Chapter 1: Overview

1. Chapter 1: Overview

2. Chapter 1: Overview

3. Objectives • Define business intelligence and business analytics. • Explain the proliferation of data and how this impacts the need for good analytics. • Identify some of the key challenges of analytics. • Name some applications where analytics are helpful. • Name some applications where analytics are not helpful. • Explain some of the common pitfalls of analytical practice.

4. Three Principles of Real Estate

5. Three Principles of Real Estate “location, location, and location”

6. Three Principles of Business Analytics

7. Three Principles of Business Analytics “business problem/opportunity, business problem/opportunity, and business problem/opportunity”

8. What Is the Business Problem/Opportunity?

9. To Serve or Not to Serve? As an example, Fidelity Investments once considered discontinuing its bill-paying service because this service consistently lost money. Some last-minute analysis saved it, by showing that Fidelity’s most loyal and most profitable customers used the service. Although it lost money, Fidelity made much more money on these customers’ other accounts.

10. To Serve or Not to Serve? After all, customers that trust their financial institution to pay their bills have a very high level of trust in that institution. Cutting such value-added services might inadvertently exacerbate the profitability problem by causing the best customers to look elsewhere for better service.

11. What Is the Business Problem/Opportunity? • Should Fidelity Investments consider discontinuing its bill-paying service because this service consistently lost money? • Should the investment company encourage customers to switch to alternative methods of bill-paying?

12. Business Intelligence “The ability to apprehend the interrelationships of presented facts in such a way as to guide action towards a desired goal.” Hans Peter Luhn (1958) A Business Intelligence System “Concepts and methods to improve business decision making by fact-based support systems.” Howard Dresner (1989) A Brief History of Decision Support Systems

13. Business Analytics “The extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions.” Davenport and Harris (2007) Competing on Analytics: The New Science of Winning

14. Business Intelligence Versus Business Analytics • At least three views exist: • Business analytics is an integral part of business intelligence. • “I think of analytics as a subset of BI based on statistics, prediction and optimization. The great bulk of BI is much more focused on reporting capabilities. Analyticshas become a sexier term to use -- and it certainly is a sexier term than reporting-- so it’s slowly replacing BIin many instances.” • Thomas Davenport (2010) • Analytics at Work: Q&A with Tom Davenport

15. Business Intelligence Versus Business Analytics • Business intelligence and business analytics are synonymous. • “The term business intelligence is used by the information technology community, whereas businessanalyticsis preferred by the business community. The two terms are synonymous and will henceforth be referred to as BI/BA.” SumitSircar (2010) Business Intelligence in the Business Curriculum

16. Business Intelligence Versus Business Analytics • Business intelligence and business analytics have key differences. • Business intelligence describes: “What happened?” • Business analytics describes: • “Why did it happen?” • “What will happen?” • “What is the best that can happen?” SearchBusinessAnalytics.com (2011) Bill Chamberlin (2011) A Primer on Advanced Business Analytics

17. Advanced Business Analytics • View of advanced business analytics for this course: • Advanced business analytics is an all encompassing term that describes the current state-of-the-art in the field of business analytics and/or business intelligence. • BI has a more query or reporting flavor. • BI ≈ MI (Management Information). • Advanced business analytics is forward looking. • Advanced business analytics includes BI, BA, OLAP, query and reporting, dashboards, data warehousing, data mining, prediction, optimization, and so on.

18. Decision Optimization What is the best decision? PredictiveModeling What will happen next? Forecasting What if these trends continue? Competitive Advantage Basic Statistical Analysis Why is this happening? Reporting with Early Warning What actions are needed? Dynamic Reporting Where exactly are the problems? Ad Hoc Reporting How many, how often, where? Basic Reporting What happened? Achieving Success with Business Analytics Advanced Analytics Basic Analytics Reporting Data Intelligence Information Decision Support Decision Guidance

19. Data Deluge hospital patient registries electronic point-of-sale data remote sensing images tax returns stock trades OLTP telephone calls airline reservations credit card charges catalog orders bank transactions social media commentary

20. Three Consequences of the Data Deluge ... Every problem will generate data eventually. Every company will need analytics eventually. Everyone will need analytics eventually.

21. Three Consequences of the Data Deluge ... Every problem will generate data eventually.Proactively defining a data collection protocol will result in more useful information, leading to more useful analytics. Every company will need analytics eventually. Everyone will need analytics eventually.

22. Three Consequences of the Data Deluge ... Every problem will generate data eventually.Proactively defining a data collection protocol will result in more useful information, leading to more useful analytics. Every company will need analytics eventually.Proactively analytical companies will compete more effectively. Everyone will need analytics eventually.

23. Three Consequences of the Data Deluge Every problem will generate data eventually.Proactively defining a data collection protocol will result in more useful information, leading to more useful analytics. Every company will need analytics eventually.Proactively analytical companies will compete more effectively. Everyone will need analytics eventually.Proactively analytical people will be more marketable and more successful in their work.

24. The Business Analytics Challenge Getting anything useful out of tons and tons of data

25. Hope for the Data Deluge + analytical tools hospital patient registries social media commentary electronic point-of-sale data remote sensing images tax returns stock trades OLTP telephone calls airline reservations credit card charges catalog orders bank transactions = actionable knowledge

26. 1.01 Quiz Describe a data system you work with that generates a large amount of information.

27. Management Changes in the Analytical Landscape Historically… Models Analytical Modelers Historically, analytics have typically been handled in the “back office,” and information was shared only by a few individuals.

