IBM Planning Analytics v2.1.x: Design and Develop Models in PAW

This course is destinated to Analysts.

After completing this course, learners should be able to:

• Describe TM1 server architecture and in-memory processing
• Create and access a TM1 server using Planning Analytics Workspace
• Create and edit dimensions using Planning Analytics Workspace
• Design cubes based on business requirements
• Explain TurboIntegrator process components
• Combine rules and dimension structures to perform calculations
• Create effective feeder statements
• Use DB functions to link cubes with different dimensionality
• Enable users to navigate to related detail data
• Implement advanced rule-based modeling techniques
• Design maintainable currency conversion models
• Implement models that support rolling forecasts

Unit 1: Overview of IBM Planning Analytics

Explain the role of IBM Planning Analytics in financial performance management

Identify core Planning Analytics components and services

Describe TM1 server architecture and in-memory processing

Navigate the Planning Analytics Workspace modeling environment

Create and access a TM1 server using Planning Analytics Workspace

Unit 2: Creating Dimensions in PAW

Describe the role of dimensions and members in a Planning Analytics model

Create and edit dimensions using Planning Analytics Workspace

Import dimension structures from external files

Use member attributes to enhance usability and reporting

Explain how hierarchies support alternative rollups of data

Unit 3: Building Multidimensional Cubes

Explain what cubes are and how they store data

Design cubes based on business requirements

Create cubes using existing dimensions

Apply cube properties and attributes appropriately

Implement pick lists to control data entry

Unit 4: Data Loading and Maintenance

Identify supported data sources for Planning Analytics

Explain TurboIntegrator process components

Create and run processes to load cube and dimension data

Maintain data accuracy using delete and accumulate techniques

Schedule processes using chores

Unit 5: Building and Applying Business Rules

Describe how rules are evaluated in Planning Analytics

Write rules using area definitions and functions

Apply rules at leaf and consolidated levels

Use rules to share data between cubes

Combine rules and dimension structures to perform calculations

Unit 6: Optimizing Rule Performance

Explain how sparsity affects cube performance

Use SKIPCHECK to optimize rule evaluation

Create effective feeder statements

Troubleshoot consolidation and feeder issues

Trace rule-driven and consolidated values

Unit 7: Efficient Data Transfer and Transformation

Use DB functions to link cubes with different dimensionality

Describe TurboIntegrator scripting best practices

Transfer and transform data between cubes

Manage uneven hierarchies using processes

Unit 8: Customizing Drill-Through Navigation

Explain the Planning Analytics drill-through process

Create drill processes using TurboIntegrator

Associate drill processes with cube cells

Enable users to navigate to related detail data

Unit 9: Advanced Rule Modeling Techniques

Describe virtual cubes and their use cases

Use lookup cubes in calculations

Apply relative proportional spreading

Reference attributes within rules

Implement advanced rule-based modeling techniques

Unit 10: Designing Flexible Currency Conversion Models

Explain common currency conversion requirements

Use control cubes and lookup techniques

Create rules to convert currencies

Design maintainable currency conversion models

Unit 11: Flexible Time Modeling Strategies

Describe discrete and continuous time modeling approaches

Select appropriate time structures based on requirements

Implement models that support rolling forecasts

Extend existing models for fiscal variations

Participants should have the following:

• A general understanding of business analytics or performance management concepts
• Basic familiarity with IBM Planning Analytics terminology
• Experience navigating web-based applications

• None

• Non recommended

• This course has labs provided by IBM.