How do you test and debug your data formats and sources before publishing your technical document?

Validate Your Data Format Ensure Data Consistency: Confirm that the format (e.g., JSON, XML, CSV) adheres to the expected schema. Check for proper encoding (UTF-8, ASCII), correct field types (strings, integers, dates), and uniform structure across datasets. Check for Missing or Extra Fields: Ensure that all necessary fields are present and correctly populated, and there are no unnecessary fields that could cause confusion.

Create scripts (often in Python or other languages) to automate data checks against source files or databases. These scripts can identify inconsistencies or missing information. For data in structured formats like XML or JSON, use schema validation tools to ensure data adheres to the defined structure. These tools can catch formatting errors and missing required fields. Many CMS platforms offer built-in data validation features. These can check for missing fields, invalid data types, and ensure adherence to pre-defined formats.

Verify Data Source Integrity: Confirm that the data sources you are referencing (databases, APIs, third-party datasets) are reliable and up-to-date. Cross-check with trusted resources to ensure accuracy. Test Data Source Accessibility: Ensure the source is accessible, especially if it’s an API or a web-based dataset. Check for network connectivity issues, access permissions, or API rate limits. Confirm Data Freshness: Ensure the data is current and relevant. For example, if using live data from a source, verify that you’re pulling the latest version. Test Data Queries: If your data involves SQL or API queries, run multiple test queries to ensure they return the correct results and handle edge cases (e.g., missing or corrupt data).

There are different ways to test your data. User testing is likely the best use of your time once you feel like it's in a suitable enough place to present to users. They will be able to give you different perspectives of how the data is viewed.

Your documents contain internal and external links that redirect end users to the desired section. It is highly possible that links got an update or become irrelevant due to some reason. If you do not have a standard documentation testing workflow, it will have a negative impact on end users, especially when you are onboarding new customers. Some of the tools like Github Actions, make, etc. are developed to look after this. In an agile environment, GitHub Actions are found reliable as it has the functionality to block the merging of PRs if the conditions are not satisfied. These actions are developed once and useful to large teams of writers. Providing them the clarity in logs will be addon benefit here to locate the issue even faster.

Ensuring data accuracy with a strong emphasis on effective data debugging is CRITICAL. Tools such as OpenRefine, JSONLint, and JSON Schema work very well. Perform integration testing for seamless data source integration, emphasizing compatibility. Simulate edge cases, use visualization tools for pattern insights, and implement real-time logging for issue identification. Incorporate user acceptance testing for usability feedback. Conduct iterative testing for updates and review documentation for alignment with the actual data structure. This focused approach enhances data reliability before document publication.

To test and debug data formats and sources before publishing a technical document, double-check data for accuracy, use tools to validate formats, and run tests to ensure everything works correctly. Review the data thoroughly to catch any errors, ensuring it is correct and reliable before sharing.

Keep track of changes to your data format and sources using version control systems like Git. This helps manage changes and rollback if needed.