ROT, Dirty, Dark, Garbage – Is it worth 12% of your revenues?
Creeping into the IT news lately has been the subject of cleaning up data, either archiving or deleting it. Not sure why this is such a hot topic as one would have thought that most organizations were doing this all along. Not so, it seems. Even in migration scenarios, many organizations don’t have any issue with just moving it, without even knowing what it is.
As a software vendor we are not in the business getting rid of corporate garbage, only to the extent that we can assist in the identification of the value of the content by understanding what it is about, eliminate dupes, and identify records or security breaches. But this is not a vendor pitch. I recently read an article by Data Mentors, entitled ‘How Much is Dirty Data Costing You? I found the article fascinating as it quantified the business cost of data that is somehow compromised, or absent. Did you know on average, every 30 minutes 120 business addresses change, 75 phone numbers change, 20 CEOs leave their jobs, and 30 new businesses are formed. (Source: D&B The Sales and Marketing Institute)?
What would open the eyes of any C-level manager is the fact that inaccurate data has a direct impact on the bottom line, with the average company losing 12% of its revenue. That should be a wake-up call. What are some examples of dirty data? The following would almost make you chuckle, if unfortunately they weren’t true.
- Retail company found over 1 million records contained home telephone number of “000000000” and addresses containing flight numbers
- Insurance company found customer records with 99/99/99 in creation date field of policy
- Car rental company discovered duplicate agreement numbers in their European data warehouse
- Healthcare company found 9 different values in gender field
- Food/Beverage retail chain found the same product was their No 1 and No 2 best sellers across their business
- An international bank could not meet its customer satisfaction goals because agents in its 23 contact centers all followed different operational processes, using up to 18 different apps — many of which contained duplicate data — to serve a single customer
Recognize your company in any of the above? Do you have processes and procedures to ensure appropriate lifecycle management for data, both structured and unstructured?