I'm always on the look-out for examples of the costs associated with bad data management. The need for data management is pretty widely accepted these days, but it always helps to be able to put some figures to the risks associated with dirty data. Here's a good one:
“In 2014, the USPS published a report on their internal audit of Undeliverable as Addressed (UAA) mail. Of the 158 billion pieces of mail processed in FY13 almost 4.3% was classified as UAA, which totaled 6.8 billion. The total cost of processing these 6.8 billion pieces of mail was $1.5 billion comprised of costs associated with forwarding mail, returning mail, general waste and administrative costs. In other words, $1.5 billion of your tax money is wasted because of five attributes with incorrect data: name, address, city, state, and zip code. Extrapolate this example across the tens of thousands of medium and large-sized businesses in the US and the actual cost of poor data quality for US-based organizations is in the billions and with some have speculating it could be as high as several trillions of dollars per year.”
That's some punchline – several trillions of dollars per year. Notice also how the five data items mentioned here have their analogs in the core of every business: Items like customer names, account numbers, and any number of key production metrics are all likely candidates for dirty – and mismatched – data. Snail mail isn't sexy, but the mail makes a fine, neutral model for any data-driven business. The tide of big data – most of it unstandardized – is a disparate flow, like traditional mail. At least mail users are supposed to know about zip codes – many of the new data sources which organizations are using today have no allegiance to any recognized standards.
Companies save time and money when their data is clean. They can only have clean data across the enterprise when they have data standards. Imagine how much it would cost to process snail mail if the zip code hadn't been invented and implemented. This is what some would-be data-driven organizations are facing. They need standards – fast. AIM Consulting