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The document discusses key concepts in business analytics including data, metrics, models, and the problem solving process. It provides examples of different types of models and analytics techniques. Descriptive analytics describe past data, predictive analytics forecast the future, and prescriptive analytics recommend decisions. The goal of business analytics is to help managers make better, fact-based decisions by analyzing data and developing models.

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Ahmed Imran Kabir Lecturer School of Business and Economics United International University 1-1
1-2
What is Business Analytics? Evolution of Business Analytics Scope of Business Analytics Data for Business Analytics Decision Models Problem Solving and Decision Making Fun with Analytics 1-3
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. 1-4
Business Analytics Applications Management of customer relationships Financial and marketing activities Supply chain management Human resource planning Pricing decisions Sport team game strategies 1-5
Importance of Business Analytics There is a strong relationship of BA with: - profitability of businesses - revenue of businesses - shareholder return BA enhances understanding of data BA is vital for businesses to remain competitive BA enables creation of informative reports 1-6
Operations research Management science Business intelligence Decision support systems Personal computer software 1-7
Descriptive analytics - uses data to understand past and present (Data Mining, Descriptive Stat, Data visualization, Data Query, Standard Reporting) Predictive analytics - analyzes past performance (Data Mining, Predictive Modeling) Prescriptive analytics - uses optimization techniques (Optimization, Decision Analysis, Simulation) 1-8
Financial Analytics HR Analytics Marketing Analytics Health Care Analytics Supply Chain Analytics Analytics for Government and Nonprofits Sports Analytics Web Analytics 1-9
DATA - collected facts and figures DATABASE - collection of computer files containing data INFORMATION - comes from analyzing data 1-10
Examples of using DATA in business: Annual reports Accounting audits Financial profitability analysis Economic trends Marketing research Operations management performance Human resource measurements 1-11
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 1-12
Example 1.2 A Sales Transaction Database File 1-13 Figure 1.1 Entities Records Fields or Attributes
Four Types Data Based on Measurement Scale: Categorical (nominal) data Ordinal data Interval data Ratio data 1-14
Example 1.3 Classifying Data Elements in a Purchasing Database 1-15 Figure 1.2
Example 1.3 (continued) Classifying Data Elements in a Purchasing Database 1-16 Figure 1.2
Categorical (nominal) Data Data placed in categories according to a specified characteristic Categories bear no quantitative relationship to one another Examples: - customers location (America, Europe, Asia) - employee classification (manager, supervisor, associate) 1-17
Ordinal Data Data that is ranked or ordered according to some relationship with one another No fixed units of measurement Examples: - college football rankings - survey responses (poor, average, good, very good, excellent) 1-18
Interval Data Ordinal data but with constant differences between observations No true zero point Ratios are not meaningful Examples: - temperature readings - SAT scores 1-19
Ratio Data Continuous values and have a natural zero point Ratios are meaningful Examples: - monthly sales - delivery times 1-20
Model: An abstraction or representation of a real system, idea, or object Captures the most important features Can be a written or verbal description, a visual display, a mathematical formula, or a spreadsheet representation 1-21
Decision Models Example 1.4 Three Forms of a Model The sales of a new produce, such as a first- generation iPad or 3D television, often follow a common pattern. Sales might grow at an increasing rate over time as positive customer feedback spreads. (See the S-shaped curve on the following slide.) A mathematical model of the S-curve can be identified; for example, S = aebect , where S is sales, t is time, e is the base of natural logarithms, and a, b and c are constants. 1-23 Copyright 息 2013 Pearson Education, Inc. publishing as Prentice Hall 1-22
1-23 Figure 1.3
A decision model is a model used to understand, analyze, or facilitate decision making. Types of model input - data - uncontrollable variables - decision variables (controllable) Types of model output - performance measures - behavioral measures 1-24
Nature of Decision Models 1-25 Figure 1.4
Example 1.5 A Sales-Promotion Model In the grocery industry, managers typically need to know how best to use pricing, coupons and advertising strategies to influence sales. Using Business Analytics, a grocer can develop a model that predicts sales using price, coupons and advertising. 1-26
