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Get the guideData standards are a set of guidelines that define how to structure, record, and describe data. They ensure that data can be shared, combined, exchanged, and understood by different users. They also help maintain data quality, such as its consistency, integrity, format, and meaning.
Data standards, incorporating data standards best practices, can have many benefits, including transparency and understanding, access, and efficiency. They promote clear meanings for data that is often reused and allow data to be reliably read, sorted, indexed, retrieved, and communicated between systems. They can also make an existing process less expensive, more productive, and more timely.
Some examples of data standards include TransXChange, UPC codes, and QR codes. TransXChange is a standard used in the UK for recording and sharing bus data on routes, timetables, fares, accessibility, and live locations. UPC codes are machine-readable images used to track product type and pricing. QR codes are two-dimensional matrix barcodes that contain information that can be scanned and converted to something useful, such as a website URL.
Data standards are often developed by subject matter experts who come to an agreement on how to solve business problems. They can be made up of smaller components that can be mixed and matched for different purposes.
Some common components of data standards include data type, well-understood terms and codes. Data type is a fundamental principle for defining the type of information in a piece of data, such as Boolean, integer, string, or date. Well-understood terms and codes can be used for data retrieval.
Data standards help maintain data quality, such as its consistency, integrity, format, and meaning. They ensure that data can be shared, combined, exchanged, and understood by different users. This plays a crucial role in maintaining the overall quality of data.
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