Metadata in technical documentation serve far more purposes than just the tagging of content. They are the foundation for organising modules in a targeted manner, delivering variants precisely, and providing information for specific target groups or markets. Those who use metadata professionally enable efficient reuse, streamlined variant management, and seamless content delivery across various channels – thereby laying the groundwork for sustainable, structured technical documentation.
This article explains what metadata are, how to develop a concept for assigning them, and the added value metadata bring to the everyday work of technical writers.
What are metadata?
Metadata (“data about data”) can be found in the content management system, in file properties, or in product databases. Metadata describe, categorise, and structure content objects without being part of the actual content itself. Typical metadata in technical documentation include, for example:
- Title
- Version
- Language
- Target audience
- Product variant
- Creation or modification date
- Status (e.g. “in progress”, “released”)
- Relevance for specific markets or standards
Benefits of metadata for technical documentation
Without metadata, modular documentation is merely a collection of files – confusing, inefficient, and prone to errors. When used correctly, metadata enable you to:
- Find content quickly: Efficient search and filter functions (e.g. by machine type and maintenance interval) allow you to find the right content in seconds.
- Reuse content: Individual text or image modules can be reused for different variants and documents. Metadata ensure that they are assigned to the correct context.
- Maintain consistency: Changes (e.g. to maintenance instructions) are managed centrally and distributed correctly via metadata.
- Efficient translation management: Metadata such as language, status, or target market make it easier to select content for translation, avoid duplicate translation jobs, and ensure consistent terminology and more efficient processes in all languages.
- Automation and digital value creation: Metadata are the central foundation for automating complex processes in technical documentation. They enable information to be delivered systemically, precisely, and without manual effort to service portals, documentation apps, or Industry 4.0 applications. Data chains – for example, for suppliers, digital twins, or service assistants – will only function smoothly if the required modules and information are clearly identified and provided via metadata.
- Compliance with standards: Thanks to standards, such as iiRDS or VDI 2770, metadata can also be structured to meet external requirements.
Practical example: Robot arm variants
In mechanical engineering, a company produces several variants of a robot arm. Each variant requires specific assembly and safety instructions. Using metadata, the technical author tags the relevant text modules; during export, the content management system automatically selects the appropriate modules and generates the correct manual for each variant.
Managing metadata efficiently
To leverage the benefits of metadata, a well-considered approach tailored to your own company is essential. Only with a clear strategy can the potential of metadata be fully realised: Moving away from simply collecting additional information to a structured, strategic approach that delivers real added value.
As soon as there is a desire to make processes more efficient, it becomes clear: It is time to develop your own, tailored metadata concept. Only in this way can the advantages of metadata be systematically and sustainably integrated into daily work.
A step-by-step approach has proven effective for implementing such a sustainable metadata concept:
1. Define objectives and use cases
Start by asking: What do you want to achieve with metadata?
Typical objectives include:
- Efficient reuse of content
- Variant management
- Automated publication processes
- Quality assurance
2. Identify required metadata types
Analyse which metadata you actually need to achieve your objectives. For variant management, for example, the metadata type “product variant” is recommended. For quality management, “status” (such as “draft” or “released”) can be helpful. Other examples include “field of application” (e.g. “technical service” vs. “operator”) or the person responsible for a module. Also consider whether you need information such as “target audience” or “valid from/until”.
3. Structure and standardise metadata
Define binding terms and value lists for all required metadata. Use fixed designations for status (“draft”, “under review”, “released”) or product variants, for example. Uniform and standardised structures avoid misunderstandings and make maintenance easier later on. Also specify how and by whom metadata is maintained.
4. Plan the technical implementation
Ensure that your content management system (CCMS) supports the required metadata fields. Adjustments may be necessary, such as additional fields for specific product features or automated approval workflows. Consider early on how you want to search, filter, and use metadata for different output channels.
5. Launch, adjust and optimise
First, implement your concept for a clearly defined area or product line. Regularly review within your team whether the chosen metadata and processes are effective, and adjust your concept step by step. With each adjustment, data quality will improve – and your content becomes more sustainably usable.
Automation: AI in metadata assignment
Especially with large documentation inventories, technical writers invest a great deal of time in the manual assignment and maintenance of metadata. AI-supported functions in the content management system now effectively support this process. They automatically analyse the content, recognise machine type, maintenance purpose, or target audience from the context, and suggest appropriate metadata or assign it directly according to predefined rules. This significantly accelerates entry and updating: authors no longer have to assign metadata to each module individually, but simply review, supplement, and correct the automatic suggestions. This saves time, ensures consistent data quality, and reduces the risk of errors or gaps in the metadata structure.
Conclusion: Metadata are key to structure in technical documentation
Metadata are one of the most important – albeit often invisible – tools of modern technical writing. They form the basis for targeted control, filtering, and reuse of information modules. With their help, technical documentation goes beyond a mere collection of text modules and becomes an intelligent, controllable knowledge base that meets the demands of product diversity, various target groups, and complex publication channels.
If you develop and consistently maintain a sustainable metadata concept, you lay the foundation for thoroughly structured, high-quality, and always up-to-date content. This pays off in all processes – whether in variant management, quality assurance, or automated information delivery.
