Earlier this month we had this year’s magic quadrant for data quality tools from Gartner (the analyst firm). The magic quadrant always stirs up posts about data quality tools and this is true again this year. For example yours truly had a post here and Lorraine Lawson had a say on the ITBusinessEdge in the post Eight Questions to Ask Before Investing in Data Quality Tools.
Some of these questions asked by Lorraine relates to a grounding principle in the magic quadrant that is, that the data quality tool should be able to do everything data quality and even, as stated in Lorraine’s question 2: Can it be embedded into business process workflows or other technology-enabled programs or initiatives, such as MDM and analytics?
Thinking that question to the end inevitably makes you think about where data quality tools ends and where applications for different business processes, with data quality built in, takes over?
That question is close to me as I’m right now working with a tool for maintaining party master data with two main advantages:
- Making the business process as smooth as possible
- Ensuring data quality at pre data entry and all through the data lifetime
So, it’s not a true data quality tool. It doesn’t do everything data quality. It’s not a true MDM platform. It doesn’t do everything master data. But I would say that it does do what it does better than the full monty behemoths.
Liliendahl on Data Quality 
Henrik, great point at data quality tools. I am not a fan of too complex. I remmember there were data profiling tools as a separate tools, and data cleansing – it was the same. I agree, it is better to find tool which fits with your requirements, helps to solve what you need, etc.
I miss other tools related to the information quality management topic. Could be those tools a data quality too? I am not sure … But in general I miss tool allowing me to clearly and easy present something what Larry English calls – information value chain (IVC).
Kind regards,
MilanK.
Thanks for commenting Milan. Indeed support for infonomics, including such a thing as the IVC, could be a good niche that probably wouldn’t be handled the best by the mega-vendors but rather by best of breed suppliers.
You are right. As a consultant I really miss a few tools. It is not only above mentioned IVC but COQ system as well. I agree, it is not for a “mega – company”. In general I was not too happy by “integration” of so called data profiling and data cleansing tools. Companies has created a monster that supports an approach based on massive inspection and correction (data cleansing) – which is btw. against the quality management principles. But it was presented as an approach to make business processes more effective. I do not fully agree with theirs (DQ tool vendors) concepts.
For a consultants, like me, it was more important to keep data profiling tool as a separate and expand it for possibility to identify redundancies at record level, as well as possibility to check completeness from a point of different requests from business or decision processes.
I think we should more focus on information quality than on data quality. It is only my view on it.
Regards, MilanK.
Thanks again Milan. I wouldn’t rule out data quality completely.
First of all you can indeed have data quality without information quality, which then isn’t worth much, but you can’t have information quality without data quality.
Another often forgotten issue is automation. We do rely a lot on process automation these days, and automated processes really only works on quality data given the current state of artificial intelligence.