Reliability of data protection is the extent to which data from a company is reliable, accurate, and reliable over time. Data from a business must be reliable to be used in analysis and decision-making.
To ensure data integrity businesses must set up and adhere to strict quality control protocols. These could include validation of data, standard formats and thorough cleaning procedures for data. The experience and expertise of data collection experts is important, as a knowledgeable team is more likely to follow best practices and create reliable data. A secure storage option for data and a technological infrastructure that is up to date can help to prevent errors which could affect data reliability.
The use of inaccurate or inconsistent data can cause serious problems both internally and externally. A data error could cause a company to display that a customer’s account is $100 when it actually has $1000. This can lead to financial penalties as well as a loss of trust. Inaccurate sensor data from manufacturing equipment can also cause product defects and recalls.
Validity and reliability are interrelated but different concepts. Validity is based on whether the data is accurate. For example a list of duplicate email addresses or those that are not unique is not valid and can’t be used to send marketing emails.
Reliability refers to the consistency and accuracy over time of the data. For example, if two lists of customer email addresses from different sources match but differ slightly, they are not able to be used to target the marketing campaign, since they won’t work or reach the wrong people. This is why it is crucial to keep precise records of the methods employed for obtaining and altering data, to maintain the integrity and transparency of the information.
