Data quality issues are like back pains…everyone has to deal with them at one point or another, and in many cases it requires some major surgery. The strategic use of data is critical, and is one of those things you have to put into hard dollars before people begin to take data quality seriously. Understanding the true cost of data quality issues provides a solid foundation of understanding around the business case for putting a good data quality program and core enabling technology in place to solve this problem.

There are two costs to consider: Operational inefficiencies and long-term consequences.

Operational inefficiencies refers to the issues around data quality that organizations must deal with everyday, such as shipments going to the wrong or a non-existent address, or calling a customer by the wrong name during a cold call. While these seem like minor problems, they typically add up to thousands and sometimes hundreds of thousands of dollars for the Global 2000, when we track the costs of all inefficiencies that data quality issues have cost the business.

To determine the cost, typically you need to mine the existing operational data for patterns of inefficiency around data quality, and place a price on each instance. For example, I had a client who knew they had a “few data quality issues” around shipment errors, although they had no idea that the sum of all shipping issues caused by lack of data quality reached just under one million dollars within a single year. But, there are other soft issues to consider here as well.

Long-term consequences refer to the issues around data quality that hurt the existing and future business. Considering the same client, they also had no idea that the shipping issues caused by lack of data quality, including no data quality program and data quality technology, actually lead many customers to seek other suppliers. Indeed, when a customer satisfaction survey was completed by an outside marketing organization, it was determined that the shipping issues actually cost the company several larger customers which added up to four million dollars in lost revenue over a three year period of time.

The problem with long-term consequences or poor data quality is that they are not always obvious. The symptoms are typically poor customer satisfaction, which carries a huge price tag, as well as poor employee morale, which has a large cost as well. These typically escape the metrics that many businesses consider, and it’s been my job to help them focus on finding issues with data, and resolving them to a typically positive effect.

Data quality is important, more so than most understand. What’s troubling about this is that many of those issues are easily solvable, if you have the right plan, and the right technology.