1. Chapter 1: Overview 1.1 Overview of Business Analytics
1.2 Software Used in This Course
1.3 Recommended Reading

2. Chapter 1: Overview 1.1 Overview of Business Analytics
1.2 Software Used in This Course
1.3 Recommended Reading

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. Analytics has become a sexier term to use -- and it certainly is a sexier term than reporting -- so it’s slowly replacing BI in 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 business analytics is preferred by the business community. The two terms are synonymous and will henceforth be referred to as BI/BA.”
Sumit Sircar (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. Achieving Success with Business Analytics
Competitive Advantage
Basic Reporting What happened?
Ad Hoc Reporting How many, how often, where?
Dynamic Reporting Where exactly are the problems?
Reporting with Early Warning What actions are needed?
Basic Statistical Analysis Why is this happening?
Forecasting What if these trends continue?
Predictive Modeling What will happen next?
Decision Optimization What is the best decision?
Advanced Analytics
Basic
Analytics
Reporting
Data
Information
Intelligence
Decision Support
Decision Guidance 18

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
There is undoubtedly something useful in there, but it isn’t easy to get at it.

25. Hope for the Data Deluge
+ analytical tools
hospital patient registries
electronic point-of-sale data
social media commentary
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.
Type answer here

27. Changes in the Analytical Landscape
Historically…
Models
Analytical Modelers
Management
[aj] Historically, analytics have typically been handled in the “back office” and served the basic need to understand what has happened in the business to get where we are today. Over time, the power of analytical models has grown to where they are much more integral to organizational dynamics. Forecasting, predictive models, and advanced modeling are emerging as key operational components. Organizations are beginning to embrace the value that analytics can drive in he business to be more efficient and more effective.
[Next Slide]
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. Changes in the Analytical Landscape
Customer Service
Retail
Logistics
Promotions
OPERATIONS
Now…
TARGET
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.
[aj] Now, analytics are pushed out to the “front office”, and they begin to have a direct impact on company performance. Analytics will drive new behaviour from customers, suppliers, and others that will have an impact on revenue and costs of the organization. Now, there are tangible monetary benefits that management will associate to analytics. Success will breed the desire for more models. The number and usage of models will proliferate.
[Next Slide]
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 exploring and modeling relationships in large amounts of data. Data mining is an essential component of business analytics.

33. The Data Experimental Opportunistic Purpose Research Operational Value
Scientific
Commercial
Generation
Actively controlled
Passively observed
Size
Small
Massive
Hygiene
Clean
Dirty
State
Static
Dynamic

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

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
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
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
Adapted from Davenport, Harris, and Morison, 2010 p 3

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?
Based very loosely on Davenport, et al pp 6-7

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
True

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
True

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
Grocery chain anecdote Davenport, et al 2010 pp 8-9

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
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 …
Which residents in a ZIP code should receive a coupon in the mail for a new store location?
Davenport, et al 2010 pp 9-10

47. Areas Where Analytics Are Often Used
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 …
What advertising strategy best elicits positive sentiment toward the brand?
Davenport, et al 2010 pp 9-10

48. Areas Where Analytics Are Often Used
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 …
What is the best next product for this customer?
What other product is this customer likely to purchase?
Davenport, et al 2010 pp 9-10

49. Areas Where Analytics Are Often Used
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 …
What is the highest price that the market will bear without substantial loss of demand?
Davenport, et al 2010 pp 9-10

50. Areas Where Analytics Are Often Used
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 …
How many 60-inch HDTVs should be in stock? (Too many is expensive; too few is lost revenue.)
Davenport, et al 2010 pp 9-10

51. Areas Where Analytics Are Often Used
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 …
What are the best times and best days to have technical experts on the showroom floor?
Davenport, et al 2010 pp 9-10

52. Areas Where Analytics Are Often Used
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 …
What weekly revenue increase can be expected after the Mother’s Day sale?
Davenport, et al 2010 pp 9-10

53. Areas Where Analytics Are Often Used
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 …
Will oatmeal sell better near granola bars or near baby food?
Davenport, et al 2010 pp 9-10

54. Areas Where Analytics Are Often Used
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 …
Davenport, et al 2010 pp 9-10
Which customers are most likely to switch to a different wireless provider in the next six months?

55. Areas Where Analytics Are Often Used
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 …
Davenport, et al 2010 pp 9-10
How likely is it that this individual will have a claim?

56. Areas Where Analytics Are Often Used
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 …
Davenport, et al 2010 pp 9-10
How can I identify a fraudulent purchase?

