Data protection reliability is the degree to which data from a company is accurate, trustworthy and consistent over time. A company’s data must be reliable to be useful in analysis and making decisions.
Businesses must adhere to strict quality control procedures to ensure the integrity of data. This could include things like data validation checks, standardized formats and thorough data cleaning procedures. Experience and expertise of the data collection teams is crucial. A team with experience is more apt to follow the best practices and provide reliable data. Similarly, adequate technological infrastructure and secure data storage capabilities can prevent mistakes that could affect data reliability.
The use of inaccurate or inconsistent data can cause serious issues, both internally and externally. A data glitch can cause a business to show that a customer’s accounts have $100 when it actually has $1000. This could lead to financial penalties as well as an erosion of confidence. Additionally, inaccurate sensor information generated by manufacturing equipment could result in product defects and recalls.
Validity and trustworthiness could be related but are different concepts. Validity is determined by whether the data is accurate. For instance, a list with duplicate or non-unique email addresses is not valid and can’t be used to send out marketing emails.
Reliability is about the consistency and accuracy of the data over time. For instance, if you have two lists of email addresses for customers from different sources that are similar but slightly different, it is not possible to make use of them to target the same marketing campaign since they will not be effective or will not reach the right people. To ensure integrity and transparency, it’s important to keep detailed records of the methods used to gather and modify data.