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
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TABLE OF CONTENT
- Need and benefits
- Technical terms
- BI life cycle and management systems functions
- ERP and BI
- SCM and BI
- E-commerce and BI
- 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
- Dimensional modeling and metadata
- Concepts and terminologies
- Techniques used (neural network, statistics, fuzzy logic, genetic algorithms
- Business intelligence economics
- Cost Matrix, SLA and ROI
- Risk Mitigation
- Business problem assessment
- Focusing pertinent information
- Desired outcome specification
- Data and architecture design
- Hardware and Software Selection
- Generate data warehouse matrix
- Dimensional modeling and ETL
- 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
- Security Policy, user privileges and security tools
- Backup and Recovery
- Monitoring and managing data growth
- Observing dashboards
- Assessing KPI and scorecard
- Future Trends
- Case Studies
- 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
- 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
- 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)
- Explaining the concept of data mining and various techniques like neural networks, decision trees, etc.
- 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.
- 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
- 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
- Explaining the technique for performance management by observing dashboards , assessing key performance indicators and using scorecard
- Illustrating the future trends in BI as cloud computing, collaboration, mobility, etc.
- Detailing various case studies of BI