Chapter 12

Enhancing Decision Making

VIDEO CASES
Video Case 1: FreshDirect Uses Business Intelligence to Manage Its Online Grocery
Video Case 2: Business Intelligence Helps the Cincinnati Zoo
Instructional Video 1: FreshDirect’s Secret Sauce: Customer Data From the
Website
Instructional Video 2: A Demonstration of Oracle’s Mobile Business Intelligence
App
 Learning Objectives

• Describe the different types of decisions and how the

decision-making process works.
• Explain how information systems support the activities of
managers and management decision making.
• Explain how business intelligence and business analytics
support decision making.
• Explain how different decision-making constituencies in an
organization use business intelligence.
• Describe the role of information systems in helping people
working in a group make decisions more efficiently.
 Moneyball: Data-Driven Baseball

• Problem: Limited resources and outdated

metrics
• Solutions: Use improved statistical analysis to
identify affordable, overlooked players
• Demonstrates the use of business intelligence
to optimize performance and keep costs low
• Illustrates how information systems can
provide advantages for a limited time
 Decision Making and Information Systems

• Business value of improved decision making

• Improving hundreds of thousands of “small” decisions
adds up to large annual value for the business
• Types of decisions:
• Unstructured: Decision maker must provide judgment,
evaluation, and insight to solve problem
• Structured: Repetitive and routine; involve definite
procedure for handling so they do not have to be
treated each time as new
• Semistructured: Only part of problem has clear-cut
answer provided by accepted procedure
 Decision Making and Information Systems

• Senior managers:
– Make many unstructured decisions
– For example: Should we enter a new market?

• Middle managers:
– Make more structured decisions but these may include unstructured
components
– For example: Why is order fulfillment report showing decline in
Minneapolis?

• Operational managers, rank and file

employees
– Make more structured decisions
– For example: Does customer meet criteria for credit?
 INFORMATION REQUIREMENTS OF KEY DECISION-MAKING GROUPS IN A FIRM

FIGURE 12-1 Senior managers, middle managers, operational managers, and employees have different types of decisions
and information requirements.
 Decision Making and Information Systems

• The four stages of the decision-making process

1. Intelligence
• Discovering, identifying, and understanding the problems
occurring in the organization
2. Design
• Identifying and exploring solutions to the problem
3. Choice
• Choosing among solution alternatives
4. Implementation
• Making chosen alternative work and continuing to monitor
how well solution is working
 STAGES IN DECISION MAKING

The decision-making process is

broken down into four stages.

FIGURE 12-2
 Decision Making and Information Systems

• Information systems can only assist in some of

the roles played by managers
• Classical model of management: five functions
–Planning, organizing, coordinating, deciding, and
controlling
• More contemporary behavioral models
–Actual behavior of managers appears to be less systematic,
more informal, less reflective, more reactive, and less well
organized than in classical model
 Decision Making and Information Systems

• Mintzberg’s 10 managerial roles

• Interpersonal roles
1. Figurehead
2. Leader
3. Liaison
• Informational roles
4. Nerve center
5. Disseminator
6. Spokesperson
• Decisional roles
7. Entrepreneur
8. Disturbance handler
9. Resource allocator
10. Negotiator
 Decision Making and Information Systems

• Three main reasons why investments in information

technology do not always produce positive results
1. Information quality
• High-quality decisions require high-quality information
2. Management filters
• Managers have selective attention and have variety of biases
that reject information that does not conform to prior
conceptions
3. Organizational inertia and politics
• Strong forces within organizations resist making decisions
calling for major change
 Decision Making and Information Systems

• High-velocity automated decision making

• Made possible through computer algorithms precisely
defining steps for a highly structured decision
• Humans taken out of decision
• For example: High-speed computer trading programs
• Trades executed in 30 milliseconds
• Responsible for “Flash Crash” of 2010
• Require safeguards to ensure proper operation and
regulation
 Business Intelligence in the Enterprise

• Business intelligence
• Infrastructure for collecting, storing, analyzing data
produced by business
• Databases, data warehouses, data marts
• Business analytics
• Tools and techniques for analyzing data
• OLAP, statistics, models, data mining
• Business intelligence vendors
• Create business intelligence and analytics purchased
by firms
 Analytics Help the Cincinnati Zoo Know Its Customers

• What management, organization, and technology factors were

behind the Cincinnati Zoo losing opportunities to increase revenue?
• Why was replacing legacy point-of-sale systems and implementing a
data warehouse essential to an information system solution?
• How did the Cincinnati Zoo benefit from business intelligence? How
did it enhance operational performance and decision making? What
role was played by predictive analytics?
• Visit the IBM Cognos Web site and describe the business intelligence
tools that would be the most useful for the Cincinnati Zoo.
 BUSINESS INTELLIGENCE AND ANALYTICS FOR DECISION SUPPORT

Business intelligence
and analytics
requires a strong
database
foundation, a set of
analytic tools, and
an involved
management team
that can ask
intelligent questions
and analyze data.

