Data in information systems allows for businesses to process various actions in a way that is seamless. If one imagines a business as a factory, many businesses main product is data. From hospitals to banks, data is created, stored, transferred, and viewed every single, work day. If this data is of poor quality, it may slow down a business’ processes. For example, a hospital’s medical records are still on paper and have not been transferred to a digital copy. Such an event can trigger easier data breaches and longer wait times for information to be retrieved, copied, and transferred. What makes data quality poor? There are collectively defined standards that define data quality. They are accuracy, completeness, consistency, uniqueness, and timeliness (Kimball, Ross, Thornthwaite, Mundy, & Becker, 2011). When poor quality data is inaccurate like for example, the last name of a customer in a bank, this leads to service hang ups and mistakes. For completeness and consistency, poor quality will not be clear or in agreement with underlying atomic-level detail (Kimball, Ross, Thornthwaite, Mundy, & Becker, 2011). If such data exists in a business, it can lead to further mistakes down the line, especially regarding assessments or information retrieval. Effectiveness in information retrieval is important, that is why poor data quality means little to no uniqueness. Lastly, if data is not current, relative to a business’...

The dental office still has paper records and have yet to transfer them to a digital version. They have extensive paper files put into a cabinet that can be easily accessed by patients or anyone walking into the office. Because the data is on paper, anyone that sees where it is stored can walk in ad take it. Additionally, they do not check for duplicates and often have incorrect spellings of patient names or old addresses that have not been updated for years. This leads to chaos and missed files, longer waits for patients.
To ensure good data, organizations need to improve compliance and accuracy. Poor data quality may negatively influence the public perception of a company/organization. Going back to uniqueness and eliminating redundancies, when duplicate mailings are sent for example to customers, this leads to extra costs. These costs can be eliminated by implementing adherence policies to manage the quality of data entry (Roebuck, 2011). Policies can be written to ensure there are periodic checks for duplicate addresses, out of data contact information, and so forth.

This lends to creation of methods that improve information accuracy and uniqueness. These two things are key hallmarks of top quality data. To do that organizations may need software upgrades…