Continuing the theme of my two previous posts, which discussed when it’s okay to call data quality as good as it needs to get and when perfect data quality is necessary, in this post I want to briefly discuss the costs — and profits — of poor data quality.
Loraine Lawson interviewed Ted Friedman of Gartner Research about “How to Measure the Cost of Data Quality Problems,” such as the costs associated with reduced productivity, redundancies, business processes breaking down because of data quality issues,regulatory compliance risks and lost business opportunities. David Loshin blogged about the challenge of estimating the cost of poor data quality, noting that many estimates, upon close examination, seem to rely exclusively on anecdotal evidence.
A recent Mental Floss article recounted “10 Very Costly Typos,” including the 1962 $80 million dollar missing hyphen in the programming code that led to the destruction of the Mariner 1 spacecraft, the 2007 Roswell, New Mexico car dealership promotion where instead of 1 out of 50,000 scratch lottery tickets revealing a $1,000 cash grand prize, all of the tickets were printed as grand-prize winners, which would have been a $50 million payout, but $250,000 in Walmart gift certificates were given out instead, and, more recently, the March 2013 typographical error in the price of pay-per-ride cards on 160,000 maps and posters that cost New York City’s Transportation Authority approximately $500,000.
Although we often only think about the costs of poor data quality, the article also shared some 2010 research performed by Harvard University claiming that Google profits an estimated $497 million dollars a year from people mistyping the names of popular websites and landing on typosquatter sites, which just happen to be conveniently littered with Google ads.
Poor data quality has also long played an important role in improving Google Search, where misspellings of search terms entered by users (and not just a spellchecker program) is leveraged by the algorithm providing the “Did you mean,” “Including results for,” and “Search instead for help” text displayed at the top of the first page of Google Search results.
What examples (or calculation methods) can you provide about either the costs or profits associated with poor data quality?
This post originally appeared at OCDQ Blog.
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