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.
