Data Fit for Use

The quality of data should be defined in the context of being fit for a particular use. Whether or not data is ‘fit for use’ depends upon the application of the data and the characteristics of quality that are required for that particular purpose. Data Quality also depends on the user’s expectations of what they define to be useful information.

There is no finite standard definition that can be applied across all varying types of data. Data Quality standards must include a forever evolving range of quality issues that incorporate the concept of ‘fitness for use’. These elements of data quality standards should be considered and balanced in the design, implementation, and auditing of an organizations data management processes and procedures.

Satio Solutions defines Data Quality as:

  • Accuracy: Accuracy of data is the degree to which the information is correctly recorded and to which it describes what is designed to describe.
  • Relevance: The relevance of data reflects the degree to which it meets the needs of those that are to use it. Assessing relevance is subjective and depends upon the varying needs of that information will be applied, aggregated, interpreted, and acted upon by those that are charged with using it.
  • Timeliness: The timeliness of data refers to the delay between the point at which the information is initially captured, calculated, or otherwise created and when the data item becomes available for other use. The timeliness of data will most often have a direct influence on its relevance.
  • Interpretability: The interpretability of data is directly related to its availability to be aggregated with other pieces of information necessary to interpret and utilize it appropriately. Varying components of information often include underlying concepts, variables and classifications as to how it is to be used. The processes of data collection and subsequent processing have impact to the measure of accuracy that will be represented.
  • Coherence: The coherence of data reflects the degree to which it can be successfully aggregated with other items of information within an analytical framework over an extended period of time. The use of data quality standards, definitions, classifications, and validation processes promotes coherence.

These data quality definition components overlap in a compounding manner. The levels of data quality that can be achieved within a given process is the result of defining, addressing, managing and balancing over time the various factors or elements that constitute better quality.

Because of the complex relationship of the varying components of data quality, an action taken to address or modify one aspect of quality tends to affect the other components. The decisions and actions that promote balance between these data quality components are based not only on intellectual knowledge but should include historical experience, continuing review, user feedback, and consultation in order to enable effective decisions that increase shareholder value to an organization.

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