. Data profiling involves analyzing the quality, structure, and content of the data to identify anomalies, duplicates, or inconsistencies. Verify if the design includes data cleansing processes to address data quality issues and improve the overall data integrity.

  • Review Data Governance Framework: Assess if the integration design aligns with the organization's data governance framework. Data governance encompasses policies, processes, and controls for managing and ensuring data quality. Verify if the design adheres to the defined data governance standards, guidelines, and best practices.

  • Seek Stakeholder Feedback: Engage relevant stakeholders, including business users, data owners, and subject matter experts, to gather their feedback on the data quality assurance aspects of the design. Obtain their perspectives on the effectiveness of the data validation rules, error handling processes, and data reconciliation mechanisms. Incorporate their feedback and address any concerns or suggestions.

  • Perform Testing and Validation: Conduct testing and validation activities to assess the effectiveness of the data quality assurance measures. Execute test scenarios that focus on data quality aspects such as accuracy, completeness, consistency, and timeliness. Verify if the design adequately detects and handles data quality issues during the testing phase.

  • Continuously Monitor and Improve: Remember that the assessment of data quality assurance is an ongoing process. Continuously monitor the actual implementation of the design, collect feedback from users, and track the data quality metrics. Identify areas for improvement and optimization based on the feedback and evolving business requirements.

By following these steps, you can assess data quality assurance in the integration design phase of the Pricefx data readiness methodology. This assessment helps ensure that the design incorporates mechanisms to maintain data quality standards, validate data accuracy, handle errors, reconcile data, and monitor data quality throughout the integration process.