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What is data quality?
Data quality is a measurement of how fit a data set is to serve the specific needs of an organization. High quality data is required for trusted decisions.
How you define data quality is somewhat variable depending on the requirements of the business, a particular data set may be seen as high-quality by one organization and low quality by another.
How to measure data quality
The quality of data can be measured in a variety of ways. Most likely, an organization will need to apply a few different measurements in order to determine the relative quality of a given data set. Some of the important dimensions of data quality to consider are:
Accuracy. Quite obviously, data must be accurate in order to be useful
Completeness. A data set with too many holes is not going to be able to answer questions
Timeliness. Data which is out of date is not going to be valuable to an organization
Accessibility. It is necessary to have reasonable access to data if it is going to be put to use
The specific standards applied to evaluate the data will depend on the underlying needs of the organization and how the data is being used.
Factors affecting data quality
When data is deemed to be of low quality, it is likely that the inputs are to blame. The manner in which data is collected will have a lot to do with the quality of what lands in a database, and what is ultimately retrieved. Organizations who wish to improve their data quality will inevitably need to address input issues with data quality tools.
How Precisely can help
Precisely offers a number of data quality solutions to help you assess, improve and monitor the quality of your data to ensure it’s complete and accurate for business insights you can trust.