The use of analytics in healthcare is on the rise with the emergence of big data, but it requires accurate and complete data. According to research firm TDWI, many businesses have issues with the quality of their data because it is incomplete, incorrect, inaccurate and inconsistent. These errors are often due to user mistakes including duplicate entries, misspelling, wrong punctuation and missing information. This “dirty data” decreases the ability of healthcare organizations to utilize business intelligence and analytics platforms to accurately analyze data and measure results.
Though dirty data is common due to misleading, non-integrated and invalid data, healthcare organizations are especially plagued by it because of a lack of standardized product information. Dirty data in healthcare also is generated by a lack of generalized formatting, problems incurred during data migration processes, overlaps and overlays and even identity fraud, resulting in unreliable data and a reduced quality of patient care.