Jim Harris says curating AI’s curriculum is the responsibility of data stewards.
Tag: data stewardship
Jim Harris says data stewards are essential to analytics, providing life cycle management for data across the enterprise.
Joyce Norris-Montanari cites five things she considers essential to data management for analytics.
Data quality initiatives challenge organizations because the discipline encompasses so many issues, approaches and tools. Across the board, there are four main activity areas – or pillars – that underlie any successful data quality initiative. Let’s look at what each pillar means, then consider the benefits SAS Data Management brings
One aspect of high-quality information is consistency. We often think about consistency in terms of consistent values. A large portion of the effort expended on “data quality dimensions” essentially focuses on data value consistency. For example, when we describe accuracy, what we often mean is consistency with a defined source
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 governance has been the topic of many of the recent posts here on the Data Roundtable. And rightfully so, since data governance plays such an integral role in the success of many enterprise information initiatives – such as data quality, master data management and analytics. These posts can help you prepare for discussing
We've witnessed a significant rise in data governance adoption in recent years. Careers, technology, education, frameworks, practitioners – there's growth in all aspects of the discipline. Regulatory compliance across many sectors is a typical driver for data governance. But I also believe one of the main reasons is the realisation by
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