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What data quality dimensions can statisticans impact by using a data profiling tool. August 16, 2007

Posted by Peter Benza in Data Accuracy, Data Completeness, Data Profiling, Data Quality, Data Tools.
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Two data quality dimensions that statisticans can play a role in is data accuracy and data completeness.  A data profiling tool comes in handy to facilitate the actual research required by the organization. 

What data profiling tool does your organization use?

What other data quality dimensions can be analyzed? 

How complete is your data? August 15, 2007

Posted by Peter Benza in Data Completeness, Data Governance, Data Quality, Data Research.
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Data completeness is contingent upon first knowing the target population* relative to the number of missing data elements (bad values) to good values by data element. 

Consider analyzing over time and set up on a scheduled basis missing value reports (better yet aggregate datasets) to study over time data completeness patterns.  These findings might also reveal other data governance processes, policies, and standards in your organization for consideration.

It is advised to include a statistical analyst early on in outlining this process in order to help define data completeness specific to your organization – past, present, and future.

*target population could be anything from your customer name/address customer master database to product-specific datasets and all their associated attributes.