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INSIGHT: The incurable problem with data fitness

INSIGHT: The incurable problem with data fitness

"We should all recognise that most organisations get by quite nicely, thank you very much, with pretty crappy data."

Through reading an end-user's strategy document for their upcoming Master Data Management (MDM) program, Andrew White, analyst, Gartner, was taken aback.

As part of the strategy, White observed a significant technology decision – to go for a packaged solution from any number of software vendors, or to develop their own custom or bespoke solution.

There was a page listing a number of design assumptions for the custom/bespoke approach.

"One stood out for me," White recalls, "it was this..."

This decision will introduce an inhibitor to the end to end solution.

"The “this” in “this decision” is in fact the idea that processes and rules will be enforced as data quality issues are identified that would otherwise impact the effective use of information and its fitness for purposes," White explains.

"Yes, the author of this assumption feels that by inserting a process to resolve business issues in data quality at source, the “end to end process” would in some way be inhibited."

Questioning the assumption, White's response was simply; "Really? Where is the finding that says..."

“By not addressing our poorly governed business processes, poor quality data bites us in the xxx some undetermined period of time later and thus processes are more randomly inhibited and with greater impact?

"We should all recognise that most organisations get by quite nicely, thank you very much, with pretty crappy data," White adds.

So much so that White believes this problem – of poor quality and low fitness of data – is not new nor will it ever go away.

"But to blindly assume that any process designed to resolve those issues at source is going to be worse than doing nothing is clearly not in the business of selling business process improvement," he claims.

"There is a trade off – I admit. So why not simulate the business process “as is” and “as it could be” and evaluate with real business metrics and outcomes?"

White's anecdotal perspective suggests that 95 times in a 100 a process to trap issues at source is less disruptive to aggregate business outcome.

"In fact it will more likely improve business outcomes," he adds.


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