The Ultimate Guide for Data Stewards

Blog Administrator | Data Quality | , ,

The first key of successful data stewardship is

understanding data quality.  Data quality is most commonly defined as
having five dimensions: completeness, data conforming to the appropriate
standards, internal consistency, accuracy, and a time stamp to verify the
period within which the data is valid.

When information is incoming, you’re bound to have mistakes.
It’s inevitable that bad data will get in your system That’s why it’s important
to put your CRM through data cleansing.… Read More

Standardizing Your Approach to Monitoring the Quality of Data

Blog Administrator | Address Standardization, Analyzing Data, Data Cleansing, Data Integration, Data Management, Data Profiling, Data Quality | , , , , , , ,

By David Loshin

In my last post, I suggested three techniques for maturing your organizational approach to data quality management. The first recommendation was defining processes for evaluating errors when they are identified. These types of processes actually involve a few key techniques:

1) An approach to specifying data validity rules that can be
used to determine whether a data instance or record has an error.

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