How the University of Washington turns BI into an Amazon-like experience


How the University of Washington turns BI into an Amazon-like experience

Higher education administrators and faculty regularly interact with efficient and innovative commercial technologies in their everyday lives. These technologies simplify disparate decisions ranging from finding the “right” gift to navigating a congestion-free commute.

It comes as no surprise that administrators and faculty then expect nothing less of technology when they’re on campus. Unfortunately, many campus systems simply aren’t as user-friendly as Amazon and Google.

Help campus members find the right data at the right time

The business intelligence leadership team at the University of Washington recognized this gap and wanted to ensure that campus members could easily navigate their BI portal to get the right data at the right time. However, one major roadblock to this was, with hundreds of reports available, users were unlikely to be aware of those reports that would be most helpful to them.

To assist users, the University of Washington created a personalized report recommendation engine in their business intelligence portal. The portal uses simple recommendation algorithms to provide users with insight into which reports may be useful to them. This is similar to an experience on Amazon, with recommendations for the consumer based on what others with similar interests are viewing (and buying). The report recommendation feature is based on three major tenets:

  • Multiple reports that are run by one user, in close time proximity, may be related, as the user may be working on the same project at the time
  • When a pair of reports is run by many users in close time proximity, they are highly likely to be related, as the report may be more useful at a certain time of the year or month
  • Users who run a report often would benefit from related reports, especially if they have not run it recently (and therefore may not know about it)

Learn More

To find out more about the algorithms used to recommend reports to campus members at the University of Washington, ITF members can log in to download the personalized recommendation engine resource, available as part of our Common Currency toolkit.

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