The practice of organizing, structuring, and labeling content in an effective and sustainable way. Good information architecture helps users find information and complete tasks efficiently, and forms the structural foundation of any digital product.
Common contexts
- Restructuring a government website where tree-testing reveals users consistently fail to find key services
- Defining top-level navigation categories for a SaaS product expanding from three features to twelve
- Running card sorting sessions to validate a proposed taxonomy before committing to development
Use when
Revisit IA whenever the product's content volume increases significantly, a new user segment is added, or findability metrics show sustained decline — don't wait for a full redesign.
Avoid when
Don't impose a new IA structure based solely on internal logic or stakeholder preference — without user research to validate labels and groupings, you're likely replacing one set of confusing categories with another.
Users don't experience your IA as structure — they experience it as friction or ease, which means the only honest test is watching someone who doesn't know your product try to find something.
Real-world examples
- Amazon's product taxonomy organises over 350 million items under 36 top-level departments discovered through years of card-sorting research, directly driving its conversion rate.
- GOV.UK consolidated 400+ government agency websites into a single IA structured around user tasks ('renew a driving licence') rather than organisational units, cutting call-centre volume by 30%.
- Airbnb's 2020 navigation overhaul moved from city-first search to experience-first browse — a IA shift guided by tree-testing data showing users searched 'things to do' more than destinations.