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Indeed, it is fair to say that most organisations tend to only act to ensure data quality when they implement new systems, undergo a major update, when they need to integrate with another platform, or when something goes disastrously wrong.As a consequence, the accuracy of the information held in IT systems, even that in business-critical databases, will "drift" over time.Recently, Freeform Dynamics looked at data quality from the perspective of IT professionals and of line of business managers.Reasons for why data quality degrades over time include typing mistakes when editing records, software bugs or communications glitches introducing errors, unverified external data sources being used to update or supplement information, as well as data records not being updated at all due to pressure of work or simple oversight.

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The challenge is to make such processes both simple to undertake and cost effective.

Given that many of these factors are down to human error, there is an opportunity for automated IT solutions to mitigate or remove such errors and data degradation.

The consequences of poor data quality are not hard to find, and nearly all users, across all lines of business, recognise them.

Given the fact that data cleansing and integrity checking involves effort and, therefore, costs, managers should bear in mind that not all data is of equal business value.

Which data belongs in which category (and why) should be a business decision, not an IT one. Impure data is like impure blood – not good for the system.