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The real cost of bad data

It is a well-established fact in software delivery that the earlier in the life cycle you can find and fix an issue, the less costly it is to remediate.

In the world of data, inaccurate inputs can cost a company not only time to repair, but it can mean lost revenue due to the inability to reach customers and prospects.

A recent paper from data quality company Melissa used an example of helpdesk workers all submitting address information from live callers into a database, and found that as much as 20% of the contact data is flawed when it is saved.

Industry analysts have found that verifying the accuracy of data when it first enters the master database is one dollar. Using ‘address’ as an example, the system will compare the input to the national U.S. Postal Service and not allow it to be saved if it is not a match. (The $1 cost is broken down as the cost of the validation solution, the cost of the worker, and the cost of running the computer equipment for each record.)

If, though, the address solution is implemented in batch, the cost rises to $10. The bad data made it this far because it was so badly formed that the validation software could not correct it.

Finally, organizations incur a greater cost if they have no mechanisms in place to validate data entry. This results in costs incurred due to misplaced shipments, returned mail and lost opportunities for the business. At this stage, the cost is $100 per record – and that cost is too high to not have a data verification and cleansing solution.

Yet address validation is not simply a cost – it can be a revenue generator. Using an address service can provide valuable information beyond the simple address that enables organizations to more easily mine better data and target specific segments of the database.

Content provided by SD Times and Melissa

 

The post The real cost of bad data appeared first on SD Times.



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