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The Real Cost of Bad Data: The 1-10-100

White Paper Published By: Melissa Data

What are the long-term costs of bad data? This white paper gives you the answers and tells you how to fix it.



Tags : 
melissa data, data quality, contact data verification, address verification, validate address, address verification software, address verify, address validation software

Melissa Data
Published:  Nov 18, 2008
Type:  White Paper
Length:  1 pages

The Real Cost of Bad Data: The 1-10-100 Rule By Bud Walker Customers are the foundation of your business. Ensuring that you have accurate information to reach them makes sense and will save you money. Imagine a scenario where you have many helpdesk personnel submitting address information from live callers to a database. Did you know that up to 20 percent of this contact data is flawed when saved? In fact, industry analysts have studied the long-term costs of inaccurate data entry. Their conclusions about the real cost of bad data are eye opening. They systematically studied costs from point-of-entry to the shipping of products, invoicing, and aggregation of customer statistics for data mining.. It was determined that it costs on average $1 to verify the contact information when it is originally submitted to the master database. This works as a safeguard and does not allow the address entry to be saved until it is parsed and compared against the U.S. Postal Service national database, thus ensuring clean addresses right from the start. This $1 includes the address validation solution, as well as the cost of the hourly employee, and the cost of running the computer equipment for each record. When the address solution was implemented in batch to cleanse and de-duplicate contact data after submission, it was calculated to cost around $10. The explanation is that some addresses were usually entered so malformed that no intelligent validation software could correct the address. These were simply dead entries and could not be counted as sales leads or billable customers. The experts also took into account the additional costs of computing time, transforming the data from proprietary formats, and the setup time to process each new batch. Finally, the cost of doing nothing was shown to be $100 per record. Due to misplaced shipments, returned mail and lost marketing opportunities, it simply costs too much money not to have a solution in place to verify, cleanse and guarantee that you have valid customer contacts. Another benefit of real-time address validation is to enhance or enrich your customer data. By appending valuable information like the ZIP+4, latitude and longitude, county name, FIPS, delivery points, carrier routes, MSA, PMSA, and more, you can pinpoint customers, send out targeted mail campaigns, and earn postal discounts. In addition you can mine important lifestyle and segmentation data to increase sales. The bottom line is this: It's cheaper to implement a cleansing solution at the beginning of your data collection chain. So budget for it as a necessary cost of doing business. Get a 45-day trial of the Data Quality Tools suite Visit www.melissadata.com/dbta Bud Walker is the Data Quality product manager for Melissa Data Corp. Disclaimer. The research described in the 1-10-100 Rule whitepaper relate to research that is experimental in nature and conducted by Melissa Data using input from customers and our extensive industry experience gathered over the last 25 years. It is not an official study from any outside organization rather it echoes the views of our own staff in terms of the actual cost of bad data. Therefore, notwithstanding anything that may be interpreted to the contrary within the article, Melissa Data shall have no duty or obligation to produce, achieve, or deliver any specific result or outcome from this article. ... [download for more]

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