Jim Harris shows how data-driven businesses incorporate three aspects of data governance to guide their decisions.
Tag: operational data governance
Dylan Jones says spend time setting a vision of how to transform your data landscape – not debating definitions.
In my prior posts about operational data governance, I've suggested the need to embed data validation as an integral component of any data integration application. In my last post, we looked at an example of using a data quality audit report to ensure fidelity of the data integration processes for
Data integration teams often find themselves in the middle of discussions where the quality of their data outputs are called into question. Without proper governance procedures in place, though, it's hard to address these accusations in a reasonable way. Here's why.
In my last post, we explored the operational facet of data governance and data stewardship. We focused on the challenges of providing a scalable way to assess incoming data sources, identify data quality rules and define enforceable data quality policies. As the number of acquired data sources increases, it becomes
Data governance can encompass a wide spectrum of practices, many of which are focused on the development, documentation, approval and deployment of policies associated with data management and utilization. I distinguish the facet of “operational” data governance from the fully encompassed practice to specifically focus on the operational tasks for