57. When Analytics Are Not Helpful
Snap decisions required Novel approach (no previous data possible) Most salient factors are rare (making decisions to work around unlikely obstacles or miracles) Expert analysis suggests a particular path Metrics are inappropriate Naïve implementation of analytics Confirming what you already know
Deciding when to run from danger
Cite Gladwell, the phony statue example
And Davenport, et al pp 10-12

58. When Analytics Are Not Helpful
Snap decisions required Novel approach (no previous data possible) Most salient factors are rare (making decisions to work around unlikely obstacles or miracles) Expert analysis suggests a particular path Metrics are inappropriate Naïve implementation of analytics Confirming what you already know
Predicting the adoption of a new technology
Cite Gladwell, the phony statue example
And Davenport, et al pp 10-12

59. When Analytics Are Not Helpful
Snap decisions required Novel approach (no previous data possible) Most salient factors are rare (making decisions to work around unlikely obstacles or miracles) Expert analysis suggests a particular path Metrics are inappropriate Naïve implementation of analytics Confirming what you already know
Planning contingencies for employees winning the lottery
Cite Gladwell, the phony statue example
And Davenport, et al pp 10-12

60. When Analytics Are Not Helpful
Snap decisions required Novel approach (no previous data possible) Most salient factors are rare (making decisions to work around unlikely obstacles or miracles) Expert analysis suggests a particular path Metrics are inappropriate Naïve implementation of analytics Confirming what you already know
The seasoned art critic can recognize a fake
Cite Gladwell, the phony statue example
And Davenport, et al pp 10-12

61. When Analytics Are Not Helpful
Snap decisions required Novel approach (no previous data possible) Most salient factors are rare (making decisions to work around unlikely obstacles or miracles) Expert analysis suggests a particular path Metrics are inappropriate Naïve implementation of analytics Confirming what you already know
Predicting athletes’ salaries or quantifying love
Cite Gladwell, the phony statue example
And Davenport, et al pp 10-12

62. When Analytics Are Not Helpful
Snap decisions required Novel approach (no previous data possible) Most salient factors are rare (making decisions to work around unlikely obstacles or miracles) Expert analysis suggests a particular path Metrics are inappropriate Naïve implementation of analytics Confirming what you already know
Cite Gladwell, the phony statue example
And Davenport, et al pp 10-12
Only looking at one variable at a time

63. When Analytics Are Not Helpful
Snap-decisions required Novel approach (no previous data possible) Most salient factors are rare (making decisions to work around unlikely obstacles or miracles) Expert analysis suggests a particular path Metrics are inappropriate Naïve implementation of analytics Confirming what you already know
Cite Gladwell, the phony statue example
And Davenport, et al pp 10-12
Ignoring variables that might be important

64. Naïve Analytics
Many companies that implement analytical programs such as Six Sigma demonstrate tremendous success.
However, it is important to use analytics in a meaningful way.
For example:
It might not be possible to establish a Six Sigma process with low production volume.
Producer-centric metrics might not give useful information about customer satisfaction, and the Six Sigma process might still fail to meet customer specifications.
Simplistic reporting on massive data might hide complex patterns and is generally unsuccessful.

65. The Fallacy of Univariate Thinking
What is the most important cause of churn?
Prob(churn)
Daytime
Usage
International
Usage

66. Expectations Leading the Analysis
Even sophisticated analytics are not immune to personal bias such as the following:
selectively fitting models with variables because they place someone’s opinion or agenda in a positive light
ignoring information that might disprove a hypothesis.
Personal bias in model fitting, whether intentional or otherwise, can diminish the usefulness of your analytical efforts.

67. Trustworthy Analytics
Let the data guide your conclusions.
Ask the following questions:
Are my assumptions about the causes of my data patterns warranted?
Should I try something different?
Assign a cynic to the analytical team whose purpose is to question the assumptions.
What would my critic say is the flaw with my analysis?
Investigate the data in such a way that a critic’s concerns can be ruled out.

68. 1.04 Poll
It is important for team members on an analytical team to try to identify the potential problems with an analytical approach.  True  False
Type answer here

69. 1.04 Poll – Correct Answer
It is important for team members on an analytical team to try to identify the potential problems with an analytical approach.  True  False
Type answer here

70. Chapter 1: Overview 1.2 Software Used in This Course
1.1 Overview of Business Analytics
1.2 Software Used in This Course
1.3 Recommended Reading

71. Objectives
Identify and describe several software tools for business analytics, including
SAS Enterprise Guide
SAS Enterprise Miner
SAS Forecast Studio.
Describe several of the key features of these programs.

