Transcript Ch.11 Business Intelligence and Decision Support

Lecture 9
Ch.11
Business Intelligence and
Decision Support
A century ago, John Wanamaker, FATHER OF MODERN
ADVERTISING, said
Half the money I spend on advertising is wasted;
the trouble is I don't know which half.
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BI, data mining, and DSSs are used to
minimize uncertainty (the reverse of
intelligence) and to be able to make
faster, smarter decisions—often in realtime.
When managers and workers have the
intelligence they need to respond correctly
and quickly to opportunities, threats, &
mistakes, they and their companies
significantly outperform those that don’t.
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11.1 Business Intelligence (BI) for Profits &
Nonprofits
BI refers to a collection of ISs and technologies that support
managerial decision making or operational control by
providing information on internal and external operations.
– It’s tough to fully understand BI because BI apps are not
stand-alone systems nor do they support a specific
objective, as do supply chain management (SCM) or
customer relationship management (CRM).
– Visualization tools including dashboards &
mashups are the user-interfaces that help
people understand the numbers.
Visit iDashboards.com to preview live dashboards by industry or
function
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BI Cases
• WildTrack (wildtrack.org/), monitors and verifies
endangered rhinos in Africa
• United Way (unitedway.org/), monitors
fundraising campaigns and generates reliable
reports
Figure 11.3 Endangered
black rhinoceroses are
tracked using BI
• Jamba Juice, (jambajuice.com/), monitors
customers’ preferences and captures data for
fast, reliable P&L and financial reporting
Figure 11.4 Jamba Juice store managers
rely on BI for marketing and accounting
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Predictive Analytics
Branch of data mining that focuses on forecasting trends (e.g.,
regression analysis) and estimating probabilities of future
events
Figure 11.5 Top 5 business
pressure driving the
adoption of predictive
analytics. (Data from
Aberdeen Group)
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Event-Driven Alerts
Event-driven alerts are real-time alerts or warnings
that are broadcast when a predefined event occurs.
Figure 11.6 (in textbook) shows the processing that
takes place when an unusually large deposit occurs
For a credit card company, a customer's sudden payoff
of the entire balance might trigger a business rule that
creates an alert because the payoff could be a signal
that the customer is planning to cancel the card.
– E.g., There could be an intervention, such as a low
interest rate offer, to reduce losing the customer.
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11.2 BI Architecture, Analytics, Reporting, and
Data Visualization
Purpose of BI is to gain insight from data for the purpose of
taking action
IT at Work 11.2
BI Saves Lives of Wounded Soldiers from Battlefield to
Treatment
When soldiers are wounded in battle, the military needs to be
able to quickly diagnose their condition and provide medical
transport. TRAC2ES tracks & coordinates patient information
throughout the U.S. military’s worldwide network of
healthcare facilities.
Figure 11.9. Overview of the BI
architecture of TRAC2ES to
calculate the best transport
method & the most appropriate
medical center for treatment
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11.3 Data, Text, and Web Mining
• Data mining is a process that uses statistical, mathematical,
artificial intelligence, and machine-learning techniques to
extract and identify useful information from large databases
• Text from documents, e-communications, and e-commerce
activities can be mined. Text needs to be codified, typically with
XML (eXtensible Markup Language), and extracted so that
predictive data mining tools can be used to generate real value
• Web mining, or Web-content mining, is used to understand
customer behavior, evaluate a Web site's effectiveness, and
quantify the success of a marketing campaign
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Data Mining Apps
•
Retailing and sales. Predicting sales, determining correct inventory levels
and distribution schedules among outlets, and loss prevention.
•
Banking. Forecasting levels of bad loans and fraudulent credit card use a
nd which kinds of customers will best respond to new loan offers.
•
Manufacturing and production. Predicting machinery failures; finding ke
y factors that control optimization of manufacturing capacity.
•
Healthcare. Developing better insights on symptoms and their causes an
d how to provide proper treatments.
•
Broadcasting. Predicting which programs are best to air during prime ti
me
•
Marketing. Classifying customer demographics that can be used to predi
ct which customers will respond to a mailing or Internet banners or buy
a particular product
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Types of decisions
Structured decisions are routine and repetitive
problems for which standard solutions exist.
Unstructured decisions involve a lot of uncertainty for
which there are no definitive or clear-cut solutions.
Semistructured decisions fall between the polar
positions.
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