1-27 Sales = 500 0.05(price) + 30(coupons) +0.08(advertising) + 0.25(price)(advertising)
Descriptive Decision Models Simply tell what is and describe relationships Do not tell managers what to do Influence Diagrams visually show how various model elements relate to one another. Example 1.6 An Influence Diagram for Total Cost 1-28 Figure 1.5
Example 1.7 A Mathematical Model for Total Cost TC = F +VQ TC is Total Cost F is Fixed cost V is Variable unit cost Q is Quantity produced 1-29 Figure 1.6
Example 1.8 A Break-even Decision Model TC(manufacturing) = $50,000 + $125*Q TC(outsourcing) = $175*Q Breakeven Point: Set TC(manufacturing) = TC(outsourcing) 1-30 Figure 1.7
Example 1.9 A Linear Demand Prediction Model As price increases, demand falls. 1-31 Figure 1.8
Example 1.10 A Nonlinear Demand Prediction Model Assumes price elasticity (constant ratio of % change in demand to % change in price) 1-32 Figure 1.9
Predictive Decision Models often incorporate uncertainty to help managers analyze risk. Aim to predict what will happen in the future. Uncertainty is imperfect knowledge of what will happen in the future. Risk is associated with the consequences of what actually happens. 1-33
Prescriptive Decision Models help decision makers identify the best solution. Optimization - finding values of decision variables that minimize (or maximize) something such as cost (or profit). Objective function - the equation that minimizes (or maximizes) the quantity of interest. Constraints - limitations or restrictions. Optimal solution - values of the decision variables at the minimum (or maximum) point. 1-34
Example 1.11 A Pricing Model A firm wishes to determine the best pricing for one of its products in order to maximize revenue. Analysts determined the following model: Sales = -2.9485(price) + 3240.9 Total revenue = (price)(sales) Identify the price that maximizes total revenue, subject to any constraints that might exist. 1-35
Deterministic prescriptive models have inputs that are known with certainty. Stochastic prescriptive models have one or more inputs that are not known with certainty. Algorithms are systematic procedures used to find optimal solutions to decision models. Search algorithms are used for complex problems to find a good solution without guaranteeing an optimal solution. 1-36
BA represents only a portion of the overall problem solving and decision making process. Six steps in the problem solving process 1. Recognizing the problem 2. Defining the problem 3. Structuring the problem 4. Analyzing the problem 5. Interpreting results and making a decision 6. Implementing the solution 1-37
1. Recognizing the Problem Problems exist when there is a gap between what is happening and what we think should be happening. For example, costs are too high compared with competitors. 1-38
2. Defining the Problem Clearly defining the problem is not a trivial task. Complexity increases when the following occur: - large number of courses of action - several competing objectives - external groups are affected - problem owner and problem solver are not the same person - time constraints exist 1-39
3. Structuring the Problem Stating goals and objectives Characterizing the possible decisions Identifying any constraints or restrictions 1-40
4. Analyzing the Problem Identifying and applying appropriate Business Analytics techniques Typically involves experimentation, statistical analysis, or a solution process Much of this course is devoted to learning BA techniques for use in Step 4. 1-41
5. Interpreting Results and Making a Decision Managers interpret the results from the analysis phase. Incorporate subjective judgment as needed. Understand limitations and model assumptions. Make a decision utilizing the above information. 1-42
6. Implementing the Solution Translate the results of the model back to the real world. Make the solution work in the organization by providing adequate training and resources. 1-43
Analytics in Practice Developing Effective Analytical Tools at Hewlett-Packard Will analytics solve the problem? Can they leverage an existing solution? Is a decision model really needed? Guidelines for successful implementation: Use prototyping. Build insight, not black boxes. Remove unneeded complexity. Partner with end users in discovery and design. Develop an analytic champion. 1-44
Algorithm Business analytics Business intelligence Categorical (nominal) data Constraint Continuous metric Data set Database Decision model 1-45 Decision support systems Descriptive statistics Deterministic model Discrete metric Entities Fields (attributes) Influence diagram Interval data Management science (MS)
Measure Measurement Metric Model Objective function Operations research (OR) Optimal solution Optimization Ordinal data 1-46 Predictive analytics Prescriptive analytics Problem solving Ratio data Risk Search Algorithm Stochastic model Uncertainty

