Jim Harris says curating AI’s curriculum is the responsibility of data stewards.
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 […]
The post How SAS supports the four pillars of a data quality initiative appeared first on The Data Roundtable.
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 […]
The post Harmonizing semantics for consistency in interpreting analytical results appeared first on The Data Roundtable.
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 […]
The post Operational data governance: Policy vs. procedure for data validation appeared first on The Data Roundtable.
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 […]
The post Data governance: The perfect marriage of soft and hard skills? appeared first on The Data Roundtable.
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 […]
The post Data validation as an operational data governance best practice, Part 1 appeared first on The Data Roundtable.
Master data management (MDM) is distinct from other data management disciplines due to its primary focus on giving the enterprise a single view of the master data that represents key business entities, such as parties, products, locations and assets. MDM achieves this by standardizing, matching and consolidating common data elements across traditional and big […]