IBM Planning Analytics: Design and Develop Models in Performance Modeler (v2.0)

1: Overview of IBM Planning Analytics
• review financial performance management
• identify the Planning Analytics position in a performance management system
• describe the IBM Planning Analytics components and architecture
• explore IBM Planning Analytics applications
• explore the IBM Planning Analytics environment
• manage and organize a model

2: Create and customize dimensions
• review cubes, dimensions, and elements
• create dimensions manually
• import dimensions
• edit dimensions
• create dimension calculations
• use Guided Import to create a dimension

3: Create and customize cubes
• construct a new cube
• discuss cube properties
• edit a cube structure
• review and use a pick list
• create cube calculations

4: Import data
• identify data sources
• create processes to load data
• create a process to delete data in a cube
• create processes to update and maintain the model

5: Share data across cubes with links
• discuss and list types of links
• create and modify links
• review rule- and process-based links

6: Complete the income statement model
• discuss the model development process
• complete objects for the model
• review tools to aid in model development

7: Create applications
• explain the application types
• access an IBM Planning Analytics application
• create a new application
• set the available clients
• apply security in the application
• activate and de-activate an application

8: Additional modeling techniques
• create dynamic subsets
• use dimension functions
• implement business logic
• improve cube performance
• use Planning Analytics utilities

9: Convert currencies
• discuss currency challenges
• review control cubes
• create rules for currency conversion
• use Planning Analytics techniques to reduce maintenance

10: Model data with Architect
• describe IBM Planning Analytics Architect
• record MDX queries
• customize drill-through paths

11: Model for different fiscal requirements
• discuss time considerations
• use discrete time dimensions
• implement a continuous time dimension model

Additional Exercise (Optional)
Optimize and tune models (Optional)
Customize business rules (Optional)
Optimize rule performance (Optional)