A thorough analysis of task performance can be carried out with use of OLAP cube. The dimensions of such a cube create hierarchies of task attributes, and through summarizing each of the dimensions it is possible to understand what task attributes of what hierarchy cause current failing/winning task performance.
Hierarchies in cube dimensions provide a deeper view into task performance. An analyst can focus on certain task attributes to determine how these attributes relate to each other and what effect they produce to overall performance of associated tasks. In essence, hierarchies show dependencies between task attributes. It is a great tool to retrieve valued data for an in-depth performance analysis of tasks within OLAP cube models.
Here are a few guidelines on how to analyze task performance through hierarchies:
- Have tasks to be analyzed for performance
- Identify attributes of these tasks
- Design the fact table and the dimension table (both of the tables are based upon task attributes)
- Build OLAP cube
- Summarize down cube dimensions to lowest possible level
- Create a drill-down hierarchy for the summary
- Review the hierarchy to understand how task attributes are dependent upon each other
- Identify what desired result is preset for task attributes of the hierarchy
- Compare the actual result with the desired result
- Analyze hierarchy dependencies to figure out which of the attributes cause performance gaps (if any)
- Create a task analytics report that describes the hierarchy.