Freedam

Taxonomy

A taxonomy is a classification scheme that arranges terms in a hierarchy, from broad categories to narrow ones, so that content is classified and found consistently. "Products > Furniture > Chairs > Office chairs" is a path through a taxonomy, and an asset classified as an office chair is also, by that path, a chair and a piece of furniture. In a DAM, a taxonomy is a controlled vocabulary arranged as a tree: people pick terms from it instead of typing their own.

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What a taxonomy does in a DAM

  • It shows what exists. Search answers "find this specific file". A taxonomy answers "what do we have for this product line or this market". For a new colleague, an agency or a partner, it shows how the organisation describes its own content.
  • It supports broad and narrow questions. A trade marketing manager wants every image of seating; a product manager wants only the office chairs. A hierarchy serves both from the same classification.
  • It makes reporting possible. Counting assets per product line or per market shows the gaps, such as a new product with no lifestyle photos or a region with no current price list.

Taxonomy, folders and tags

Folders Taxonomy Free tags
Structure One hierarchy One or more hierarchies of terms None
Where an asset sits In one place Under one term in each facet (product, asset type, region) Under any number of words
Consistency Depends on whoever files the asset Values come from the agreed list Low: spellings and synonyms vary
Changing it Moving files breaks links and habits Renaming a term changes it on every asset that uses it Each variant is fixed by hand

Most DAM taxonomies are faceted: several small hierarchies (product, asset type, market, campaign, channel) instead of one large tree. An asset is classified once in each facet, and filters combine them: "lifestyle photos of office chairs, approved for the Nordic markets". A single tree forces one dimension to come first, and the asset then has to be filed again for every other dimension.

Building a taxonomy

  1. Start from how people search. Read the search logs of the current system, and ask sales, partners and agencies what they request most often.
  2. Choose the facets. Product, asset type, market, campaign, channel and language cover most B2B marketing libraries.
  3. Follow the product catalogue, not the organisation chart. Departments are reorganised more often than product lines and markets change.
  4. Keep each tree shallow. Two or three levels are usually enough. If most branches hold only a handful of assets, the tree is more detailed than the library needs.
  5. Use the words people use. If the team says "packshot", the term is "packshot".
  6. Test it on real assets. Classify 50 to 100 assets with the people who will use it and note where they hesitate or disagree.
  7. Name an owner who adds terms and reviews the tree as products are launched and retired.

ANSI/NISO Z39.19-2005 and ISO 25964-1 (2011) describe broader and narrower term relationships in detail. The DAM taxonomy guide covers facets and governance, with a worked example for a consumer brand.

Taxonomies in Freedam

  • Vocabularies can be flat lists or trees, and a flat list can be converted into a tree once it grows. See controlled vocabulary.
  • Terms are kept in a custom order or sorted alphabetically, and each has a label per language.
  • A metadata field can be limited to one level of a tree, and a second field can depend on the first: pick a product line, and the product field offers only the products in that line.
  • Tree vocabularies appear as a tree in the term picker and in the quick filters of the asset library.
  • Deleting a term that assets still use asks whether to move them to a replacement term or clear the value.
  • Analytics insights list terms that, 60 days after they were created, have never been applied, searched for or used as a filter, so the tree can be pruned.

Frequently asked questions

What is the difference between a taxonomy and an ontology?

A taxonomy records one kind of relationship: broader and narrower. An ontology also records other relationships and properties, for example that a product "is made of" a material or "is sold in" a market, in a form software can reason over. Most DAM libraries need a taxonomy; ontologies are used in knowledge graphs and large publishing systems.

How deep should a DAM taxonomy be?

Two or three levels in each facet is enough for most libraries. Deeper trees slow people down when they tag, and lead to inconsistent choices between neighbouring branches.

What is an example of a taxonomy in marketing?

A product taxonomy that follows the catalogue: product family, then product line, then product. Combined with facets for asset type (packshot, lifestyle, video, datasheet) and market, it lets a partner find "all packshots of the office chair range for France" with three filters.

Related terms

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