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The product concept back then went something like: In a world where enterprises have numerous sources of data, let’s make a thing that helps people find the best data asset to answer their question based on what other users were using. And to determine “best,” we’d ingest log files and leverage machinelearning.
This “analysis” is made possible in large part through machinelearning (ML); the patterns and connections ML detects are then served to the data catalog (and other tools), which these tools leverage to make people- and machine-facing recommendations about data management and data integrations.
Cloud migration. For example, the researching buyer may seek a catalog that scores 6 for governance, 10 for self-service, 4 for clouddata migration, and 2 for DataOps (let’s call this a {6, 10, 4, 2} profile). Finally, one catalog can operationalize data governance more effectively.
DataOps sprung up to connect data sources to data consumers. The data warehouse and analytical data stores moved to the cloud and disaggregated into the data mesh. And now with some of these clouddata warehouses becoming such behemoths, everything is getting centralized again.
Read Here are the top data trends our experts see for 2023 and beyond. DataOps Delivers Continuous Improvement and Value In IDC’s spotlight report, Improving Data Integrity and Trust through Transparency and Enrichment , Research Director Stewart Bond highlights the advent of DataOps as a distinct discipline.
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