You really shouldn’t worry about data quality. That is unless you’re concerned about losing customers, losing money, legal headaches and a multitude of other consequences. Sacrificing data quality is very likely to result in one or more of these outcomes. If none of these issues concern you, by all means, carry on as if data quality should not be a priority. If you’d like to avoid these business blunders, it’s worth it to implement a legitimate data quality program sooner rather than later.

Below, I’ve highlighted a few possible outcomes I recently wrote about in my ITWorld blog that can occur when data quality isn’t a priority:

  • Legal issues stemming from data integrity errors are all too common. The healthcare industry is littered with instances of litigation surrounding mistaken patient records, incorrect prescription medications, etc., which all too frequently complicate operations performance and patient satisfaction. A quick online search reveals numerous court cases against utility companies for overcharging customers due to bad data or inaccurate records leading to the wrong conclusions. And quite often these cases have other unintended consequences with immense negative impact on the customers, which can lead to costly court cases and damages paid by the utility providers. For example, Dish Network earned a reputation as one of the most notorious cable providers due to incorrect billing and paid $6 million in settlements.
  • Many Internet service providers have learned the hard way that failure to maintain customer information properly leads to obsolescence. When you fail to regularly verify email, the percentage of bad emails grows, especially when people change jobs. As this percentage reaches a certain threshold, many organizations will ban your emails due to high bounce back rates. Interestingly, the Supreme Court is considering a case that could make even search companies liable for incorrect information, regardless of how they acquired it. That would make data verification necessary for those who don’t already employ it.
  • The international trade and manufacturing industry is full of stories in which transfer of data causes a disparity in measurement units that leads to failures and cost overruns. One example that illustrated this confusion involved an American rice grower selling to a Japanese buyer. The price quoted was 39 cents per pound, but the buyer thought it was 39 cents/kilogram, making the actual cost much higher than expected. To foster long-term relations, the seller discounted the rice to just cover his costs. Meanwhile, the buyer was forced to sell the rice at no profit. Both parties lost money and face on the deal.

The examples are endless and they all lead in one direction: bad data sooner or later results in a serious loss. And the interesting aspect is that most IT and business managers leave this to chance, hoping that it will not occur. Most economists call this behavior “pitting hope over experience.” This is so because the likelihood of the bad outcome occurring is very high. Indeed, in most cases, it’s just a matter of time.