Data quality focuses on the overall usefulness of a dataset as a characteristic of its capacity for other purposes to be efficiently accessed and analyzed, typically by a database, system of data analytics or data warehouse. 

It is important to have knowledge about data quality because, without high-quality data, you can’t understand or keep in touch with your customer. In this data-driven era, it is simpler than ever before to find out key facts about existing and potential customers. This information would help you to market more efficiently and foster loyalty that can last for years. For some of us that are in the business world, know that if you are not informed of the benefits of data quality, your rivals are, and they will not fail to search for the foremost data to maximize their competitive edge.

Data quality aims at the capacity of data to be reasonably reliable to fulfill a particular function. The quality of data has some features that decide if the output is adequate:


  • Accuracy: Data needs to be accurate in order to be of high quality. Accuracy ensures the information is expressed in the results. It is necessary to remember that details may be complete, but still be incomplete. You might have the entire customer base’s addresses, for instance, but a few of these addresses might be misspelled. Despite genuine ambitions, many organizations find it challenging to take advantage of the many opportunities that data can offer, as poor-quality data cripple efforts around business intelligence and analytics.


  • Completeness: This is the degree to which the intended characteristics of data are present. This does not mean that data needs to be 100 percent complete to be high quality, but instead, the completeness is calculated in relation to customer satisfaction and data availability.


  • Incomplete data reports are among the major challenges organizations have to deal with. This is particularly the case for customer data, as customers don’t often see the importance of providing organizations with detailed information.


  • Timeliness: One of the most critical facets of database management is the timeliness of data. In business decision – making, this relates to the availability and usability of data. Smart choices are guided by healthy and well-organized data for a clearer picture of what to expect in the future.


  • Availability: This is the degree of accessibility to data. Data should not be difficult to access; it should be easily accessible to anyone who uses it.


  • Validity: This helps in checking the reliability of the sources; that is particularly important when linking multiple datasets to each other.

The above parameters are mainly used to determine which data counts as ‘high quality.’ Generally speaking, data quality is also evaluated by its capacity to fulfill a particular function as described above; the basic aim of data is to support: decision-making, planning, and operations.


If you need help, iData is here! You can also check out the Getting Started with Data Quality  and Advancing Your Career in Data courses from the iData Quality Academy.

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