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Data protection reliability refers to the degree to which the data of a business is reliable, accurate, and reliable over time. Data from a business must be reliable in order to be used for analysis and decision-making.
To ensure the reliability of data, businesses should establish and adhere to strict quality control protocols. This could include data validation checks as well as standard formats and comprehensive data cleansing procedures. Experience and expertise of teams that collect data are equally important. A team with experience is more likely to follow best practices and provide reliable data. A secure data storage facility and a technological infrastructure that is up-to-date will also help avoid errors which could impact the reliability of data.
Incorrect or inconsistent data could cause serious problems internally and externally. A data glitch can cause a business to show that a customer’s account has $100 when it actually has $1000. This could lead to financial penalties and a loss of confidence. Similarly, inaccurate sensor data generated by manufacturing equipment could cause recalls and defects to products.
Validity and reliability are both interrelated but are two different concepts. Validity is about whether the information you’ve gathered is accurate. For instance the list of customer email addresses that are not unique or contain duplicate entries means that the list doesn’t have validity and should not be relied on to send marketing emails.
Reliability is about the consistency and accuracy of that data over time. For instance, if you have two lists of email addresses for customers from different sources that are the same, but slightly different, you shouldn’t make use of them to target an individual marketing campaign since they will not work or reach the wrong people. To ensure transparency and trust it’s essential to keep detailed records on the methods used to collect and modify data.