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chapter_1_UGBA.pptx

  • 1. Ahmed Imran Kabir Lecturer School of Business and Economics United International University 1-1
  • 2. 1-2
  • 3. What is Business Analytics? Evolution of Business Analytics Scope of Business Analytics Data for Business Analytics Decision Models Problem Solving and Decision Making Fun with Analytics 1-3
  • 4. 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. 1-4
  • 5. Business Analytics Applications Management of customer relationships Financial and marketing activities Supply chain management Human resource planning Pricing decisions Sport team game strategies 1-5
  • 6. Importance of Business Analytics There is a strong relationship of BA with: - profitability of businesses - revenue of businesses - shareholder return BA enhances understanding of data BA is vital for businesses to remain competitive BA enables creation of informative reports 1-6
  • 7. Operations research Management science Business intelligence Decision support systems Personal computer software 1-7
  • 8. Descriptive analytics - uses data to understand past and present (Data Mining, Descriptive Stat, Data visualization, Data Query, Standard Reporting) Predictive analytics - analyzes past performance (Data Mining, Predictive Modeling) Prescriptive analytics - uses optimization techniques (Optimization, Decision Analysis, Simulation) 1-8
  • 9. Financial Analytics HR Analytics Marketing Analytics Health Care Analytics Supply Chain Analytics Analytics for Government and Nonprofits Sports Analytics Web Analytics 1-9
  • 10. DATA - collected facts and figures DATABASE - collection of computer files containing data INFORMATION - comes from analyzing data 1-10
  • 11. Examples of using DATA in business: Annual reports Accounting audits Financial profitability analysis Economic trends Marketing research Operations management performance Human resource measurements 1-11
  • 12. 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 1-12
  • 13. Example 1.2 A Sales Transaction Database File 1-13 Figure 1.1 Entities Records Fields or Attributes
  • 14. Four Types Data Based on Measurement Scale: Categorical (nominal) data Ordinal data Interval data Ratio data 1-14
  • 15. Example 1.3 Classifying Data Elements in a Purchasing Database 1-15 Figure 1.2
  • 16. Example 1.3 (continued) Classifying Data Elements in a Purchasing Database 1-16 Figure 1.2
  • 17. Categorical (nominal) Data Data placed in categories according to a specified characteristic Categories bear no quantitative relationship to one another Examples: - customers location (America, Europe, Asia) - employee classification (manager, supervisor, associate) 1-17
  • 18. Ordinal Data Data that is ranked or ordered according to some relationship with one another No fixed units of measurement Examples: - college football rankings - survey responses (poor, average, good, very good, excellent) 1-18
  • 19. Interval Data Ordinal data but with constant differences between observations No true zero point Ratios are not meaningful Examples: - temperature readings - SAT scores 1-19
  • 20. Ratio Data Continuous values and have a natural zero point Ratios are meaningful Examples: - monthly sales - delivery times 1-20
  • 21. Model: An abstraction or representation of a real system, idea, or object Captures the most important features Can be a written or verbal description, a visual display, a mathematical formula, or a spreadsheet representation 1-21
  • 22. Decision Models Example 1.4 Three Forms of a Model The sales of a new produce, such as a first- generation iPad or 3D television, often follow a common pattern. Sales might grow at an increasing rate over time as positive customer feedback spreads. (See the S-shaped curve on the following slide.) A mathematical model of the S-curve can be identified; for example, S = aebect , where S is sales, t is time, e is the base of natural logarithms, and a, b and c are constants. 1-23 Copyright 息 2013 Pearson Education, Inc. publishing as Prentice Hall 1-22
  • 24. A decision model is a model used to understand, analyze, or facilitate decision making. Types of model input - data - uncontrollable variables - decision variables (controllable) Types of model output - performance measures - behavioral measures 1-24
  • 25. Nature of Decision Models 1-25 Figure 1.4
  • 26. Example 1.5 A Sales-Promotion Model In the grocery industry, managers typically need to know how best to use pricing, coupons and advertising strategies to influence sales. Using Business Analytics, a grocer can develop a model that predicts sales using price, coupons and advertising. 1-26
  • 27. 1-27 Sales = 500 0.05(price) + 30(coupons) +0.08(advertising) + 0.25(price)(advertising)
