Data Management Strategy based on Use-Case Scenarios for Ensuring Data Integrity and Precision
In the realm of data management, a new approach is gaining traction: Data Readiness. This strategy, aimed at effectuating data stewardship, changes the dialog among stakeholders and offers a fresh perspective on data quality management.
The Data Readiness-driven approach proposes a paradigm shift, focusing on the question of for what use cases data is readied, rather than pursuing the highest quality of data in absolute terms. This strategy is similar to how software readiness is tested before deployment, with the primary focus on whether the software can meet functional requirements.
To implement this approach, a data readiness assurance framework can be established. This framework combines quantitative metrics, customizable rules, and automated remediation steps. It involves defining data readiness metrics such as sample size, class imbalance, data distribution, and data integrity. These metrics are automatically generated via configurations before data consumption or model training, objectively assessing whether the data meets the quality standards needed for the intended use.
Customizing rules and automated remedies tailored to the specific dataset and consumption task is another crucial aspect. This can be achieved using modules like CADRE (Customizable Assurance of Data Readiness). This allows targeted intervention to fix quality issues or transform the data to improve readiness, much like software testing frameworks allow custom tests and fixes.
Embedding data readiness evaluation early in the data pipeline is essential, ideally before model training or downstream use. Automatic reports that summarize readiness status and highlight risk areas for human review are crucial, similar to pre-release software testing stages.
Understanding the business decision context and data requirements is also vital to ensure data readiness workflows align with the specific consumption needs. This includes human-in-the-loop validation to confirm data is accurate, trustworthy, and relevant.
Implementing audit trails, explainability frameworks, and access controls is necessary to monitor data use and maintain quality and compliance. This supports responsible consumption, similar to software usage governance.
Adopting structured, scalable frameworks like Data Readiness Levels and Data Processing Stages helps categorize data status (from raw to AI-ready) and processing maturity, enabling systematic progression of data quality improvement tailored to complex use cases such as scientific AI.
Compared to software readiness testing, data readiness requires multi-dimensional evaluation of data properties, domain-specific quality criteria, and automated plus human-guided corrections embedded within pipelines. Using integrated tooling (e.g., APPFL's readiness framework) and following established best practices ensures data quality is continuously monitored and improved for targeted consumption use cases.
Data quality is a widespread issue across all industries, often due to rushing to address data quality without proper planning. The proposal is to leverage the data readiness approach to improve data quality by establishing specific data consumption context through a specific use case.
Cataloging a data readiness artifact could reduce repetitive data exploration and analysis, enable easy data auditing, and increase data accountability and trustworthiness. The data readiness artifact becomes a record of truth controlled by the corresponding data steward.
Re-assessment of data readiness is necessary when new use cases are discovered, similar to regression testing of software when new features are added or existing features are updated. This approach is similar to how software gets tested end-to-end to ensure readiness at each component/sub-system in the path to meet specific use case requirements.
In conclusion, the data readiness approach offers a promising solution to the data quality challenge. By focusing on data readiness for specific consumption use cases, we can ensure that data is usable, complete, reliable, trustworthy, and meaningful before insightful knowledge and intelligence can be extracted from it to support specific use cases.
Technology plays a crucial role in implementing the Data Readiness approach, as it relies on data-and-cloud computing to automate data assessment and proceed with targeted data adjustments. This technology allows for efficient evaluation of data quality metrics and customized rules, ensuring data readiness for various consumption tasks.
Moreover, the data readiness approach closely aligns with software testing methodologies, particularly in its focus on establishing specific use cases and continuously monitoring data quality throughout the consumption pipeline, just like software undergoes testing at each component and sub-system.