72. Tasks for Business Analytics
Identify and access data sources
Combine data sources
Transform variables
Explore and describe data
Visualize patterns in the data
Analyze and model data
Validate models
Score
Update models

73. Introduction to the SAS System
The SAS System is driven by SAS programs.
SAS programs consist of commands in the form of DATA steps and PROC (or procedure) steps.
SAS features point-and-click interfaces that write programs and perform additional functions automatically. Three interfaces featured in this course are
SAS Enterprise Guide
SAS Enterprise Miner
SAS Forecast Studio.

74. The Tools of Business Analytics
...

75. The Tools of Business Analytics
...

76. The Tools of Business Analytics

77. Introduction to SAS Enterprise Guide
SAS Enterprise Guide provides a point-and-click interface for managing data and generating reports.

78. Introduction to SAS Enterprise Guide
Behind the scenes, SAS Enterprise Guide generates SAS programs that are submitted to SAS. Results are returned to SAS Enterprise Guide.

79. SAS Enterprise Guide Programming Interface
SAS Enterprise Guide also includes a full programming interface that can be used to write, edit, and submit SAS programs.

80. Introduction to SAS Enterprise Miner
SAS Enterprise Miner streamlines the data mining process to create highly accurate predictive and descriptive models based on vast amounts of data gathered from across an organization.

81. SAS Enterprise Miner Analytic Strengths
Pattern Discovery
Predictive Modeling

82. SAS Software for Forecasting
Base SAS
DATA step programming
SAS/STAT
ordinary least squares regression
regression with correlated errors
SAS/ETS
univariate and multivariate time series forecasting
dynamic regression with correlated errors
econometric modeling
spectral analysis
time series cross-sectional modeling
Forecast Server
high performance large-scale forecasting
...

83. Environments for Forecasting Using SAS
The SAS windowing environment
The Time Series Forecasting System
SAS Enterprise Guide
SAS Forecast Studio
...

84. Introduction to SAS Forecast Studio
SAS Forecast Server can automatically generate large quantities of statistically based forecasts without the need for human intervention, unless so desired. It operates in either batch mode or interactively through the SASForecast Studio GUI.

85. The Forecasting Workflow
Repair series
Gather results
Reconcile forecasts
Assess observed results
Apply forecast overrides
Define forecasting objectives
Validate series and hierarchy
Disseminate observed results
Refine forecasting objectives
Accommodate data updates
Apply analysis and generate forecasts
Accumulate series, create the hierarchy
Select series, specify the data hierarchy

86. Large-Scale Forecasting Scenario
80% can be forecast automatically.
10% require extra effort.
10% cannot be forecast accurately.
Time Series Data

87. Idea Exchange
Identify several business problems that you could address with analytics. You might think about case studies that you are using in other classes, projects that you work on at your job, examples from the media, or others.
Describe the goal, whether the variables can be measured, how the data could be obtained, and what types of specific questions you would like to address with analytics.
For now, entertain several examples. Later in the course, you might consider how to collect (or download) the relevant data, perform a data analysis, and report your findings to others in an organization.
What type of data do you think you would need? How could you obtain it?
What software tools do you think you are likely to use?

88. Chapter 1: Overview 1.3 Recommended Reading
1.1 Overview of Business Analytics
1.2 Software Used in This Course
1.3 Recommended Reading

89. Recommended Reading
Davenport, Thomas H., Jeanne G. Harris, and Robert Morison Analytics at Work: Smarter Decisions, Better Results. Boston: Harvard Business Press.
Chapter 1, pp. 1-17; part 2 pp (overview and data); pp optional
This week’s assigned readings from Davenport, et al. introduce the concepts of applying analytics in the workplace, moving an organization up the ladder of analytical competence, and into a leadership position relative to competing organizations.

90. Recommended Reading
Davenport, Thomas H. and Jeanne G. Harris. “The Dark Side of Customer Analytics.” Harvard Business Review. May Plus commentary from George L. Jones, Katherine N. Lemon, David Norton, and Michael B. McCallister.

91. Recommended Reading
Haque, Umair. “The Case for Being Disruptively Good.” Harvard Business Review blogs. April 12, 2010.
This blog and the article by Davenport and Harris present two perspectives on the data deluge; namely, an organization can choose to make use of a data deluge for purposes of questionable ethical value, or it can choose to capitalize on the ready availability of information to create an image as an leader in business ethics and humanitarian, environmental, or economic best-practices. Either perspective concludes that standing still puts an organization at a competitive disadvantage.