Data protection reliability is the process of making sure that data is correct complete, safe, and secure throughout its entire lifecycle, from creation to archival or removal. This includes securing against unauthorized data access, corruption, and errors with robust security measures, audits, and checksum validations. Data reliability is vital for enabling confident and informed choices, providing organizations with the ability to leverage data for business impact.
The accuracy of data can be compromised by a variety of factors, including:
Credibility of Data Sources. A dataset’s trustworthiness and credibility are greatly affected by its provenance. Credible sources are those that have a a proven track record for providing reliable data. They can be validated by peer reviews, expert validations or industry standards.
Human error Data entry and recording mistakes can result in inaccurate data for the accuracy of a data set, thus reducing its reliability. Standardized processes and proper training are essential to prevent these mistakes.
Backing Up and Storage: A backup strategy, such as the 3-2-1 method (3 copies on two local devices, plus one offsite) helps to prevent data loss due to natural disasters or hardware malfunctions. Physical integrity is a further consideration, with organisations leveraging several technology vendors having to ensure that the physical integrity of their data across all systems can be maintained and secured.
Reliability of data is a complex issue, with the most important aspect being that a business is using trusted and high-quality data to drive decisions and create value. To do this, businesses need to create a culture of trust in data and ensure that their processes are designed to produce reliable results. This means implementing standard methods, educating data collection staff, and offering reliable software.