Everyone from communities to executives is embracing the importance of data quality, and so are we here at Secoda. We know that data enablement isn’t complete without understanding the quality of the data being ingested and generated.
That’s why Secoda users can now integrate Great Expectations with their Secoda workspace.
Why data quality and data enablement
Data enablement doesn’t stop at data assets—sharing the quality of those assets is just as important in bridging the gap between analytics and business teams to build trust.
We recently published a blog on what data enablement is and why it’s the next step in the evolution of data catalogs. Cataloging data isn’t tool-specific—it involves writing descriptions for data tables and columns. What a catalog is missing, however, is integrations, context, and easy collaboration.
A huge component of this context should include data quality. A data catalog that doesn’t account for data quality is like a Yelp page without reviews. How can you trust a business does what it says it does? How do you ensure you’ll get a good experience? Adding context isn’t inherently part of cataloging. Secoda, as a data enablement tool, allows its customers to search and share data knowledge. However, sharing this knowledge would be counterproductive if the quality of the data itself wasn’t ensured.
Data quality tools shed light on what business users should expect of their data and when those expectations aren’t met. Integrating data quality into data-sharing tools gives users the ability to automatically unpublish data assets that aren’t passing quality tests.
Great Expectations in action
Great Expectations is a data validation framework and platform, providing the guard rails to write succinct tests (or “Expectations”) for data. These Expectations are then validated (in “validations”) as part of data pipelines, stopping downstream dependencies from updating if the data validation doesn’t meet expectations like exceeding null count or value range.
In Secoda, the Expectations coded within Great Expectations are associated with their respective tables. In the table view, you’ll see which validations are currently passing or failing.
Analytics teams benefit from a concise view of the state of all data, while business stakeholders gain trust in the fact that data is tested and understood. If any tests fail, the lineage view provides insight into what may have caused the failure.
Data quality is just another piece of the puzzle when it comes to understanding your data.
What’s next for Secoda
On the roadmap is expanding data quality integrations with other tools customers use like Metaplane, Bigeye, and Datafold. We believe high visibility into data quality will push organizations forward in building data trust and increasing collaboration.
For Secoda users, you’ll find instructions to connect to Great Expectations in your dashboard.
If you’d like to bring data quality and documentation under one platform, try Secoda for free.