Automatic Data Quality with your Data Catalog using DvSum

You are using a data catalog to organize all the datasets in your organization in a centralized repository.

While you are able to discover, find and classify your data, you may also want to know what is the quality of the dataset you are using and monitor the same going forward. Data Quality Analysis helps when data changes unexpectedly, data pipelines fail, reports are delayed, inaccurate data shows up on reports, and hence it is a required component for enabling better and wider use of data in any organization

But the problem with many modern data catalogs is the lack of integration between data quality checks and the datasets to surface data quality information in the catalog

How does DvSum support this approach?

DvSum’s Intelligent Data Quality integrated with the Agile Data Catalog automatically figures out the right data quality rules for the extracted datasets and provides a summary view of the quality of the data with information on profiling results, rules and quality checks thereby providing a unified view of your data, its context and its quality.