So, how do we actually implement a data observability framework that can improve our end-to-end data quality? What metrics should we be tracking at each stage?
Here are the ingredients to a high-functioning data observability framework:
- DataOps Culture
- Standardized Data Platform
- Unified Data Observability Platform
Before you can even think about producing a high-value data product, you need mass adoption of the DataOps Culture. You need everyone bought into this, but you especially need leadership bought in. They are the ones that dictate the systems and processes for development, maintenance, and feedback. As powerful as a bottom-up movement can be, you need budget approvals to make the technological changes needed to support a DataOps system.
Once everyone is bought into the idea of being efficient, leadership can move the organization toward a standardized data platform. What do we mean by that? To get end-to-end ownership and accountability across all teams, you need infrastructure in place that will allow teams to speak the same language and openly communicate about issues. That means you need standardized libraries for API & data management (i.e., querying data warehouse, read/write from data lake, pulling data from APIs, etc.). You need a standardized library for data quality. You need source code tracking, data versioning, and CI/CD processes.
With that, your infrastructure is set up for success. Now you need a unified observability platform that gives your entire organization open access to your system health. This observability platform would act as a centralized metadata repository. It would encompass all the features listed earlier (like monitoring, alerting, tracking, comparison, and analysis) so data teams could get an end-to-end view of how the sections of the platform they own are affecting other sections.