![]() The solution is based around a new measure called ‘Total Training Hours’ on the course_bookings table. I also want to see total training hours delivered by each course. In other words, I want to see the rows excluded by the filter that’s applied when you select a school in the slicer. I wanted another table visualisation on the same report to show courses delivered to teachers in similar schools in the same geographical area as the one selected in the slicer - but not including the one selected. Power BI creates relationships between the tables (based on each school’s unique reference number) and I can quickly build the basic report. I connect to the data warehouse from Power BI and I can then see three tables - course_bookings, schools and website_regsitrations. The real work - loading and cleaning data from different operational systems- is all done in the data warehouse. They can use a slicer to pick a school and then see a summary of the courses they’ve delivered to teachers in the school and also website registrations - the teachers who subsequently registered on our client’s website to get access to free course materials.Ĭreating that simple report was easy. ![]() Our client wants to see course enrolments and website registrations for a single school. The front end is a Power BI reporting solution - a set of management information reports for drilling into data about course enrolments, website registrations and other kinds of engagement with teachers. The back end data warehouse is built in SQL Server and hosted in Azure. We’ve developed a business intelligence solution for a training organisation that works with schools and teachers. This was a problem that took me a little bit longer to solve than I expected.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |