Course Description
Overview
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.
Audience
Core Project Team members
• Project Managers
• Technical Analysts
• Developers
Prerequisites
• 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
Key topics
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
• Create a simple, sorted, and formatted report
• Examine dimensionally modelled and dimensional data sources
• Explore how data items are added queries
• Examine personal data sources and data modules
15: Creating list reports
• Group, format, and sort list reports
• Describe options for aggregating data
• Create a multi-fact query
• Create a report with repeated data
16: Focusing reports using filters
• Create filters to narrow the focus of reports
• Examine detail filters and summary filters
• Determine when to apply filters on aggregate data
17: Creating crosstab reports
• Format and sort crosstab reports
• Create complex crosstabs using drag and drop functionality
• Create crosstabs using unrelated data items
18: Present data graphically
• Create charts containing peer and nested columns
• Present data using different chart type options
• Add context to charts
• Create and reuse custom chart palettes
• Introduce visualization
• Present key data in a single dashboard report
19: Focusing Reports Using Prompts
• Identify various prompt types
• Use parameters and prompts to focus data
• Search for prompt types
• Navigate between pages
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