Why should I take this certification?
Business intelligence is an important new tool for modern businesses. Technological advancements can now give businesses ways to analyze data that they could never do before. With such advances, organisations can make decisions more effectively about pricing, marketing, products and more than they have ever been able to in the past.
Major corporations are starting to use BI to determine what products should be advertised to specific customers & to more efficiently run the industrial maintenance schedules. Business intelligence is useful in many such settings and a lot more.
How will I benefit from this certification?
Vskills Business Intelligence certification tests the candidates on various areas in business intelligence which includes knowledge of planning, designing, implementing and maintaining the organization’s data warehouse, data mining, data analytics and data intelligence for better decision making. This certification will teach you the basics of business intelligence, it's application & usefulness to businesses.
You will gain the skills to improve performance using data, statistical & quantitative analysis & predictive modeling to make important decisions.
Companies that hire Business Intelligence Professional
Vskills Certified candidates will find employment in various companies like Aricent, HCL, KPIT, TCS, Global logic, Citrix
Business Intelligence Table of Contents
https://www.vskills.in/certification/Certified-Business-Intelligence-Professional-Table-of-Contents
Business Intelligence Course Outline
https://www.vskills.in/certification/Certified-Business-Intelligence-Professional-Course-Outline
Business Intelligence Tutorial
https://www.vskills.in/certification/tutorial/information-technology/certified-business-intelligence-professional/
Business Intelligence Sample Questions
https://www.vskills.in/certification/Certified-Business-Intelligence-Professional-Sample-Questions
Business Intelligence Mock Test
https://www.vskills.in/practice/skills/Management/Business-Intelligence
Apply for Business Intelligence Certification
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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
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
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