The Data Roundtable
A community of data management experts
.@philsimon offers his definition of the term.
Bigger doesn’t always mean better. And that’s often the case with big data. Your data quality (DQ) problem – no denial, please – often only magnifies when you get bigger data sets. Having more unstructured data adds another level of complexity. The need for data quality on Hadoop is shown by user
Yes. For those keeping score at home, this is my second post in a row starting with a one-word answer to its questioning title. In this case, it’s a question that’s asked a lot and for good reason since big data raises big questions for all data-related disciplines.
The data governance “industry” thrives on a curious dichotomy. On the one hand, some service providers insist to clients that they need a data governance program, that they must create a data governance council and that they should immediately staff a collection of roles ranging from data governance council member
Explaining data governance to a business community is difficult. Even more so when you need to convince business folks that they are pivotal to data governance success. Data governance demands not just business attention but business commitment. Policies and processes are not just tick boxes on a corporate charter; they are
Operationalizing data governance means putting processes and tools in place for defining, enforcing and reporting on compliance with data quality and validation standards. There is a life cycle associated with a data policy, which is typically motivated by an externally mandated business policy or expectation, such as regulatory compliance.