The cost and business impact of poor data quality


The development of technology during the last decades has enabled organizations to collect and store enormous amounts of data. However, as the data volumes increase, the complexity of managing them increases.

Since larger and more complex information resources are being collected and managed in organizations today, the risk of poor data quality increases, this also implies the creation of ‘information silos’ in which data are redundantly stored, managed, and processed, creating a multitude of inconsistencies in data definitions, data formats, and data values, which makes it almost impossible to understand and use key data.

Poor quality data can directly or indirectly cause many negative economic and social impacts on an organization through less customer satisfaction, increased running costs, inefficient decision-making processes, lower performance, and lowered employee job satisfaction. Poor data quality also increases operational costs since time, and other resources are spent detecting and correcting errors.

Since data are created and used in all daily operations, data are critical inputs to almost all decisions. Data implicitly define common terms in an enterprise; data constitutes a significant contributor to organizational culture. Thus, poor data quality can have negative effects on the organizational culture. Poor data quality also means that it becomes difficult to build trust in the company data, implying a lack of user acceptance of any initiatives based on such data.

Many companies experience high costs due to poor quality data, although the exact extent of such costs is difficult to estimate. Studies to produce estimates of the total cost of poor data quality have proven difficult to perform. Additionally, data quality research has not yet advanced to having standard measurement methods for any of these issues.

However, according to studies conducted by industry experts, including Gartner Group, Price Waterhouse Coopers, and The Data Warehousing Institute, poor data quality broadly causes 8-12% of revenue loss and 40-60% of additional expense. This means that the economic effect of even small data inaccuracies can be very significant. Marginal data inaccuracies may not necessarily represent a major problem in manufacturing. Still, such inaccuracies will directly affect lost sales and operational disruptions in the after-sales organizations.

Facts and findings of poor data quality

Here we summarize some of the facts and key findings of the cost and business impact of poor quality data:

  • 88 percent of all data integration projects either fail completely or significantly over-run their budgets
  • Seventy-five percent of businesses have identified costs associated with inaccurate data.
  • Because of poor data, 33% of organizations have postponed or canceled new IT systems.
  • Poorly targeted mailings and staff overheads alone cost the United States $611 billion per year. Bad data, according to Gartner, is the leading cause of CRM system failure.
  • Only about half of companies say they are confident in the quality of their data.
  • Because dirty data causes many business intelligence (BI) projects to fail, BI-based business decisions must be based on clean data.
  • Only 15% of businesses have high confidence in the external data.
  • Customer information degrades at 2% per month or 25% annually.
  • Typically, businesses overestimate the quality of their data while underestimating the cost of errors.
  • Customer expectations, source systems, and compliance rules all change regularly. This must be reflected in data quality management systems.
  • Custom coding and traditional methods consume a significant amount of time and money to contain an immediate crisis rather than addressing the long-term issue.

Poor quality data can hurt the health of a company. If not identified and corrected early on, defective data can contaminate all downstream systems and information assets, jacking up costs, jeopardizing customer relationships, and causing imprecise forecasts and poor decisions.

The Data Warehousing Institute (TDWI) estimates that poor quality customer data costs US businesses $611 billion in postage, printing, and staff overhead. Frighteningly, the real cost of poor-quality data is much higher.

Organizations can frustrate loyal customers by incorrectly addressing letters or failing to recognize them when they call or visit a store or website. Once a company loses its loyal customers, it loses its base of sales and referrals and future revenue potential.