Data protection reliability is the degree to which a business’s data is reliable, accurate, and consistent over time. Data provided by a company has to be reliable in order to be used in analysis and decision-making.\n<\/p>\n
Companies must follow strict quality control procedures to ensure the reliability of data. These can include validation of data along with standard formats and rigorous data cleansing procedures. Expertise and experience of data collection teams are essential. A knowledgeable team is more likely to follow the best practices and provide reliable data. Secure data storage and a technological infrastructure that is up-to-date can also help prevent errors which could impact the reliability of data.\n<\/p>\n
Incorrect or inconsistent data can cause serious problems internally and externally. For instance in the event that a database for a business shows that the customer has $1000 in their account, but they actually have $100 due to an error in the data and this could result in financial penalties and lost confidence. Inaccurate sensor information from manufacturing equipment could result in recalls and product defects.\n<\/p>\n
Validity and dependability could be related but are different concepts. Validity is based on whether the data is accurate. For example the list that has duplicate or non-unique email addresses is not valid, and therefore cannot be used to send marketing emails.\n<\/p>\n
Reliability is all about the accuracy and consistency of that data over time – for instance, if there are two lists of customer email addresses from different sources that are similar but slightly different, you cannot make use of them to target an individual marketing campaign since they will not work or will reach the wrong people. To maintain transparency and integrity, it’s important to keep detailed records of the methods used to gather and modify data.\n<\/p>\n