The key takeaways from the presentation can be summarized as follows:
Importance of Data Quality
- Data quality is critical for virtually every data process and business decision in an organization.
- Poor data quality can lead to significant issues, including compliance failures, unhappy clients, and wasted time fixing data problems.
Vanguard's Data Ecosystem
- Vanguard has a federated, mesh-style data ecosystem with dozens of data products managed by different teams.
- Each data product has its own definition of data quality that needs to be enforced.
- Data quality checks are performed at multiple stages of the data workflow, including consistency, accuracy, completeness, and timeliness checks.
Vanguard's Custom Data Platform
- Vanguard built a custom data platform called G4 to manage data quality across their ecosystem.
- G4 is powered by AWS Glue Data Quality, leveraging its scalability, self-service capabilities, and integration with other AWS services.
- The platform has three main layers:
- Interaction layer: Where data stewards define data quality rules using DQL.
- Orchestration layer: Manages the execution of data quality checks at scale.
- Execution layer: Where the actual data quality checks are performed using AWS Glue.
Key Considerations
- Platforming data quality: Integrating data quality checks into operational processes and workflows, not just the technical implementation.
- Handling dynamic data: Splitting data into homogeneous sets to simplify rule authoring and maintenance.
- Detection vs. Prevention: Vanguard chose to prevent low-quality data from entering their data hubs, rather than just detecting issues after the fact.
Overall, the presentation showcases how Vanguard leveraged AWS Glue Data Quality as part of a custom data quality platform to address the complex data quality challenges in their federated, mesh-style data ecosystem.