Agencies considering the adoption of open data standards should involve a trusted standard development organization with permanence, customer focus, and with sufficient user involvement

This paper analyzes several case studies of data standard development and management, recommends a set of best practices from them, and evaluates how implementable the best practices are with a new data standard creation project.


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Lesson Learned

Develop and manage standards by a trusted source with permanence, customer focus, and with sufficient user involvement.

    Additional standard development organizations (SDOs) that are accredited are needed to cover areas of expertise where open data is expected to be released. Also needed is a directory of SDOs by domain to help remove standards being re-developed. If no SDO is available develop the standard within the context of the project with base organization separately, prepare to be flexible and involve with users’ input.

Leverage existing data vocabularies.
    Though existing data standards that the industry has coalesced around may not be perfect, try and reuse existing data vocabulary and software by extending the standard rather than creating new ones.

Develop the standard process size for the audience.
    Rigorous and overly detailed processes and standards documentation would likely deter all but the most technical and determined individuals in niche work. Instead, it is recommended to use tools and terminology that are already familiar with the industry.

Evaluate the standard at the right pace and use rigorous methods.
    Use semantic versioning and be explicit about pre-releasing. Use versioning tools like GitHub for writing, managing and collaborating on the data standards without the initial setup time.
Limit unnecessary tools and libraries.
    When looking at file formats, there are a lot of options that will improve data access and compressibility but may not be very accessible to your audience. Often times basic formats like the CSV-based standard can meet everyone’s needs and be more accessible to users.

Diligently document the standard and process.
    Use Readme files that include version, date updated, authors, changelog, known issues, and that indicate which fields are mandatory or optional.
Balance flexibility while limiting vocabulary dispersion.
    Adding too much flexibility in mandatory fields will result in a wide variety of data and will limit interoperability. Specify different fields as mandatory for different applications that provide the broadest number of uses.

Develop structure and tools that catch and limit errors.
    Limiting the number of times something is duplicated will reduce the opportunity for conflict and limit the number of places the data is needed for edits. Using number-string lookups for common elements saves small amounts of space and input time but makes it harder for readers to find errors when they are just looking over the data. Include a validator for the standard that users can test against to reduce errors coming in.

Promote your standard to make sure the industry knows about it.
    No data standard is useful in isolation. Use websites, listservs, conferences and other media resources to make sure people know about your standard and why it was created.

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Making Open Transportation Data Useful and Accessible: Recommendations for Good Practices in Open Data Standards Management

Author: Sall, Zorn, Cooper and Sana

Published By: 2017 Transportation Research Board Annual Meeting

Source Date: 08/01/2016

URL: http://fast-trips.mtc.ca.gov/library/TRB17-OpenData.pdf

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Jim Larkin


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Lesson ID: 2019-00867