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Essentials for IBM Cognos Analytics 11 is a blended offering consisting of five-days of instructor-led training and 16.5 hours of Web-based, self-paced training. This accelerated offering is intended for core project team members wanting to acquire a broad understanding of IBM Cognos Analytic platform implementation. During the ILT segments, participants will perform hands-on demonstrations and exercises that cover three essential topic areas: modeling, report authoring, and administration of IBM Cognos Analytics.


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  • Overview of IBM Cognos Analytics
  • Common Data Structures
  • Items to consider when Defining Requirements
  • Baseline Project Creation
  • How to Prepare reusable metadata
  • Predictable Results Modeling
  • Calculation and Filter Creation
  • Time Dimension Implementation
  • Specifying Determinants
  • Presentation Views and their Dimensions
  • Analysis Objects Creation
  • Reporting Type Components
  • List Report Creation
  • How to use Filters
  • Crosstab Report Creation
  • Presenting Graphical Data
  • Using Prompts
  • Report Calculations
  • Conditional Formatting
  • Drill-Through Definitions
  • Administration Introduction
  • Reviewing the Architecture
  • Securing the Environment
  • Managing Activities and Content

Note: While class exercises are performed within a Cognos Analytics v11 environment, our expert instructors can provide guidance on all versions and their differences.

  • Knowledge of common industry-standard data structures and design
  • Experience with SQL
  • Experience gathering requirements and analyzing data.
  • Knowledge of Web application server architectures
  • Security systems administration
  • Knowledge of your business requirements
  • Experience using the Windows operating system
  • Experience using a web browser

  • Core project team members
  • Project managers
  • Technical analysts
  • Developers

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 Analytics

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

13. 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

14. Introduction to IBM Cognos Analytics - Reporting

  • Examine IBM Cognos Analytics - Reporting and its interface
  • Explore different report types
  • Create reports in preview or design mode

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