Renee N. Saris-Baglama, Ph.D. and P. Allison Minugh, Ph.D.
It’s a dirty little secret that can cost you time, money, lost opportunity, and your reputation: dirty data. Dirty data include data that are missing, invalid, inaccurate, or inconsistent. According to Gartner, a leading IT research and advisory company, over 25% of critical data in top companies are dirty and businesses often underestimate the size of the problem. But it’s not just businesses that contend with the consequences of dirty data. Just as poor data quality hurts the business sector, poor data quality hurts researchers, too. It can call into question the scientific rigor of a study result and misguide decision-makers who rely on these data.
Given the scope of this problem and its consequences, you may want to ask yourself: When was the last time I performed a data quality assessment? A data quality assessment involves a data audit to determine data strengths and weaknesses. The results and recommended actions to improve data quality are documented. Datacorp has conducted nationwide data quality assessments that have resulted in substantial data quality improvements for its clients. We employ standardized practices and automated tools to document, clean, and manage data that result in high quality, transparent data sets that can be trusted.
Contact us at firstname.lastname@example.org to learn how we can help you improve your data!
Gartner Inc. (2007). ‘Dirty Data’ is a Business Problem, Not an IT Problem, Says Gartner [Press Release]. http://www.gartner.com/it/page.jsp?id=501733
Kim, W., Choi, B., Hong, E., Kim, S., & Lee, D. (2003). A taxonomy of dirty data. Data Mining and Knowledge Discovery, 7, 81-99.