FIGURE 12-3
 Business Intelligence in the Enterprise

• Six elements in the business intelligence

environment
1. Data from the business environment
2. Business intelligence infrastructure
3. Business analytics toolset
4. Managerial users and methods
5. Delivery platform—MIS, DSS, ESS
6. User interface
 Business Intelligence in the Enterprise

• Business intelligence and analytics capabilities

–Goal is to deliver accurate real-time information to
decision makers
–Main functionalities of BI systems
1. Production reports
2. Parameterized reports
3. Dashboards/scorecards
4. Ad hoc query/search/report creation
5. Drill down
6. Forecasts, scenarios, models
 Business Intelligence in the Enterprise

• Business intelligence users

–80% are casual users relying on production reports
–Senior executives
• Use monitoring functionalities
–Middle managers and analysts
• Ad-hoc analysis
–Operational employees
• Prepackaged reports
• For example: sales forecasts, customer satisfaction,
loyalty and attrition, supply chain backlog, employee
productivity
 BUSINESS INTELLIGENCE USERS

FIGURE 12-4 Casual users are consumers of BI output, while intense power users are the producers of reports, new
analyses, models, and forecasts.
 Business Intelligence in the Enterprise

• Production reports
• Most widely used output of BI suites
• Common predefined, prepackaged reports
• Sales: Forecast sales; sales team performance
• Service/call center: Customer satisfaction; service cost
• Marketing: Campaign effectiveness; loyalty and attrition
• Procurement and support: Supplier performance
• Supply chain: Backlog; fulfillment status
• Financials: General ledger; cash flow
• Human resources: Employee productivity; compensation
 Business Intelligence in the Enterprise

• Predictive analytics
• Use variety of data, techniques to predict future
trends and behavior patterns
• Statistical analysis
• Data mining
• Historical data
• Assumptions
• Incorporated into numerous BI applications for sales,
marketing, finance, fraud detection, health care
• Credit scoring
• Predicting responses to direct marketing campaigns
 Business Intelligence in the Enterprise

• Big data analytics

• Big data: Massive datasets collected from social
media, online and in-store customer data, and so on
• Help create real-time, personalized shopping
experiences for major online retailers
• Hunch.com, used by eBay
• Customized recommendations
• Database includes purchase data, social networks
• Taste graphs map users with product affinities
 Business Intelligence in the Enterprise

• Additional BI applications
–Data visualization and visual analytics tools
• Help users see patterns and relationships that would be
difficult to see in text lists
• Rich graphs, charts
• Dashboards
• Maps
–Geographic information systems (GIS)
• Ties location-related data to maps
• Example: For helping local governments calculate
response times to disasters
 Business Intelligence in the Enterprise

• Two main management strategies for

developing BI and BA capabilities
1. One-stop integrated solution
–Hardware firms sell software that run optimally on their
hardware
–Makes firm dependent on single vendor—switching costs
2. Multiple best-of-breed solution
–Greater flexibility and independence
–Potential difficulties in integration
–Must deal with multiple vendors
 Business Intelligence Constituencies

• Operational and middle managers

• Use MIS (running data from TPS) for:
• Routine production reports
• Exception reports
• “Super user” and business analysts
• Use DSS for:
• More sophisticated analysis and custom reports
• Semistructured decisions
 Business Intelligence Constituencies

•Decision support systems

• Use mathematical or analytical models
• Allow varied types of analysis
• “What-if” analysis
• Sensitivity analysis
• Backward sensitivity analysis
• Multidimensional analysis / OLAP
• For example: pivot tables
 SENSITIVITY ANALYSIS

FIGURE 12-5 This table displays the results of a sensitivity analysis of the effect of changing the sales price of a necktie and
the cost per unit on the product’s break-even point. It answers the question, “What happens to the break-
even point if the sales price and the cost to make each unit increase or decrease?”
 A PIVOT TABLE THAT EXAMINES CUSTOMER REGIONAL DISTRIBUTION AND
ADVERTISING SOURCE

In this pivot table, we

are able to examine
where an online
training company’s
customers come from
in terms of region and
advertising source.

FIGURE 12-6
 Business Intelligence Constituencies

• ESS: decision support for senior management

–Help executives focus on important performance
information
–Balanced scorecard method:
• Measures outcomes on four dimensions:
1. Financial
2. Business process
3. Customer
4. Learning and growth
• Key performance indicators (KPIs) measure each
dimension
 THE BALANCED SCORECARD FRAMEWORK

FIGURE 12-7

In the balanced scorecard framework, the firm’s

strategic objectives are operationalized along four
dimensions: financial, business process, customer,
and learning and growth. Each dimension is
measured using several KPIs.
 Business Intelligence Constituencies

• Decision support for senior management (cont.)

• Business performance management (BPM)
• Translates firm’s strategies (e.g., differentiation, low-cost
producer, scope of operation) into operational targets
• KPIs developed to measure progress toward targets
• Data for ESS
• Internal data from enterprise applications
• External data such as financial market databases
• Drill-down capabilities
 Colgate-Palmolive Keeps Managers Smiling with Executive Dashboards

• Describe the different types of business intelligence users at

Colgate-Palmolive.
• Describe the “people” issues that were affecting Colgate’s
ability to use business intelligence.
• What management, organization, and technology factors had
to be addressed in providing business intelligence capabilities
for each type of user?
• What kind of decisions does Colgate’s new business intelligence
capability support? Give three examples. What is their
potential business impact?
 Business Intelligence Constituencies

• Group decision support systems (GDSS)

–Interactive system to facilitate solution of unstructured
problems by group
–Specialized hardware and software; typically used in
conference rooms
• Overhead projectors, display screens
• Software to collect, rank, edit participant ideas and responses
• May require facilitator and staff
–Enables increasing meeting size and increasing productivity
–Promotes collaborative atmosphere, anonymity
–Uses structured methods to organize and evaluate ideas