28. Changes in the Analytical Landscape • Historical Changes • Executive dashboarding – Static reports about business processes • Total quality management (TQM) – Customer focused • Six Sigma – Voice of the process, voice of the customer • Customer relationship management (CRM) – The right offer to the right person at the right time • Forecasting and predicting – 360-degree customer view

29. Changes in the Analytical Landscape • Relational databases • Enterprise resource planning (ERP) systems • Point of sale (POS) systems • Data warehousing • Decision support systems • Reporting and ad hoc queries • Online analytical processing (OLAP) • Performance management mystems • Executive information systems (EIS) • Balanced scorecard • Dashboard • Business intelligence

30. CRM Evolution • Total quality management (TQM) • Product-centric • Quality: Six Sigma • Total customer satisfaction • Mass marketing • One-to-one marketing • Customer relationship • Wallet share of customer • Customer retention • Customer relationship management (CRM) • Customer-centric • Strategy • Process • Technology

31. OPERATIONS CustomerService Retail Logistics Promotions Changes in the Analytical Landscape TARGET Now… Customers Analytical Modelers Proliferation of Models Suppliers Now analytics are being pushed out to the “front office” and are directly impacting company performance. There are clear, tangible benefits that management will track. Data mining is a critical part of business analytics. Employees Stockholders

32. Idiosyncrasies of Business Analytics • 1. The Data • Massive, operational, and opportunistic • 2. The Users and Sponsors • Business decision support • 3. The Methodology • Computer-intensive ad hockery • Multidisciplinary lineage Data mining can be defined as advanced methods for exploringand modeling relationships in large amounts of data. Data mining is an essential component of business analytics.

33. The Data

34. The Data: Disparate Business Units Marketing Invoicing Risk Acquisitions Sales Operations

35. 1.02 Multiple Choice Poll • Organizational data from different business units is generally well-organized and in a form that is ready for analysis. • True • False

36. 1.02 Multiple Choice Poll – Correct Answer • Organizational data from different business units is generally well-organized and in a form that is ready for analysis. • True • False

37. Opportunistic Data • Operational data is typically not collected with data analysis in mind. • Multiple business units produce a silo-based data system. • This makes business analytics different from experimental statistics and especially challenging.

38. The Methodology: What We Learned Not to Do • Prediction is more important than inference. • Metrics are used “because they work,” not based on theory. • p-values are rough guides rather than firm decision cutoffs. • Interpretation of a model might be irrelevant. • The preliminary value of a model is determined by its ability to predict a holdout sample. • The long-term value of a model is determined by its ability to continue to perform well on new data over time. • Models are retired as customer behavior shifts, market trends emerge, and so on.

39. Using Analytics Intelligently • Intelligent use of analytics results in the following: • better understanding of how technological, economic, and marketplace shifts affect business performance • ability to consistently and reliably distinguish between effective and ineffective interventions • efficient use of assets, reduced waste in supplies, and better management of time and resources • risk reduction via measurable outcomes and reproducible findings • early detection of market trends hidden in massive data • continuous improvement in decision making over time

40. Simple Reporting Examples:OLAP, RFM, QC, descriptive statistics, extrapolation Answer questions such as Where are my key indicators now? Where were my key indicators last week? Is the current process behaving like normal? What is likely to happen tomorrow?

41. Proactive Analytical Investigation Examples:inferential statistics, experimentation, empirical validation, forecasting, optimization Answer questions such as What does a change in the market mean for my targets? What do other factors tell me about what I can expect from my target? What is the best combination of factors to give me the most efficient use of resources and maximum profitability? What is the highest price the market will tolerate? What will happen in six months if I do nothing? What if I implement an alternative strategy?

42. 1.03 Multiple Choice Poll • Simple reporting is an important part of business analytics even though it only shows a snapshot of the past. • True • False

43. 1.03 Multiple Choice Poll – Correct Answer • Simple reporting is an important part of business analytics even though it only shows a snapshot of the past. • True • False

44. Data Stalemate Many companies have data that they do not use or that is used by third parties. These third parties might even resell the data and any derived metrics back to the original company! Example: retail grocery POS card

45. Every Little Bit… Taking an analytical approach to only a few key business problems with reliable metrics  tangible benefit. The benefits and savings derived from early analytical successes  managerial support for further analytical efforts. Everyone has data. Analytics can connect data to smart decisions. Proactively analytical companies outpace competition.

46. Areas Where Analytics Are Often Used Which residents in a ZIP code should receive a coupon in the mail for a new store location? New customer acquisition Customer loyalty Cross-sell / up-sell Pricing tolerance Supply optimization Staffing optimization Financial forecasting Product placement Churn Insurance rate setting Fraud detection …

47. Areas Where Analytics Are Often Used What advertising strategy best elicits positive sentiment toward the brand? New customer acquisition Customer loyalty Cross-sell / up-sell Pricing tolerance Supply optimization Staffing optimization Financial forecasting Product placement Churn Insurance rate setting Fraud detection …

48. Areas Where Analytics Are Often Used What is the best next product for this customer? What other product is this customer likely to purchase? New customer acquisition Customer loyalty Cross-sell / up-sell Pricing tolerance Supply optimization Staffing optimization Financial forecasting Product placement Churn Insurance rate setting Fraud detection …

49. Areas Where Analytics Are Often Used What is the highest price that the market will bear without substantial loss of demand? New customer acquisition Customer loyalty Cross-sell / up-sell Pricing tolerance Supply optimization Staffing optimization Financial forecasting Product placement Churn Insurance rate setting Fraud detection …

50. Areas Where Analytics Are Often Used How many 60-inch HDTVs should be in stock? (Too many is expensive; too few is lost revenue.) New customer acquisition Customer loyalty Cross-sell / up-sell Pricing tolerance Supply optimization Staffing optimization Financial forecasting Product placement Churn Insurance rate setting Fraud detection …