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The important characteristics of data quality

As everyone is aware, the second term for data validation process is the “human being” or we can say that it’s all about the correctness and reasonableness of data.

In such situation, every organization wants their data quality to be perfect but the real challenge comes while defining what those qualities represent. Judging the quality of data requires an observation of its attributes and then weighing those attributes according to what is most important to the organization and the application(s) for which they are being used.

So, let us know the important characteristics that define data quality are:

Timeliness and Relevance: The collection of the data should be done at the right moment in time. Data collected too soon or too late could falsify a situation and drive incorrect decisions.

Granularity and Uniqueness: The level of collecting detailing data is important, because confusion and inaccurate decisions can occur or even aggregated, summarized and manipulated collections of data could show you a different denotation than the data implied at a lower level. Hence, there should be an appropriateness level of granularity to provide satisfied uniqueness and distinctive properties to become visible. This is a necessity for operations to function effectively.

Legitimacy and Validity: We will start with an example. On surveys, items such as gender, ethnicity, and nationality are typically limited to a set of options with restricted open answers. Hence, based on the survey’s requirement, any answers other than these would not be considered valid or legitimate. This example goes for many data and should be carefully considered when determining its quality. The employees in each department in an organization understand the value of data and where it should be valid, so the requirements must be leveraged when assessing data quality.

Availability and Accessibility: Due to legal and regulatory constraints, this characteristic can be tricky because individuals need the right level of access to the data in order to perform their jobs. Hence, this characteristic believes that the data should exist and is available for access to be granted.

Accuracy and Precision: This characteristic is all about the exactness of the data which means it should convey the correct message without being misleading and should not have any wrong elements.

Reliability and Consistency: Many systems in today’s environments utilize and/or gather the same source data. Despite the location of collected data, it cannot contradict a value residing in a different source or collected by a different system. Hence, maintaining a stable and steady mechanism for collection of data without contradiction or unwarranted variance is very important.

Completeness and Comprehensiveness: No organization will love to keep incomplete data or gaps in data collection lead. Hence, it is important to understand the overall requirements that constitute a comprehensive set of data to decide whether or not the requirements are being fulfilled.

At BDS Services, through our data validation services, we help you get better results by managing the uprightness of databases to be more steady and serviceable, authorizing them to provide more value to users.


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