In today’s “Big Data” world most business decisions are data driven but the smart and accurate decisions start with clean data.
Data cleansing or data cleaning is the process of detecting and correcting (or removing) corrupt or inaccurate records from a record set, table, or database and refers to identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing, modifying, or deleting the dirty or coarse data. (wikipedia)
Having clean data is important in every department across B2C and B2B organizations especially in marketing. Some of those multiple data points are:
- Address Standardization
- Address Correction and National Change of Address
- Email Validation
- Phone Type Validation (Mobile vs Voip vs LandLine vs Company # vs Direct)
- Missing or incomplete data
Bad data can be worse than no data and lead to problems with cost, wasted employee productivity, and customer distrust. For example:
- Poor data quality is also hitting organizations where it hurts – to the tune of $15 million as the average annual financial cost (Gartner)
- 77 percent of companies believe their bottom line is affected by inaccurate and incomplete contact data and on average, respondents believe 12 percent of revenue is wasted. (Experian survey)
- Bad Data Costs the U.S. $3 Trillion Per Year (Harvard Business Review)
With the increasing volumes of data, data sources, and technology stacks housing siloed data coupled with the increasing need to make better and more informed data-driven decisions it is becoming paramount that organizations stay on top of their data hygiene.