The Observatory team performs research using the anonymous analytics data created as people use government services.
Here we describe the research outcomes that we hope to achieve.
- understanding the intent, behaviour and outcomes of users as they navigate specific life events
- understanding how the community's engagement with .gov.au has changed during the Coronavirus pandemic
The Observatory team regularly conducts research to understand our user needs. That research informs development of the Observatory.
This roadmap shows stories that are in our backlog for design and development.
- Observatory website - observatory.service.gov.au
- Reports - How do analysts receive and respond to incoming requests for data analysis?
- New training schedule
- Additional data sources for reporting
- Case studies of web analytics improving services
- Community space - Connect with and learn from other data analysts across government
- Feedback form - Install this on your website to understand how successful a person's journey was
- Monthly showcases - Show off analytics implementations, outcomes and learnings from across government
- New Observatory pricing structure - Reducing costs for teams to make use of the Observatory
How our subscribers use our service
High-level insights from a Discovery research stage with Australian Public Service data analysts and practitioners.
This research shows what we learned about how analysts work, their challenges, changes they would like to see, and how the Observatory might help achieve those changes.
How people access government services
This graph represents two weeks of user traffic across 38 .gov.au websites. The bottom and left hand side visualises search engine traffic from Google and Bing, predominantly showing users visiting a single page then leaving. The graph middle visualises webpages drawing traffic from across the search environment along with traffic from people using bookmarks.
The top of the graph visualises traffic transiting around the humanservices.gov.au domain. Node size and colour represent the Eigenvector measure for the page in relation to all other nodes in the graph. Eigenvector centrality measures the importance of webpages in the graph. Webpages with a high Eigenvector are connected to other pages that are also important. Google’s PageRank algorithm is a variant of Eigenvector Centrality.
How government connects services
This was created using website links from the DTA's Australian Government Web Crawl dataset. It shows domain-to-domain connections with more than 1250 links between them. Isolated domains (those without any links) have been removed. Node size and colour reflect their importance and influence; larger and darker nodes are those that have more connections to others. Graph layout uses the Yifan Hu proportional algorithm and was generated using the Gephi software package.
Visual Analysis of webpages per Portfolio
This ‘Nightingale diagram’ provides a birds-eye view of over 9 million URLs gathered from the Australian Government Web Crawl dataset. The 19 sectors of this graph are divided into portfolios, all containing multiple agencies and websites. The multiple colours of each portfolio represent different websites.
The inner, middle and outer rings refer to volume of websites for each portfolio contained in the webcrawl dataset. On the left is a close up of the apex, showing the smallest minority of webpages per portfolio.
Find out how analytics data and the Observatory team can help inform and evaluate policy.
To contact us about partnerships, email email@example.com.