Business intelligence (BI) is a set of theories, methodologies, architectures, and technologies that transform raw data into meaningful and useful information for business purposes. BI can handle enormous amounts of unstructured data to help identify, develop and otherwise create new opportunities. BI, in simple words, makes interpreting voluminous data friendly. Making use of new opportunities and implementing an effective strategy can provide a competitive market advantage and long-term stability.BI technologies provide historical, current and predictive views of business operations. Common functions of business intelligence technologies are reporting, online analytical processing, analytics, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics and prescriptive analytics.
- A basic understanding of dimensional modeling (star schema) for data warehouses.
- The ability to create Integration Services packages that include control flows and data flows.
- The ability to create a basic multidimensional cube with Analysis Services.
- The ability to create a basic tabular model with PowerPivot and Analysis Services.
- The ability to create Reporting Services reports with Report Designer.
- It is a 20 days program and extends up to 2hrs each.
- The format is 40% theory, 60% Hands-on.
- It is a 5 days program and extends up to 8hrs each.
- The format is 40% theory, 60% Hands-on.
Private Classroom arranged on request and minimum attendies for batch is 4.
- 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 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
- 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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