Dear {Title} {Surname}


sorryI recently received the following email from a supplier:

Dear {Title} {Surname}

Our sincere apologies for this week’s emailer where we incorrectly addressed you in an unprofessional manner. This mistake was due to an error from our service provider which has now been fixed.

Kind regards,

The irony? The previous email (for which they were apologising) had been addressed to “Dear Allemann“. A minor error (no given name) that had already been forgotten.

To often, when we rush in to correct data quality errors we exacerbate the problem.

Data quality and master data projects are frequently under pressure to deliver – time and budget are often both in short supply.

I often see evidence of short cuts taken in previous cleansing projects when looking at live data. In many cases, the data cleansing effort may have actually made data quality problems worse.

How do we avoid this?

  1. Ensure business engagement and sign off on data cleansing rules
  2. Ensure adequate testing of data quality processes

Hopefully this example will help you to convince the business stakeholders of the importance of these two points.

 

 

 

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