A Few Facts on the Cost of Poor Data Quality

In the ever growing world of big data, the benefits of implementing a strong data quality and data governance program become more apparent. Having poor data quality can cost a company major financial loss, as well as reputation. In numerous surveys, IT directors and program managers agree that poor data quality is a major obstacle in streamlining their company data. This can include inconsistent and redundant data, as well as inaccurate and conflicting data from various sources.

costs of poor data quality data ladder

With corporate data growing at a rate of about 60 percent per year (varying by industry), something must be done to combat the ever growing problem of poor data quality. Here are a few other numbers to ponder:

  1. Bad data costs the U.S. economy about $3 trillion per year — and what’s even worse is that it may be costing your company 10-25 percent of its revenue
  2. 15-45 percent of a company’s operating expenses are wasted due to poor data quality
  3. The average company wastes about $180,000 per year on direct mailings due to poor data quality

 

Pretty scary numbers! This is a pretty easy problem to resolve once you’ve identified what the issues are. Some common issues contributing to poor data quality include:

-Data originates from various sources

-Data gets combined with other data, breeding bad data

-Not correcting updated addresses and customer information

-Incorrect segmentation for marketing purposes

-Lack of standard processes for managing data within the organization

Implementing data standards at your company or organization can take the guesswork out of a messy data program. The key is to tackle these data problems before they grow and proliferate, so implementing a data profiling program can help eliminate a lot of poor data.

  1. Understand if your data is good or bad
  2. Use a data quality tool to provide some digging and deep analysis of your data
  3. Establish certain business rules and standards after a good understanding of your data has been accomplished
  4. Enact compliance policies to enforce practices and maintain good data quality

 

Interested in improving your data quality program? Learn about DataMatch today!