BIDM Session 01.pdf
- 1. Business Intelligence and Data Mining Session 01 1
- 2. Variety of Terms • Business Intelligence • Data Mining • Business Analytics • Machine Learning • Big Data • Data Science 2
- 3. Success Factors • Processing Capabilities • Storage Capabilities • Networking Capabilities 3
- 4. The Core Disciplines • Mathematics • Statistics • Computer Science 4
- 5. Business Analytics is the use of: data, information technology, statistical analysis, quantitative methods, and mathematical or computer-based models to help managers gain improved insight about their business operations and make better, fact-based decisions. What is Business Analytics? 5
- 6. Why there are so many techniques? - Ad-hoc querying and reporting - Data mining techniques - Structured data, typical sources - Small to mid-size datasets - Optimizations and predictive analytics - Complex statistical analysis - All types of data, and many sources - Very large datasets - More of a real-time 6
- 7. Four Types of Analytics 7
- 8. The Progression of Analytics 8
- 9. Example 1.1 Retail Markdown Decisions Most department stores clear seasonal inventory by reducing prices. The question is: When to reduce the price and by how much? Descriptive analytics: examine historical data for similar products (prices, units sold, advertising, …) Diagnostic Analytics: Analyze the relationships between data for association and causation. Predictive analytics: predict sales based on price Prescriptive analytics: find the best sets of pricing and advertising to maximize sales revenue Scope of Business Analytics 9
- 10. Scope of Business Analytics Analytics in Practice: Harrah’s Entertainment •Harrah’s owns numerous hotels and casinos •Uses analytics to: - forecast demand for rooms - segment customers by gaming activities •Uses prescriptive models to: - set room rates - allocate rooms - offer perks and rewards to customers 10
- 11. What Can Computers Learn? 11
- 12. Four Levels of Learning Facts Concepts Procedures Principles 12
- 13. Facts A fact is a simple statement of truth. 13
- 14. Concepts A concept is a set of objects, symbols, or events grouped together because they share certain characteristics. 14
- 15. Procedures A procedure is a step-by-step course of action to achieve a goal. 15
- 16. Principles Principles are general truths or laws that are basic to other truths. 16
- 17. Computers & Learning Computers are good at retaining facts and learning concepts by following specific principles and procedures. Concepts are the output of a data mining session. 17
- 18. Three Concept Views Classical View Probabilistic View Exemplar View 18
- 19. Classical View All concepts have definite defining properties. 19
- 20. Probabilistic View People store and recall concepts as generalizations created by observations. 20
- 21. Exemplar View People store and recall likely concept exemplars that are used to classify unknown instances. 21
- 22. Example The figure below illustrates five initial cases (for n=1, 2, 3, 4, and 5), where n points are placed on a circle and every point is connected by a line to every other point so as to divide the circle into many pieces… No pieces = 1 2 4 8 16 n = 1 2 3 4 5 General Model is: (n4 - 6n3 + 23n2 - 18n + 24) / 24 How many pieces if there are 6 points? What’s the General model for n points? 31 22
- 23. Data Mining: Definition The process of employing one or more computer learning techniques to automatically analyze and extract knowledge from data. All Data Mining methods use induction based learning. 23
- 24. Induction-based Learning The process of forming general concept definitions by observing specific examples of concepts to be learned. 24
- 25. BI Reporting Vs. Analytics “Business Intelligence is needed to run the business while Business Analytics is needed to change the business.” “The difference between Business Intelligence is looking in the rearview mirror and using historical data from one minute ago to many years ago. Business Analytics is looking in front of you to see what is going to happen. This will help you anticipate in what’s coming, while BI will tell you what happened. ” 25
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- 30. Metrics are used to quantify performance. Measures are numerical values of metrics. Discrete metrics involve counting - on time or not on time - number or proportion of on time deliveries Continuous metrics are measured on a continuum - delivery time - package weight - purchase price Data for Business Analytics 30
- 31. Measurement Levels 31 ORDER DISTANCE ORIGIN QUALITATIVE NOMINAL X X X ORDINAL ✔ X X QUANTITATIVE INTERVAL ✔ ✔ X RATIO ✔ ✔ ✔
- 32. OLAP • Refers to the process of quickly conducting complex, multidimensional analyses of data stored in a database that is optimized for retrieval, typically using graphical software tools. • Measures and Dimensions 32
- 33. OLAP Operations • SLICING • DICING • ROLL UP • DRILL DOWN 33