Course Outline


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

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