When data is retrieved, with no anomalies or alterations, then it is said to be in the desired quality intended. The desired quality is one which is useful for proper analysis, planning, and other intended purposes.
Data quality is also defined as data which meets the actual situation of things on ground or in real situations and possesses other characteristics such as accuracy, allowable format, timely collection, and retrieval, as well as other desirable features. The importance of good data quality for the growth of both business and public organizations is invaluable.

The evolution of data quality is traced back to the 90s when a research was centered on the quality of data. The subsequent use of gigantic mainframe computers also provided some level of data management for the proper routing of data when the need arose. This ensured that the data collected was not complex to figure out with the need for unplanned retrieval.
Presently, the emphasis on data quality has witnessed great involvement of big companies who see the invaluable nature of enhanced data quality, especially for business and organizational advancement.

The importance of data quality is essential for proper utilization of the received data because received data with poor quality would lead to an undesirable result which would be a loss to the resources spent on data collection and warehousing.
Enhanced Marketing: Desirable data quality ensures that businesses are able to reach real and high-end product or service consumers. While poor data quality leads to loss of needed customers as a result of wrong advert targeting
Easy Access: With the right data quality configuration, needed data can be specially preserved, while unwanted data is discarded, thereby saving enormous time needed in accessing useful information.
Reduced cost of operation: With quality data of business partners or customers contact details, business operators are able to reduce the cost of investing in the wrong partner or customer, the associated cost of poor data quality which may result in goods delivery to the wrong individual is saved when good data quality is ensured.
Increased level of customer relationship: Data quality enables business owners and other organizations to have good information about the customers that they service, and the understanding of customer data leads to a desirable feeling by customers on their importance in the running of a business venture.
Data quality management is the term associated with tools responsible for data quality, these tools help to perform several functions that help to keep data in its optimal state.
Informatica, SAS, and IBM Quality Stage are examples of data quality management tools. The functions performed by data quality management tool includes; parsing, cleansing, normalization, and matching etc.
The inability to measure the positive effect of data quality deprives organizations and business owners of investing resources inadequate data quality. Notwithstanding, falling prey to the negative effect of poor data quality brings about a result that far outweighs the cost of investing in the needed data quality.

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