According to J.M. Duran, data is of high quality when it is “fit for their intended uses in operations, planning and decision making.” In other words, if the data allows for an individual to do an effective job in operations, planning for something and making a correct decision, it is deemed to be quality data. It’s taken a step farther, though, when discussing an increase in data volume. It is important for the quality to stay the same even when more data is added.

Definitions of Data Quality

According to different agencies, there are many different definitions of what data quality is. These definitions are:

  • GIS Glossary: Quality refers to the level of excellence in relation to the phenomena. In other words, the more perfect the data is in comparison to the response, the better.
  • British Columbia Government: Completeness, validity, consistency, accuracy and timeliness are all important when discussing the specific use of data.
  • Glossary of Quality Assurance Terms: The characteristics of data that provide the most desired response.

In other words: the ability for the data to be used most effectively and provide the most precise outcome is data quality. Because of this, companies are obviously very interested in guaranteeing that their data quality is incredibly high. The more effective the data is working, the more effective their business is working and effectiveness tends to mean more profit.

History of Data Quality

Data quality started out during the mainframe times. A method had been developed where misspellings in names could be automatically fixed as well as in addresses. This was so necessary because postage was spent to send mail to that place and if the address was wrong, it came back. More importantly, the mainframe tracked customers that had moved, died, gone to prison or changed their name due to marriage.

As time went on, government agencies began to offer this information to service companies that would have otherwise had to manually do it themselves. The reason for this is because the mainframe was so expensive, no one wanted to purchase it and manage it. Therefore, the government giving this information away saved these companies millions of dollars a year. However, with the inexpensive server now available, this data quality has been brought within the corporation. However, the very basis of it is simple: the better the data, the less money that needs to be spent on it.