Chapter 12: Enhancing Decision Making Flashcards
business intelligence & analytics, DM constituencies
1
Q
Business value of improved DM
A
Improving hundreds of thousands of “small” decisions adds up to large annual value for the
business
2
Q
Types of decisions
A
- 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
3
Q
Decisions on different management levels
A
- Senior managers: many unstructured decisions
–> Should we enter a new market? - Middle managers: more structured decisions but these may include unstructured components
–> Why is order fulfillment report showing decline in Minneapolis? - Operational managers, rank & file employees: more structured decisions
–> Does customer meet criteria for credit?
4
Q
Four stages of the DM process
A
- Intelligence: Discovering, identifying & understanding the problems occurring in the organization
- Design: Identifying & exploring solutions to the problem
- Choice: Choosing among solution alternatives
- Implementation: Making the chosen alternative work & continuing to monitor how well solution is working
5
Q
How are DM & IS interrelated?
A
- IS can only assist in some of the roles played by managers
- Classical model of management: Planning, organizing, coordinating, deciding & controlling
- More contemporary behavioral models: Actual behavior of managers appears to be less systematic, more informal, less reflective, more
reactive & less well organized than in classical model
6
Q
Mintzberg’s 10 managerial roles
A
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
7
Q
What prevents investments in IT from having a positive effect?
A
- Poor information quality
- Management filters: selective attention & variety of biases that reject information that does not
conform to prior conceptions - Organizational inertia & politics: Strong forces within organizations resist making decisions calling for major change
8
Q
What is high-velocity automated DM?
A
- Made possible through computer algorithms precisely defining steps for a highly structured decision (humans taken out of decision)
- Safeguards required
- example: High-speed computer trading programs that execut trades in 30 ms (“Flash Crash” 2010)
9
Q
How are business intelligence & business analytics connected?
A
- Business intelligence & analytics require a strong database foundation, a set of analytic tools & an involved management team that can ask intelligent questions & analyze data
- BI: Infrastructure for collecting, storing, analyzing data produced by business (databases, data warehouses, data marts)
- BA: Tools & techniques for analyzing data (OLAP, statistics, models, data mining)
- Business intelligence vendors BI & BA purchased by firms
10
Q
Six elements in the business intelligence environment
A
- Data from the business environment
- Business intelligence infrastructure
- Business analytics toolset
- Managerial users & methods
- Delivery platform: MIS, DSS, ESS
- User interface (sata visualization tools)
11
Q
Main analytic functionalities of BI systems & goal
A
- Production reports
- Parameterized reports
- Dashboards/scorecards
- Ad hoc query/search/report creation
- Drilldown
- Forecasts, scenarios, models
–> Goal: deliver accurate real-time information to decision makers
–> most widely used output: production reports (predefined & prepackaged)
12
Q
Types of business analytics
A
- predictive analytics
- big data analytics
- operational intelligence & analytics
- location analytics
13
Q
Define predictive analytics
A
- use a variety of data & techniques to predict future trends & 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)
14
Q
Define big data analytics
A
- Big data: Massive datasets collected from social media, online and in-store customer data etc.
- Help create real-time, personalized shopping experiences for major online retailers
- Smart cities: public records, sensors (location data from smartphones), ability to evaluate effect of one service change on system
15
Q
Define operational intelligence & analytics
A
- operational intelligence: Business activity monitoring
- collection & use of data generated by sensors
- IoT: creating huge streams of data from web activities, sensors & other monitoring devices
- software for operational intelligence & analytics enable companies to analyze their big data
