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Chapter 7:Business Intelligence and
Decision Support
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Learning objectives
1. Decision making
2. Business intelligence
3. Data mining
4. Web analytics
5. Dashboards and portals
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Police department
• Data mining to deploy officers
• 47% decline in complaints
• 246% increase in recovered weapons
• $15,000 HR savings during shift
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Business intelligence
• Information for data-driven decision making
• Includes software applications, technologies,
and practices
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Levels of decision making
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Internal External
• Transactional
databases• Data warehouses
• Cloud• External databases• Websites, social
networks, and text
messages
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Sources of BI
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• Online analytical
processing
• Statistics and
modeling
techniques
• Text mining
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Analyzing patterns
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• What-if analysis• Goal seeking and optimization• Forecasting
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Simulating and forecasting
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Examples Challenge
• Amazon.com• ALADDIN• Jeopardy!
• Scammers• CAPTCHA
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Artificial intelligence
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Artificial intelligenceapplications
• Robotics
• Expert systems
• Neural nets
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Web analytics
• Website
• Social media
• E-commerce
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• Analyzing software
• Reaching goals
• Ad effectiveness
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Analyzing traffic
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• Graphical user interface• Key performance indicators
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Dashboards
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• Variety of information on one screen• Enterprise portal
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Portals
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• Aggregate data from multiple sources• Customizable web pages
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Mashups
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Human element
• Although humans choose intelligence, tools,
and interpretation, they have weaknesses
• Must consider human motives in design and
implementation of information systems
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Summary
1. Decision making
2. Business intelligence
3. Data mining
4. Web analytics
5. Dashboards and portals
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Insurance Fraud case
• $68 billion lost in fraud each year
• Fraud detection systems rely on rules and
patterns detected by analytic engines
• Analysts work closely with investigators to
apply human judgment as they create new
rules and follow data leads
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• Command Center—four-panel touchscreen• Real-time aggregated data• Fan dashboard
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New York Jets case
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