The Challenges of Data Transparency
In the recent Federal Computer Week article Stimulus spenders race to the finish line, Alice Lipowicz reported on the efforts of state and local recipients to prepare detailed reports on how they are spending federal stimulus money.
Among the common concerns cited in the article was dealing with the sheer volume of data, and more importantly, the quality of the data.
“Transparency advocates predict that data quality,” reports Lipowicz, “will be as much of a concern at Recovery.gov as it is at USAspending.gov, which Congress established in 2006 to provide visibility into federal spending. That site has been plagued with problems such as errors, missing data and mislabeled data.”
Data transparency is definitely a laudable goal, and not just for the government. Organizations in every industry and of every size need to do a better job of making available for review, the data that was used to drive critical business decisions, especially financial decisions.
Missing or incomplete data is a common problem, but transparency can not simply mean a massive dump of all available data.
Completeness without any regard for accuracy could possibly do more harm than good. Data frequently contains numerous variations caused by different conventions, lack of standards, omissions, and other inconsistencies.
An excellent question raised in the article was:
“Data quality has been a problem for years, so why do we keep getting [more data] instead of addressing these priorities?”
I think that this question represents one of the most significant challenges for data transparency.
Should data be concealed until it has been verified to be of sufficient quality? Or should data be provided as soon as it becomes available without regard for quality?
Please share your thoughts.
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