We research and set standards for
Labels
How we write and structure content such as:
- titles
- ledes (summary of page)
- headings
- links
Structures
How we categorize, create relationships and present content such as:
- taxonomies and hierarchies
- hyperlinks
- organization schemes
Navigation
How we browse for information such as:
- menus
- breadcrumbs
- headers
- footers
Search
How we find information such as:
- search interface (search bar and results)
- query builders (spell checks, autosuggestions)
- filters and facets
User goals
As a user of ontario.ca, I need to:
- land on the page I need or want the first time
- trust the content I find
- know what options are available to me
So that I can:
- complete my task
- answer the questions I had
- discover relevant information that I didn’t know about
- have reassurance that I had all the information I need
How users browse for programs and services
We’ve conducted research sessions to learn how people of Ontario think about programs and services (such as renewing driver’s licence, applying for student loan, or registering a business).
We discovered that users:
- usually organize services by topic such as driving, health, or travel.
- may sometimes organize services by role such as seniors, students, or parents.
- rarely organize services by region such as Northern, Central, or Southwestern Ontario.
- do not organize services by ministry or government body such as ServiceOntario, Ministry of Transportation, or Consumer Protection.
The findings guide how we structure content on ontario.ca.
Ongoing research activities
Tree tests and UI patterns
We conduct tree tests to evaluate how well users can find answers to relevant tasks.
We also explore UI (user interface) patterns that will reflect how the tree is structured.
A clear structure improves usability as it helps users discover relevant content that they may not have searched otherwise.
Open card sorts and groupings
We conducted open card sorts to understand how users would group and label the content.
With clear labels and a logical structure, we can help users locate specific information.
Search data and behaviour
We analyze top search queries and prototype ways in which we can design the experience to complete tasks as fast as possible.
Organization terms vs. user terms
How government addresses a program or service can often be different from how users would refer to them. For example, vehicle permit is the same as green paper, green permit, or registration papers.
We interview ministries and gather user data to create a curated list of terms that help us understand:
- what the official term is
- what the French equivalent is
- what it means in the context of Ontario
- words that users use to refer to the official term
Vocabularies and thesauri can also enhance the search experience as users do not have to type the exact words to get to the results.
How IA supports productivity and efficiency
Easier to content audit
When structures are clearly mapped, this enables ministries to evaluate content better as it’s easier to spot where everything is.
Easier to maintain and scale
As we analyze information, we document standards and systems that can apply across ontario.ca so that it will be easier to add, modify, or archive content in the future.
Reduce technical and design debt
With strong content standards and systems in place, we can have a leaner CMS (content management system).
This means we can spend more time building user-centred services and less on content that no longer serve user needs.
Takeaways
- Standards, systems and training are more important for our team than categorizing each content on ontario.ca, as this can help us scale faster and better.
- There are many components to IA that impact each other and it can be very overwhelming as we work. Rather than focusing on one thing at a time, we document our ideas as we come across insights.
- There is a lot of government content to organize as it encompasses everything from birth to death. We constantly have to limit our scope and implement in small cycles to move ahead.
- Good IA is not possible without collaborating with stakeholders, teams, and user research.
- There is no such thing as “perfect” when it comes to IA as we all interpret information differently. To keep the momentum going, we aim for “good enough” and allow room for modification when needed.