Another Calculation Method – The 1 – 10 – 100 Method

I have previously described the 1-in 10 rule.  In the article “The real Cost of Bad Data”  it is described how industry analysts had made the 1-10-100 Method.

The average cost of correct entered contact information into the master database cost
$1 is includes data validation solutions, wages for the employee and cost of computer equipment.

If you do the adress validation and de-dupication after the the submission of data in a batch cleansing, the average cost per entry is $10. 

With the commonly used Laissez-Faire solution (doing nothing) the cost is greater than $100 per record.

It is also stated in the the article that up to 20% of the contact data is wrong when it is
saved.

A quick calculation.  You have 100.000 records with 5% incorrect data.  Then the bad
data costs you 100.000 x 0,05 x 100: $500.000 or 2.500.000 DKK

Time to end Laissez-Faire!

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2 Responses

  1. Appreciated if you could help me on explanation of 1:10:100 rule of quality or through suggesting a good/easy reference (book).

  2. Hi I am working in a company where we want to set up quality parameters for a new project we have planned a method which is not that much encouraging

    For e.g. We have 28 set fields for quality and if an agent does not meets 1 set his quality will be 27 when we calculate the quality by using No. of RFT after error(27)/Total number of set fields * 100

    27/28*100= 96.24% Quality which is less than the benchmarlk of 99% Please can you all input your comments how to do better quality to meet benchmark of 99%

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