1. Introduction to IBM Cognos Analytics
- Describe IBM Cognos Analytics and its position within an analytics solution
- Describe IBM Cognos Analytics components
- Describe IBM Cognos Analytics at a high level
- Explain how to extend IBM Cognos
2. Identifying common data structures
- Define the role of a metadata model in Cognos Analytics
- Distinguish the characteristics of common data structures
- Understand the relative merits of each model type
- Examine relationships and cardinality
- Identify different data traps
- Identify data access strategies
3. Defining requirements
- Examine key modeling recommendations
- Define reporting requirements
- Explore data sources to identify data access strategies
- Identify the advantages of modeling metadata as a star schema
- Model in layers
4. Creating a baseline project
- Follow the IBM Cognos and Framework Manager workflow processes
- Define a project and its structure
- Describe the Framework Manager environment
- Create a baseline project
- Enhance the model with additional metadata
5. Preparing reusable metadata
- Verify relationships and query item properties
- Create efficient filters by configuring prompt properties
6. Modeling for predictable results: Identifying reporting Issues
- Describe multi-fact queries and when full outer joins are appropriate
- Describe how IBM Cognos uses cardinality
- Identify reporting traps
- Use tools to analyze the model
7: Modeling for predictable results: Virtual star schemas
- Understand the benefits of using model query subjects
- Use aliases to avoid ambiguous joins
- Merge query subjects to create as view behavior
- Resolve a recursive relationship
- Create a complex relationship expression
8. Modeling for predictable results: consolidate metadata
- Create virtual dimensions to resolve fact-to-fact joins
- Create a consolidated modeling layer for presentation purposes
- Consolidate snowflake dimensions with model query subjects
- Simplify facts by hiding unnecessary codes
9. Creating calculations and filters
- Use calculations to create commonly-needed query items for authors
- Use static filters to reduce the data returned
- Use macros and parameters in calculations and filters to dynamically control the data returned
10. Implementing a time dimension
- Make time-based queries simple to author by implementing a time dimension
- Resolve confusion caused by multiple relationships between a time dimension and another table
11. Specifying determinants
- Use determinants to specify multiple levels of granularity and prevent double-counting
12. Creating the presentation view
- Identify the dimensions associated with a fact table
- Identify conformed vs. non-conformed dimensions
- Create star schema groupings to provide authors with logical groupings of query subjects
- Rapidly create a model using the Model Design Accelerator
- Rapidly create a model using the Model Design Accelerator
13. Working with different query subject types
- Identify the effects of modifying query subjects on generated SQL
- Specify two types of stored procedure query subjects
- Use prompt values to accept user input
14. Setting Security in Framework Manager
- Examine the IBM Cognos security environment
- Restrict access to packages
- Create and apply security filters
- Restrict access to objects in the model
15. Creating Analysis objects
- Apply dimensional information to relational metadata to enable OLAP-style queries
- Sort members for presentation and predictability
- Define members and member unique names
- Identify changes that impact a MUN
16. Managing OLAP Data Sources
- Connect to an OLAP data source (cube) in a Framework Manager project
- Publish an OLAP model
- Publish a model with multiple OLAP data sources
- Publish a model with an OLAP data source and a relational data source
17. Advanced generated SQL concepts and complex queries
- Governors that affect SQL generation
- Stitch query SQL
- Conformed and non-conformed dimensions in generated SQL
- Multi-fact/multi-grain stitch query SQL
- Variances in IBM Cognos Analytics - Reporting generated SQL
- Dimensionally modeled relational SQL generation
- Cross join SQL
- Various results sets for multi-fact queries
18. Using advanced parameterization techniques in Framework Manger
- Identify environment and model session parameters
- Leverage session, model, and custom parameters
- Create prompt macros
- Leverage macro functions associated with security
19. Model maintenance and extensibility
- Perform basic maintenance and management on a model
- Remap metadata to another source
- Import and link a second data source
- Run scripts to automate or update a model
- Create a model report
20. Optimizing and tuning Framework Manager models
- Identify how minimized SQL affects model performance
- Use governors to set limits on query execution
- Identify the impact of rollup processing on aggregation
- Apply design mode filters
- Limit the number of data source connections
- Use the quality of service indicator
21. Working in a Multi-Modeler Environment
- Segment and link a project
- Branch a project and merge results
22. Managing packages in Framework Manager
- Specify package languages and function sets
- Control model versioning
- Nest packages
Appendix A. Additional modeling techniques
- Leverage a user defined function
- Identify the purpose of query sets
- Use source control to manage Framework Manager files
Appendix B. Modeling multilingual metadata
- Customize metadata for a multilingual audience