Analysis of tasks by multiple indicators and parameters ensures weighted decision making because it allows gaining a better understanding of task state and performance. OLAP cube is an analytical method that makes it possible to use multi-dimensional task analysis. OLAP cube uses multiple attributes of a task as the input data to analyze and measure the task’s performance. The method then generates aggregated information about the task in the form of a cube which visualizes the scalability of task intelligence.
The advantage of using OLAP cube in task analysis is that this method lets use task data aggregated by multiple measures and categorized by three or more dimensions to create a parametric task behavior model for performance reporting and further decision making. OLAP cube is best for analyzing tasks that have multiple attributes; for other tasks with several attributes this analytical method appears to be too complicated.
Multi-dimensional task analysis through OLAP cube involves the following steps:
- Identify task attributes to be analyzed. These attributes are called "measures"
- Think how measures relate to each other
- Categorize related and similar measures into groups. These groups will be “dimensions” of the cube (if there are more than 3 dimensions, there will be an OLAP hypercube)
- Create the fact table and the dimension table
- Use a special visualization tool to draw OLAP cube
- Carry out operations (Slicing, Dicing, Drilling down/up, Rolling up, Pivoting) to spin the cube and see tasks in various perspectives
- Design an analytical report that includes task intelligence.