Article

Conduct A Thorough Data Quality Assessment

Is your business suffering from duplicate work efforts and outdated data? To know where to begin, it is important to gain a deep understanding of current processes and digital lifecycles. Before you try to come up with a solution start tackling your data quality management problems by performing a thorough analysis of the current state of your data inventory.

The effort you put in today will help insure the longevity of some of your most beneficial assets.

Understand that while content presentation changes with each new release of JavaScript, your core digital assets can be managed independently with a well designed Content Management System.

Data quality dimensions can include:

Completeness – a percentage of data that includes one or more values. It’s important that critical data (such as customer names, phone numbers, email addresses, etc.) be completed first since completeness doesn’t impact non-critical data that much.

Uniqueness – When measured against other data sets, there is only one entry of its kind.

Timeliness – How much of an impact does date and time have on the data? This could be previous sales, product launches or any information that is relied on over a period of time to be accurate.

Validity – Does the data conform to the respective standards set for it?

Accuracy – How well does the data reflect the real-world person or thing that is identified by it?

Consistency – How well does the data align with a preconceived pattern? Birth dates share a common consistency issue, since in the U.S., the standard is MM/DD/YYYY, whereas in Europe and other areas, the usage of DD/MM/YYYY is standard.

 

 

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