By Bernice Purcell, DBA
Associate Dean and Professor
School of Business Administration
Have you ever played whisper down the lane? That’s the children’s game where someone whispers a sentence to a second person, who whispers it to a third, and so on, until the last person hears it. When the last person says the sentence out loud, it is typically not the same thing that the first person said.
It’s no secret that everyone has a lot of data out there, and it is being used to make decisions about us. Hiring decisions, credit decisions, and health care decisions are all made based upon the data that the decision maker has access to. However, a Harvard Business Review survey a few years ago indicated that only 16 percent of the managers surveyed were strongly confident of the data they were using to make their decisions.1
Data quality has been defined as “reliance on accuracy, consistency and completeness of data to be useful across the enterprise.”2 According to Michelle Knight, characteristics of data quality include completeness, integrity, reasonability, and validity. A recent global trends report indicates that poor data quality is one of the three top data concerns of the companies surveyed. Of these companies, 98 percent used data to improve customer experience.3
Currently, there is an international trend in legislation that will combat poor data quality and other data concerns. The most widely known is the General Data Protection Regulation (GDPR), but similar laws and regulations are being proposed or examined in Australia, Brazil, Canada, and even some states in the USA like California. One of the common provisions in these laws or regulations deals with data quality. Even without mandated regulation, companies strive to improve data quality to improve decision making. The increasing importance of corporate data governance is a response to data quality concerns.
There are some steps that individuals can take to improve data quality. If you want companies to make accurate financial decisions about you (particularly financially), check your credit report. Common data errors in credit reports include incorrect personal data, accounts not belonging to you, closed accounts reported as open, and duplicate accounts; the latter two directly have an adverse effect on your credit score.4 Similarly, doctors’ offices are now required to keep health record data electronically, and many allow patient access to their personal health records through a secure online portal. If you have such access, check to make sure your data is accurate so your doctor can make the best possible health care recommendations.
Working together, we can all do our part to make sure that Whisper Down the Lane remains a pleasant little child’s game. We don’t want to be playing whisper down the lane with our data.
 Redmond, T. (2016). Getting in front of data. Technics Publications; Basking Ridge, NJ.
 Knight, M. (2017, November 20). “What is data quality?” Dataversity. Retrieved from https://www.dataversity.net/what-is-data-quality/
 Anon. “2019 Global data management research benchmark report: Taking control in the digital age.” Experian. Retrieved from https://www.edq.com/globalassets/white-papers/2019-global-data-management-benchmark-research.pdf
 Fralick, K. (2018, September 12). “6 Common credit report errors to look out for,” The Motley Fool. Retrieved from https://www.fool.com/investing/2018/09/12/6-common-credit-report-errors-to-look-out-for.aspx