Data protection reliability is the process used to ensure that data is accurate, complete and secure throughout its entire lifecycle, from the moment of creation until the time of archival or deletion. This includes securing against unauthorized data access, corruption, and errors with robust security measures, audits and checksum validations. Data reliability is critical to enable confident and informed decision-making, and empowers organizations with the ability to utilize data to make a difference in business.
Data reliability can be compromised by many factors, including
Credibility of Data Sources. A dataset’s reliability capital raising strategy and credibility are greatly determined by its source. Credible sources have a track record of producing reliable data. They are validated by peer reviews, expert validations or the adherence to industry standards.
Human error: Data entry and recording errors can introduce inaccuracies to a dataset, reducing its reliability. Standardized processes and training is essential in preventing these errors.
Backup and Storage: A backup plan, like the 3-2-1 method (3 copies on two local devices, plus one offsite) minimizes the risk of data loss due to hardware malfunctions or natural disasters. Physical integrity is also a factor to consider, as organizations rely on several technology vendors having to ensure that the physical integrity of their data across all systems is preserved and secured.
Reliability of data is a thorny issue with the most crucial aspect being that a business has reliable and reliable data to make decisions and create value. To achieve this, companies have to establish a culture of data trust and ensure that their processes are designed to produce reliable results. This means adopting standards-based methodologies, teaching data collectors, and providing reliable tools.