Imagine if you owned a restaurant, and you found out that about 10% of customer checks didn't match up with orders placed through the kitchen. You'd quickly ask tough questions: Is someone stealing money? Are customers being cheated? What's causing the errors? After a quick assessment you would take quick action to correct the problem and make sure it never happens again.

Strangely, that kind of awareness of data quality doesn't seem to scale up to large organizations. When data management teams contact CapTech for help, they routinely recount challenges in funding data quality work. They ask for simple, direct examples showing tangible business benefit from improving data quality.

Here are three of our favorites:

  • A Fortune 500 financial corporation supported an antiquated HR system. Due to the difficulty of enhancing the existing system many manual workarounds with poor controls had evolved, increasing data inconsistency and errors. Previous business cases to replace the HR system had been rejected because they relied on "intangible" benefits that, although obvious to HR staff, failed in the boardroom. CapTech led a requirements team that studied HR business processes and quantified savings due to data quality improvement at 11,200 work hours per year. Elements of the case included reduction in HR internal audit, data validation, and data correction costs; ability to apply best business practices in recruiting, compensation, and benefits; and reduction of cost of compliance with new regulations.
  • A state motor vehicle authority needed tangible ROI estimates for fixing the top data quality issues to justify a data quality project with key stakeholders. The CapTech team conducted a study of current data quality problems and found projected savings of about $300,000 per year. Problems found included simple keying errors but also problems with systems, including one in which only last name was used to search offender records after traffic stops, reducing the reliability and usefulness of the data. We found that correcting these problems would result in substantial intangible benefits to citizens, eliminate the need for manual correction and re-entry of duplicate records, and reduce duplicate correspondence and other customer service costs
  • We participated in a study of data quality in the data warehouse of a national electronics retailer, and found that rapid data warehouse growth had contributed to a perception of poor data quality, but with improvement of validations on incoming data reduced time spent on manual data verification by 20%, a substantial savings. In addition the increased confidence in the data after making the recommended improvements resulted in more use of warehouse-based reporting versus other alternatives, and better quality awareness that improved data warehouse development practices.

Companies perceive difficulty in justifying the cost of data quality initiatives, but they commonly lose money by not undertaking them. A close review of areas affected by data quality problems often reveals hidden ongoing costs in error correction or missed profit opportunities. Moreover, enterprises are held back from new projects because of the liability of poor data quality. CIOs who undergo data quality remediation spend in the short term to save and profit in the long term. Much like restaurants, enterprises that don't match the orders going out of the kitchen against customer checks are losing more than they may realize.