The United States Thoroughfare, Landmark, and Postal Address Data Standard illustrates a data standard for addresses in the US; an attempt to have a highly uniform, accurate, and complete list to bring users in alignment. Because it fits these qualities to a high degree, it's used by the public at large and by states. Therefore, standards can be seen as benchmarks of quality and correctness.
Specifically, a standard is a set of codified assertions that a company establishes and then expects as the reliable default format. These codes must be enforced on data through the use of established data handling rules and data tools. Rules can be set to check data entry, like enforcing the correct state code instead of allowing typed input, or rules can be applied at later times like for batch processing to standardized data in bulk.
At a high level, say analyzing a customer contact information data set for completeness, an overall standard of 100% completion may be set. Setting a strict standard like this may go to support marketing and sales efforts by ensuring that every customer’s contact information is known and they can be reliably outreached to. This then makes the connection on how data quality impacts the business’s bottom line. Poor data quality leads to poor marketing and sales results, and therefore reduced bottom line.
Standards can dictate the attributes of data fields themselves; they can govern the relationships certain entities can have with others; and they can restrict values allowed in a format. Even today, AI and machine learning are being used to algorithmically understand patterns within data and establish the inherent rules. As a company understands its data over time, the standard can be improved. These rules are coded and enforced through the use of Master Data Management platforms.