The reliability of data protection refers to the degree at which data from a company is reliable, accurate and consistent over time. Data from a business’s must be reliable to be useful in analysis and making decisions.
To ensure the integrity of data, businesses should establish and adhere to strict quality control procedures. This could include data validation checks as well as standard formats and comprehensive cleaning procedures for data. Expertise and experience of the teams responsible for data collection are equally important. A team with experience is more likely to adhere to the best practices and provide reliable data. Similarly, adequate technological infrastructure and secure data storage capabilities can help avoid errors that could impact data reliability.
Incorrect or incorrect data can cause serious problems internally and externally. A data issue can cause a company to display that a customer’s bank account has $100, when it actually has $1000. This could lead to financial penalties and the loss of trust. In the same way, inaccurate sensor data from manufacturing equipment could cause recalls and defects to products.
Validity and reliability are both interrelated however they are distinct concepts. Validity is determined by whether the information is correct. For example the list that has duplicate email addresses or those that are not unique is not valid and cannot be used to send marketing emails.
Reliability refers to the accuracy and consistency of that data over time – for example, if you have two lists of customer email addresses from different sources that are identical, but slightly different, you can’t make use of them to target the same marketing campaign since they will not work or will reach the wrong people. To ensure integrity and transparency it’s essential to keep detailed records of the methods used to collect and edit data.