Quality standards that apply to the development. Software Lifecycle used Software Quality tool(s) and methods used.
The overarching goal of Data Warehouse Development Quality Plan is proactive problem-solving, where any anomalies or discrepancies are swiftly identified and rectified before they escalate into issues. Through continuous monitoring, analysis, and data observability, the reliability, accuracy, and accessibility of the data assets are maintained, thereby fostering trust and confidence in data-driven decision-making.
- Principles of Data Observability
| Principle | Activity | So that … |
| Freshness | Ensure that data is up-to-date and reflects the most recent state of the source systems | users can make decisions based on timely and accurate information, leading to more informed and effective actions. |
| Distribution | Monitor how data is spread across systems and locations to ensure that it falls within acceptable ranges and thresholds | potential issues such as data skew or imbalance can be identified and addressed promptly, maintaining data quality and integrity across the distributed environment. |
| Volume | Track the volume of data being ingested, processed, and stored | capacity planning and resource allocation can be optimised, preventing infrastructure overload or resource contention and maintaining efficient data processing. |
| Schema | Validate data schema consistency and evolution over time | data compatibility and interoperability are maintained, preventing errors and inconsistencies that could disrupt downstream processes or analyses. |
| Lineage | Capture and visualise the lineage of data, including its origins, transformations, and destinations | data provenance and impact analysis can be performed, enabling users to trace data back to its source and understand its journey through the data pipeline. |