  • 28. Descriptive Decision Models Simply tell what is and describe relationships Do not tell managers what to do Influence Diagrams visually show how various model elements relate to one another. Example 1.6 An Influence Diagram for Total Cost 1-28 Figure 1.5
  • 29. Example 1.7 A Mathematical Model for Total Cost TC = F +VQ TC is Total Cost F is Fixed cost V is Variable unit cost Q is Quantity produced 1-29 Figure 1.6
  • 30. Example 1.8 A Break-even Decision Model TC(manufacturing) = $50,000 + $125*Q TC(outsourcing) = $175*Q Breakeven Point: Set TC(manufacturing) = TC(outsourcing) 1-30 Figure 1.7
  • 31. Example 1.9 A Linear Demand Prediction Model As price increases, demand falls. 1-31 Figure 1.8
  • 32. Example 1.10 A Nonlinear Demand Prediction Model Assumes price elasticity (constant ratio of % change in demand to % change in price) 1-32 Figure 1.9
  • 33. Predictive Decision Models often incorporate uncertainty to help managers analyze risk. Aim to predict what will happen in the future. Uncertainty is imperfect knowledge of what will happen in the future. Risk is associated with the consequences of what actually happens. 1-33
  • 34. Prescriptive Decision Models help decision makers identify the best solution. Optimization - finding values of decision variables that minimize (or maximize) something such as cost (or profit). Objective function - the equation that minimizes (or maximizes) the quantity of interest. Constraints - limitations or restrictions. Optimal solution - values of the decision variables at the minimum (or maximum) point. 1-34
  • 35. Example 1.11 A Pricing Model A firm wishes to determine the best pricing for one of its products in order to maximize revenue. Analysts determined the following model: Sales = -2.9485(price) + 3240.9 Total revenue = (price)(sales) Identify the price that maximizes total revenue, subject to any constraints that might exist. 1-35
  • 36. Deterministic prescriptive models have inputs that are known with certainty. Stochastic prescriptive models have one or more inputs that are not known with certainty. Algorithms are systematic procedures used to find optimal solutions to decision models. Search algorithms are used for complex problems to find a good solution without guaranteeing an optimal solution. 1-36
  • 37. BA represents only a portion of the overall problem solving and decision making process. Six steps in the problem solving process 1. Recognizing the problem 2. Defining the problem 3. Structuring the problem 4. Analyzing the problem 5. Interpreting results and making a decision 6. Implementing the solution 1-37
  • 38. 1. Recognizing the Problem Problems exist when there is a gap between what is happening and what we think should be happening. For example, costs are too high compared with competitors. 1-38
  • 39. 2. Defining the Problem Clearly defining the problem is not a trivial task. Complexity increases when the following occur: - large number of courses of action - several competing objectives - external groups are affected - problem owner and problem solver are not the same person - time constraints exist 1-39
  • 40. 3. Structuring the Problem Stating goals and objectives Characterizing the possible decisions Identifying any constraints or restrictions 1-40
  • 41. 4. Analyzing the Problem Identifying and applying appropriate Business Analytics techniques Typically involves experimentation, statistical analysis, or a solution process Much of this course is devoted to learning BA techniques for use in Step 4. 1-41
  • 42. 5. Interpreting Results and Making a Decision Managers interpret the results from the analysis phase. Incorporate subjective judgment as needed. Understand limitations and model assumptions. Make a decision utilizing the above information. 1-42
  • 43. 6. Implementing the Solution Translate the results of the model back to the real world. Make the solution work in the organization by providing adequate training and resources. 1-43
  • 44. Analytics in Practice Developing Effective Analytical Tools at Hewlett-Packard Will analytics solve the problem? Can they leverage an existing solution? Is a decision model really needed? Guidelines for successful implementation: Use prototyping. Build insight, not black boxes. Remove unneeded complexity. Partner with end users in discovery and design. Develop an analytic champion. 1-44
  • 45. Algorithm Business analytics Business intelligence Categorical (nominal) data Constraint Continuous metric Data set Database Decision model 1-45 Decision support systems Descriptive statistics Deterministic model Discrete metric Entities Fields (attributes) Influence diagram Interval data Management science (MS)
  • 46. Measure Measurement Metric Model Objective function Operations research (OR) Optimal solution Optimization Ordinal data 1-46 Predictive analytics Prescriptive analytics Problem solving Ratio data Risk Search Algorithm Stochastic model Uncertainty
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