I recently experienced a data-quality problem first-hand while on the phone with the company that books my family a condo for our annual ski vacation. They couldn’t find my customer data, despite the fact that I had been a customer for years. Eventually, we figured out that they were searching under “Richard” but had me in their system as “Rick.”
Not really a big deal. This was just a minor annoyance and no revenue was lost. But when these kinds of name and address cleansing problems crop up in other instances (such as with banks), it can be much more than a hassle.
For the financial and retail firms I have worked with, data quality can be seen as either a glass half full or a glass half empty. There is a heavy cost when they annoy or outright lose customers because they can’t get all their account information correct. It’s even more embarrassing to the firm when they cannot even get their customers’ names correct.
Further, the financial firm loses an opportunity to up-sell and cross-sell products or services because they don’t know who (from a financial perspective) they are talking to.
Regulatory compliance is another area where the data-quality stakes are high. Dirty data can really muddy up a company’s attempt at real-time disclosure and puts the CFO at high risk when signing off on financial reports and even press releases based on incorrect information. And the consequences for the CFO are not just an embarrassing press release or an apology -- legal action is possible.
Public companies reporting financials and those dealing directly with customers are just part of the picture. Just about any company, of any size, needs to operate as efficiently as possible. Try doing that with business data that isn’t consistent across the company! How can teams collaborate when they’re not even looking at the same information? Management meetings break down into arguments about whose number is correct rather than how to improve customer satisfaction, increase sales or improve profits. Many companies are trying to implement performance management systems but how can that happen with dirty data? It can’t…garbage in, garbage out.
Without an Enterprise Data Management (EDM) program, data-quality issues occur across an enterprise and impose serious costs. IT and business power users often focus a lot of attention on the latest and greatest BI tools that have been bought and rolled out in an enterprise. But the greatest BI tool in the world won’t help if the data is dirty. It may not be as much fun as working with the shiniest gadget, but if you want real ROI from your IT investments, then start with implementing an EDM solution. That’s real business value.
What are some business issues you’ve seen when data quality goes awry?