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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. 

Data cleansing involves analyzing data to find mistakes,
re-engineering and validating new metadata with rules to address errors,
transforming the data, and then reloading the data into the database.

One of the key takeaways when it comes to data quality is
not to view it as a series of problems but rather as a process that needs to be
managed–much like cleaning house. Watch our video to understand the data
quality lifecycle better. 

Two critical components that also play a role in data
quality are validation and verification. While they’re similar, they’re not
identical. Validation refers to the data that adheres to a certain specified
and expected format. For example, a zip code has either five digits or a ZIP+4
has 9 digits. Verification is comparing the data in your database to a
standard. For example, requiring someone to enter their password twice when
they sign-up for something to make sure it was typed in correctly.

There are many more components and elements when it comes to
being a successful data steward. Those mentioned above are just a few taken
from Melissa Data’s “Ultimate Guide for Data Stewards”. If you would
like to read the full version of the guide, click here to
download it and learn more about data quality.

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