Data quality does not matter if you are not afraid of litigation, lost customers, lost revenues, hidden costs, wrong decisions, and many other possible consequences. If you are not afraid or have not considered any of these issues, data quality is clearly not a priority in your organization. The reality, however, is that ignoring data quality is likely to produce one or more of the above consequences. And once it happens you will pay the cost associated with it plus the cost of implementing a proper data quality program.
Let’s look briefly at a few of the potential issues that can arise when data quality is ignored:
- Litigation due to data errors is quite common. Healthcare is full of horror stories of mistaken patient records, incorrect prescription medications, etc., which all too frequently have fatal results. But this problem is not unique to healthcare. A quick search shows numerous court cases against utility companies for overcharging customers due to bad data or incorrect records. And quite often such cases have other unintended consequences with huge negative impact on the customers, which leads to costly court cases and damages for which the utilities become liable. For example, Dish Network earned a reputation as one of the most hated companies due to incorrect billing and paid $6 million to settle allegations.
- Many Internet companies have learned the lesson 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 a must have for those who don’t believe in it already.
- International trade and manufacturing is full of stories in which transfer of data causes a mismatch of measurement units that leads to failures and cost overruns. One such story illustrated the confusion that arose when an American rice grower sold to a Japanese buyer. The price quoted was 39 cents/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.
I can expand the list of cases easily. But this is not necessary, as the underlying cause is the same and so is the effect. 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. In most cases it’s just a matter of time.
Hence my question: Why do so many organizations ignore data quality? My hope is that the emerging CDO (Chief Data officer) role will change all of this. I will explore this topic further in my next blog.
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