Businesses are typically obligated to retain data for a period of time to ensure that business processes are followed in accordance with prevailing industry standards and government regulations. Data security must keep that sensitive data safe from loss of theft. Data integrity ensures that customers are treated correctly, such as receiving proper account crediting and reporting. Businesses collect and use an enormous amount of customer data, including sensitive or personally identifiable data. This makes data integrity critical to analytical results, as missing or inaccurate data might result in poor business decisions or product behaviors. This is certainly true of modern business analytics for business decision-making and product development. A traditional axiom of early computing was garbage in/garbage out. This manifests itself in three major ways: This transition demands an enormous amount of data, which has become the new raw material of the digital economy. Where traditional businesses often focused on the construction and distribution of physical products, today's businesses typically prosper through the delivery of digital products and services. Data corruption can be caused by hardware failures, human error, a malicious action or failure of data security. Instead, it's a comprehensive environment that's created through an array of applicable standards, rules, processes and procedures that are implemented across an organization's infrastructure and observed by its employees, partners and users.ĭata corruption occurs when any unwanted or unexpected changes to data take place during storage, access or processing - all of which represent a failure or loss of data integrity. The idea of integrity is a central element of many regulatory compliance frameworks, such as the General Data Protection Regulation ( GDPR).ĭata integrity isn't a single product, platform or tool. Data security involves considerations such as authentication, authorization, encryption, backup or other data protection, and access logging.ĭata integrity involves both physical and logical integrity.ĭata integrity is a broad discipline that influences how data is collected, stored, accessed and used. Further, safe data can't readily be exploited by malicious actors. Data is maintained in a secure manner and can only be accessed and used by authorized applications or individuals. For example, data accessed a year from now will be the same data that's generated or accessed today. Data remains unchanged regardless of how, or how often, it's accessed and no matter how long it's stored. Repeating tests should return the same results. For example, test results aren't rounded up or down, and any test criteria or conditions are well-documented and understood. Data isn't altered or aggregated in any way that affects data analytics. Tests that failed or yielded undesirable results aren't omitted from data requests. For example, if 100 tests are performed, complete data reflects the results of all 100 tests. Data is maintained in its full form and no data elements are filtered, truncated or lost. Bigelow, Senior Technology Editorĭata integrity is the assurance that digital information is uncorrupted and can only be accessed or modified by those authorized to do so.ĭata integrity describes data that's kept complete, accurate, consistent and safe throughout its entire lifecycle in the following ways:
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