Certified Business Intelligence Professional

TABLE OF CONTENT

Introduction

• Evolution
• Need and benefits
• Technical terms

Integration

• BI life cycle and management systems functions
• ERP and BI
• SCM and BI
• E-commerce and BI

Data Management

• Data Management
• Reporting and Querying
• BI and MDM
• Knowledge Management

OLAP (Online analytical processing)

• Evolution, Features and functions
• Multidimensional analysis
• Data drill-in and drill-up
• OLAP Models (ROLAP and MOLAP) and applications

Dashboards

• EIS
• KPI
• BI Dashboard

Data Warehousing

• Dimensional modeling and metadata
• ETL

Data Analytics

• Concepts and terminologies
• Techniques used (neural network, statistics, fuzzy logic, genetic algorithms

Value Proposition

• Business intelligence economics
• Cost Matrix, SLA and ROI
• Risk Mitigation

Requirement Assessment

• Business problem assessment
• Focusing pertinent information
• Desired outcome specification

Design

• Data and architecture design
• Hardware and Software Selection
• Generate data warehouse matrix
• Dimensional modeling and ETL

Implementation

• Physical Design
• Physical Storage (SAN, RAID, etc.)
• Indexing (B-Tree, Clustered, etc.)
• Data partitioning and clustering for performance
• Analytics criteria selection
• OLAP tools and data slicing or dicing

Post-Implementation

• Security Policy, user privileges and security tools
• Backup and Recovery
• Monitoring and managing data growth

Performance Measurement

• Observing dashboards
• Assessing KPI and scorecard

Advanced BI

• Future Trends
• Case Studies

COURSE OUTLINE

Introduction

• Understanding the evolution, need and benefits of business intelligence
• Explaining various technical terminologies used with business intelligence

Integration

• Describing the business intelligence lifecycle and the functions of different management systems in an organization
• Illustrating the dependability and integration of ERP, SCM and E-commerce with BI

Data Management

• Detailing the process of data management in an organization
• Describing the Usage of BI for Reporting and Querying
• Understanding knowledge management and master data management (MDM) application in BI for data management

OLAP (Online analytical processing)

• Explaining the evolution, features and functions of OLAP
• Detailing the multidimensional analysis for OLAP implementation
• Illustrating the concept of data drill-in and drill-up
• Describing the various OLAP models as ROLAP and MOLAP and their applications

Dashboards

• Understanding executive information system (EIS), key performance indicator (KPI) and dashboards for BI management and control

Data Warehousing

• Describing the process of data design and dimensional modeling in data warehousing
• Explaining the process of managing metadata and focusing on the upcoming trends
• Detailing the need and techniques for extract, transform and load (ETL)

Data Mining

• Explaining the concept of data mining and various techniques like neural networks, decision trees, etc.

Data Analytics

• Describing the concepts and technical terminologies used in data analytics
• Detailing the different techniques used for data analytics like neural network, statistics, fuzzy logic, genetic algorithms, etc.

Value Proposition

• Understanding data mining and warehousing economics and viability derivation
• Illustrating concepts of cost matrix, SLA and ROI applied to data warehousing
• Describing the importance of risk mitigation in data mining and warehousing

Requirement Assessment

• Understanding the process for assessing the business problem
• Explaining the technique to specify desired outcomes and focus pertinent information

Design

• Illustrating the data design and architecture design process
• Describing considerations for hardware and software selection for BI
• Detailing the steps to generate data warehouse matrix and analyze dimensional modeling and ETL for BI

Implementation

• Detailing the process of physical design for implementing BI
• Explaining the various physical storage techniques like SAN, RAID, etc.
• Describing the different indexing techniques like B-Tree, clustered, etc. for optimization
• Understanding partitioning of data and clustering for improved performance
• Illustrating steps to select analytics criteria and usage of OLAP tools with data slicing or dicing in implementing BI in an organization

Post-Implementation

• Detailing the concepts and implementation of security policy, user privileges and usage of various security tools
• Describing the process for backup and recovery of data
• Illustrating the process to monitor and manage data growth in BI

Performance Measurement

• Explaining the technique for performance management by observing dashboards , assessing key performance indicators and using scorecard

Advanced BI

• Illustrating the future trends in BI as cloud computing, collaboration, mobility, etc.
• Detailing various case studies of BI

Write a review