Category: Data Management
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Coming soon: data like money
It is a commonplace to say we should manage data like a resource. But when you think about it, data is an asset but not a resource. Data isn’t a thing like real estate, employees, or customers, but rather it represents all of those things. In data-geek-speak, data is a meta-resource that holds information about…
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Study data early to improve application alignment
A recurring theme in the literature on IT over the years has been frequent failure of IT projects. Most studies lay the bulk of the blame on requirements (examples here and here). One way to improve accuracy and fit-to-purpose of requirements, and thereby promote project success, is to include data analysis as well as process…
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DQ, he isn’t so dumb he just needs glasses
In a recent very thoughtful post on data quality, Paul Erb plays out an analogy comparing data users with Don Quixote and data quality professionals with Sancho Panza, then reverses the analogy to cleverly coin the “Sancho Panza” test of data quality professionals. He encourages data quality professionals promoting the critical role of data quality…
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No business value in nulls
It seems I’m frequently in conversations about using null to represent a business value. To paraphrase, say there are credit and cash customers, and there’s a suggestion to set “Customer_Type” to “C” for credit and null for cash. To data and database professionals this is obviously a bad idea, but it’s not obvious from a…
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Someone’s integrating your data
Here’s a little-recognized fact about data integration: if you run a business or any sizable chunk of one, someone is integrating your data. In my professional life I have on occasion suggested data integration efforts. Sometimes my suggestions have been accepted and sometimes not. As an IT professional I understand that different managers have different…
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Beware the devils in the details of data integration
Much of today’s IT application development – custom or off-the-shelf – involves integrating data from legacy systems, third- party software products and external data sources such as demographics or mail lists. More often than not, data integration is unexpectedly complex, either due to data quality issues or the nature of the data integration itself.