One of the most frustrating questions I can hear as a data quality practitioner is simply two words; “so what?”
It’s not that it’s a difficult question to answer (although attributing costs and impacts can be challenging) it’s just that I can’t help but find this question at times a lazy and ignorant response to the raising of a data related issue. It often belittles a problem and inadvertently endorses a culture that is largely indifferent to data. Continue reading
As part of my current role I’ve been heavily involved in the definition, documentation, and implementation of data quality rules as part of a Solvency II (Solvency 2) programme. One of the big challenges of this activity is being able to systematically manage and categorise rules and I thought that it might be helpful for others undertaking similar activities if I were to share the source of the method I chose to employ.
I’ve been reading Danette McGilvray’s excellent book, Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information, in which she uses the analogy of a Doctor utilising a “quick fix” to treat a patient rather than undertaking a thorough assessment, to help establish the importance of root cause analysis. This somehow got me thinking about Dr Gregory House; the main character in the television series ‘House M.D.’. What triggered the thought was the obvious doctor connection, but I started to think more about the way that ‘House’ undertakes his assessments. Continue reading
I recently started a discussion topic over at DataQualityPro.com asking people to discuss how they have managed to achieve buy-in to data quality related initiatives. There have been some fantastic responses so I thought I’d take some time to summarise some of the emerging themes and throw in some others:
Just wanted to say a quick thanks to @dataqualitypro for making me their newest ‘featured member’. The interview is now up on the site here.
