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FEB 27, 2012 9:09am ET

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Data Quality: Quo Vadimus?

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Over the past week, an excellent meme has been making its way around the data quality blogosphere.  It all started, as many of the best data quality blogging memes do, with a post written by Henrik Liliendahl Sørensen.

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Comments (4)
I like the short journey metaphor. We need targeted goals and ongong commitment, but beyond seting standards for our quality of work, we can't determine where market dynamics will require our attention past a year or two. Even annual IT budgets are becoming harder to manage when demand for information becomes so distributed and elastic.
Posted by Jim E | Tuesday, February 28 2012 at 12:22PM ET
Thanks for your comment, Jim.

Yes, data quality, just like every other aspect of the constantly changing business world, requires a continuous monitoring and continuous improvement mindset. We can't predict what will happen tomorrow, which is precisely why we can't plan and prepare as if we could. We have to follow what works for as long as it works without being afraid to adjust as necessary when circumstances inevitably change.

Best Regards,

Jim

Posted by Jim H | Tuesday, February 28 2012 at 4:41PM ET
Business succeed (or at least stay afloat) despite mismanagement, corruption, and other inefficiencies all the time. Seems like everything is a "journey" if it just means the business works at making things better. The only real "destination" might be selling it off or going public - letting the next person/company continue that journey.
Posted by Robert W | Wednesday, February 29 2012 at 11:03AM ET
I think this is best summed up as 'Survival of the fit' as opposed to 'survival of the fittest' as far as data quality is concerned. Also given the high cost of data quality solutions from a traditional IT point of view keeps business and IT execs from wanting to tackle the problem. Since we knew that there was resistance to data quality initiatives on our current ERP data we started with our external sales/marketing leads data. Deduplicating leads, cross referencing current customer with large database lists to identify good prospects. We found the effort paid for itself in a day but more importantly convinced the doers and leaders that data quality is important. Note we didn't use one of the huge providers to start this off but a company called data ladder which was cheap enough to get things moving with no big approvals needed.

data quality tools

data ladder

Posted by Jon B | Wednesday, April 04 2012 at 8:21AM ET
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