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Big Data and Business Intelligence.pdfyws
- 1. Bid Data andBusiness Intelligence
- 2. Learning Objectives Upon successfulcompletion of this chapter, you will be able to: • Explain the difference between BI, Analytics, Data Marts and Big Data. • Define the characteristics of data for good decision making. • Describe what Data Mining is. • Explain market basket and cluster analysis.
- 3. Business Analytics, BI,Big Data, Data Mining - What’s the difference? • Business Analytics – Tools to explore past data to gain insight into future business decisions. • BI – Tools and techniques to turn data into meaningful information. • Big Data –data sets that are so large or complex that traditional data processing applications are inadequate. • Data Mining - Tools for discovering patterns in large data sets.
- 4. Businesses Need Supportfor Decision Making • Uncertain economics • Rapidly changing environments • Global competition • Demanding customers • Taking advantage of information acquired by companies is a Critical Success Factor.
- 5. Characteristics of Datafor Good Decision Making Source: speakingdata blog
- 6. The Information Gap •The shortfall between gathering information and using it for decision making. – Firms have inadequate data warehouses. – Business Analysts spend 2 days a week gathering and formatting data, instead of performing analysis. (Data Warehousing Institute). – Business Intelligence (BI) seeks to bridge the information gap.
- 7. Data Mining • “Datamining is an interdisciplinary subfield of computer science. It is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems.” - Wikipedia • Examining large databases to produce new information. – Uses statistical methods and artificial intelligence to analyze data. – Finds hidden features of the data that were not yet known.
- 8. BI • Tools andtechniques to turn data into meaningful information. – Process: Methods used by the organization to turn data into knowledge. – Product: Information that allows businesses to make decisions.
- 9. BI Applications • CustomerAnalytics • Human Capital Productivity Analysis • Business Productivity Analytics • Sales Channel Analytics • Supply Chain Analytics • Behavior Analytics
- 10. What is BusinessIntelligence? • Collecting and refining information from many sources (internal and external) • Analyzing and presenting the information in useful ways (dashboards, visualizations) • So that people can make better decisions • That help build and retain competitive advantage.
- 11. Klipfolio - sampleof a marketing dashboard
- 12. FitBit – HealthDashboard
- 13. BI Initiatives • 70%of senior executives report that analytics will be important for competitive advantage. Only 2% feel that they’ve achieved competitive advantage. (zassociates report) • 70-80% of BI projects fail because of poor communication and not understanding what to ask. (Goodwin, 2010) • 60-70% of BI projects fail because of technology, culture and lack of infrastructure (Lapu, 2007)
- 14. Evolution of BI Source:Delaware Consulting
- 15. Evolution of BI(contd.) Source: b-eye-network.com
- 16. Data Warehouse • Collectionof data from multiple sources (internal and external) • Summary, historical and raw data from operations. • Data “cleaning” before use. • Stored independently from operational data. • Broken down into DataMarts for use. Chapter 4 of ISBB Text
- 17. 5 Tasks ofData Mining in Business • Classification – Categorizing data into actionable groups. (ex. loan applicants) • Estimation – Response rates, probabilities of responses. • Prediction – Predicting customer behavior. • Affinity Grouping – What items or services are customers likely to purchase together? • Description – Finding interesting patterns.
- 18. Data Mining Techniques •Market Basket Analysis • Cluster Analysis • Decision Trees and Rule Induction • Neural Networks
- 19. Market Basket Analysis •Finding patterns or sequences in the way that people purchase products and services. • Walmart Analytics – Obvious: People who buy Gin also buy tonic. – Non-obvious: Men who bought diapers would also purchase beer.
- 20. Cluster Analysis • Groupingdata into like clusters based on specific attributes. • Examples – Crime map clusters to better deploy police. – Where to build a cellular tower. – Outbreaks of Zika virus.
- 21. Summary • Explained BI,Analytics, Data Marts and Big Data. • Defined the characteristics of data for good decision making. • Described data mining in detail. • Explained and gave examples of market basket and cluster analysis.