Analytics apps don't tell you much about usage behavior. You might be able to see how many users performed an event, or how many times they did it. But none of the analytics packages out there are good at showing you how often people do things. Are they using to-dos once a week? Every day? Only signing into the app once a month but happily paying for years?
Time matters. You can't understand usage without time.
Here I describe an approach for defining new information architectures for large organizational websites managed by many stakeholder groups.
Broadly speaking, there are four general phases to the approach:
Auditing. Begin by immersing yourself in existing content and encourage stakeholders to adopt a critical, audience-minded perspective of their content.
Diagramming. Work with stakeholders to develop new conceptual categories that better serve audiences and organizational direction.
Elaborating. Think through content in detail and test new categories against specific instances and edge cases.
Producing. Prepare content teams for production using a shared database of new sitemap pages and editorial considerations that you’ve developed incrementally.
There is increasing concern that most current published research findings are false. The probability that a research claim is true may depend on study power and bias, the number of other studies on the same question, and, importantly, the ratio of true to no relationships among the relationships probed in each scientific field. In this framework, a research finding is less likely to be true when the studies conducted in a field are smaller; when effect sizes are smaller; when there is a greater number and lesser preselection of tested relationships; where there is greater flexibility in designs, definitions, outcomes, and analytical modes; when there is greater financial and other interest and prejudice; and when more teams are involved in a scientific field in chase of statistical significance.