As opposed to the traditional rigid schema model, where each use case must adapt to the ways of the model, DataOps provides self-service data analysis and data science solutions. Data consumers can analyze data and come up with new use cases for data-driven decisions.
The approach provides production-ready data and empowers consumers to become creative in effectively using the enterprise data without having to deal with complexities, such as finding data, quality, access, data integrity, difficulties with modern data management, and poorly-defined data.
DataOps aims to defeat data chaos by turning raw data into valuable and meaningful information. It brings the ability to infer relations among semantic objects across data silos and grants the capability to discover, analyze, and act upon data with ease.
Data consumers can use the robust search capabilities with the help of an extensive collection of metadata, data tagging, and data lineage driven by DataOps.