September 16, 2024

Data Standards: Definition, Benefits, Examples, and Role in Data Quality

Explore the importance of data standards, their benefits, components, and role in maintaining data quality. Learn how they're developed and see real-world examples.
Dexter Chu
Head of Marketing

What are Data Standards?

Data 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 are often developed by subject matter experts who come to an agreement on how to solve business problems.
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  • They can be made up of smaller components that can be mixed and matched for different purposes.
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  • Some common components of data standards include data type, well-understood terms and codes.

What are the Benefits of Data Standards?

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.

     
  • Transparency and understanding: Promote clear meanings for data that is often reused.
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  • Access: Allow data to be reliably read, sorted, indexed, retrieved, and communicated between systems.
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  • Efficiency: Can make an existing process less expensive, more productive, and more timely.

What are Some Examples of Data Standards?

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.

     
  • TransXChange: A standard used in the UK for recording and sharing bus data on routes, timetables, fares, accessibility, and live locations.
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  • UPC codes: Machine-readable images used to track product type and pricing.
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  • QR codes: Two-dimensional matrix barcodes that contain information that can be scanned and converted to something useful, such as a website URL.

How are Data Standards Developed?

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.

     
  • Data standards are developed by subject matter experts.
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  • They are created to solve business problems.
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  • They can be made up of smaller components for different purposes.

What are the Components of Data Standards?

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 type: A fundamental principle for defining the type of information in a piece of data.
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  • Well-understood terms and codes: Can be used for data retrieval.
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  • These components ensure the structure, recording, and description of data.

What Role do Data Standards Play in Data Quality?

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.

     
  • Data standards ensure consistency, integrity, format, and meaning of data.
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  • They ensure that data can be shared, combined, exchanged, and understood by different users.
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  • This plays a crucial role in maintaining the overall quality of data.

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