How To Define Data Quality?

Put simply, data quality informs us regarding the reliability of a specific data set and whether it will be ok for the user to use in making decisions. On most occasions, the quality is calculated in degrees.

But what is data quality in practice?

Data QA is known to measure the data condition depending on certain aspects such as precision, completeness, validity, consistency, as well as uniqueness. 

The analysts of data quality have the responsibility of performing data quality evaluations involving interpreting and evaluating every quality data metric. Following this, they are going to create a total score showcasing the overall quality of the data and this provides the company with a percentage rating depicting the accuracy of the data.

Simply speaking, data quality shows the usefulness of the data in the long run. However, this term likewise refers to the process of planning, executing, and regulating the activities by applying the required quality management techniques and practices needed for making sure that the data happens to be valuable and actionable to the data consumers. 

How is it possible to improve the data quality?

It is a fact that data quality management will be the right answer when it comes to improving data quality. The intention of data quality management is to take advantage of a set of solutions for preventing any data quality issues in the future as well as clean data that is unable to satisfy data quality KPIs. All these actions allow the companies to satisfy the existing as well as future targets.

Data quality means something more than simply data cleaning. Bearing this in mind, we have mentioned several disciplines employed for preventing data quality issues and for enhancing data quality as well by removing all the bad data.

• Data governance – It helps to spell out the standards and policies ascertaining the data quality KPIs and also which specific data elements ought to be emphasized. These standards likewise consist of what business regulations should be followed for ensuring data quality.

• Data matching – This particular technology depends on match codes for figuring out whether 2 or more bits of data are going to describe the identical real-world stuff. 

• Data profiling – We apply this method for comprehending the different data assets that happen to be an integral part of data quality management. This process is essential since most assets mentioned here have been created by various individuals over the years while sticking to different standards.

• Master data management – These frameworks happen to be awesome resources for preventing issues when it comes to quality data. In fact, MDM frameworks are concerned with location master data, product master data, as well as party master data.

• Data quality reporting – It is feasible to use info accumulated from data matching and data profiling for measuring data quality KPIs. This process likewise consists of working a quality issue log, as well as any follow-up data prevention and cleansing methods.

• Product information management – It will be imperative for manufacturers and sellers of various items to align the data quality KPIs with one another such that when a product is ordered by a customer it is going to be the identical item at every stage of the supply chain. In this way, much of the product information management involves establishing a standard method of receiving and presenting product data.

• Digital asset management – Digital assets will be covering items such as images, text documents, videos, as well as similar files used along with product data. Here, we need to make certain that all the tags are pertinent as well as the digital asset quality.

• Customer data integration – This process involves accumulating customer master data procured by means of CRM applications. It will be imperative to compile this information into a single source of truth.

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