Big Data, Business Intelligence and Data Analytics
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Big Data, Business Intelligence and Data Analytic. Tools and Technology. Impact and Visuality
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Big Data, Business Intelligence and Data Analytics
- 1. Business Intelligence and Data Analytics Ali Arshad 11i-0242 Atif Saddique 12i-1614 Hasan Ibrahim 11i- 0128
- 2. What is Business Intelligence? BI is a technology driven process for analyzing data and extracting useful information to help corporate executives, project managers and end users make informed decisions.
- 3. What is Big Data?
- 4. What is Data/Business Analytics? Business analytics is the extraction of an organization’s data from statistical perspective. This process is used to gain useful insights and optimize business processes.
- 5. Concepts of Theory, Methodology, Architecture, Framework, Technology, Tool, Environment
- 6. Tool vs. Technology • BI tools provide trend analysis rather than simply displaying data statically. They also help in combing data from multiple sources. Some current popular Bi tools are Tableau, QlikView, iDashboard etc. • BI technologies provide historical, current and predictive views of business operations. Their most common functions are reporting, OLAP, data mining, bench marking and many more.
- 7. Architecture vs. Framework • BI Architecture is set of frameworks for organizing the data, information management and technology components that are used to build BI systems for reporting and data analytics. • BI framework provides standards and best practices required to ensure BI reporting and analysis meets organizational requirements.
- 8. Challenges of BI, Big Data, Data Analytics • Continuous Availability • Data Security and Workload Diversity • Cost, BI has evolved and everybody has some form of BI in place now, as it is becoming a fairly substantial cost item. The overall cost of BI – the cost of technology, upkeep and implementation – is certainly one of the challenges that implementers are facing. • The number of users are dramatically increasing. • New areas of Operational BI and new Data sources. • Performance and Scalability.
- 9. Leading Vendors providing solutions for BI, Big Data, Data Analytics • Google • Information Builder • Microsoft • MicroStrategy • Oracle • SAS • Business Objects (owned by SAP) • Cognos (owned by IBM) • EMC • HP • Teradata
- 10. Conclusion
Notes de l'éditeur
- Theory: Business Intelligence can be described as a value proposition that helps organizations in their decision-making processes. Methodology: Utilizing a lifecycle approach, you the designers and managers of large data warehouses will achieve your goals more quickly. You will build effective data warehouses that make your organization's information accessible and consistent, turn data into an asset and serve as the foundation for decision making. Ralph Kimball's Data Warehouse Lifecycle Toolkit Architecture: A business intelligence architecture is a framework for organizing the data, information management and technology components that are used to build business intelligence (BI) systems for reporting and data analytics. Framework: A business intelligence framework provides the policy direction, standards and best practices required to ensure that business intelligence reporting and analysis meets organizational requirements. It is comprised of: Data management (Governance) standards and best practices Project management framework Metrics management Technology: Business intelligence (BI) software is a collection of decision support technologies for the enterprise aimed at enabling knowledge workers such as executives, managers, and analysts to make better and faster decisions Tools: Business intelligence tools are a type of application software designed to retrieve, analyze, transform and report data for business intelligence. Environment: A BI environment involves business model, data Model, data sources, ETL tools needed to transform and organize data into useful information