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Chapter 6: Enhancing Business Intelligence Using Information Systems

A key to effective management is

high-quality and timely information to support decision making

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Chapter 6 Learning Objectives

Business Intelligence

Describe the concept of business intelligence and how databases serve as a foundation for gaining business intelligence.

Business Intelligence Components

Explain the three components of business intelligence: information and knowledge discovery, business analytics, and information visualization.

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Business Intelligence

Describe the concept of business intelligence and how databases serve as a foundation for gaining business intelligence.

Business Intelligence Components

Explain the three components of business intelligence: information and knowledge discovery, business analytics, and information visualization.

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Why Organizations Need Business Intelligence: Threats and Opportunities

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Why Organizations Need Business Intelligence: Understanding Big Data

Businesses are dealing with the challenge of “Big Data”

High Volume

Unprecedented amounts of data

High Variety

Structured data
Unstructured data

High Velocity

Rapid processing to maximize value

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Why Organizations Need Business Intelligence: Continuous Planning

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Databases: Providing Inputs into Business Intelligence Applications

Data and knowledge are an organization’s most valuable resource
Enabling interactive Web sites using databases:

E-commerce makes extensive use of databases
Product catalog data are stored in databases and available to users
Customer billing and shipping
E-commerce applications process millions of transactions per day

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Databases: Foundation Concepts

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Databases: Tables and Records

This sample data table for the entity Student includes 7 attributes and 10 records.

Source: Microsoft Access

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Databases: Advantages

Advantage	Description
Program-data independence	Programs and data are separated by the DBMS
Minimal data redundancy	There is a single copy of the data
Improved data consistency	Eliminating redundancy improves consistency
Improved data sharing	The DBMS allows sharing between programs
Application development	Data standards simplify program development
Standards enforcement	Centralized data management standardizes rules
Increased security	Simplifies enforcement of access restrictions
Improved data quality	Centralized control, minimized redundancy, and improved data consistency enhance data quality
Improved data accessibility	Centralized, standardized access
Reduced program maintenance	Changes in data replicate seamlessly

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Databases: Costs and Risks

Cost or Risk	Description
Need for new, specialized personnel	Conversion to the database approach may require hiring additional personnel.
Installation and management cost and complexity	The database approach has higher up-front costs and complexity in order to gain long-term benefits.
Conversion costs	Extensive costs are common when converting existing systems, often referred to as legacy systems, to the database approach.
Need for explicit backup and recovery	A shared corporate data resource must be accurate and available at all times.
Organizational conflict	Ownership—creation, naming, modification, and deletion—of data can cause organizational conflict.

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Databases: Effective Management

The Data Model

What data will be captured
How the data will be represented

The Data Dictionary

Document of database metadata

Attribute name
Type of data
Valid values
Business rules

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Databases: Entering and Querying Data

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Databases: �Query via Graphical User Interface

Source: Courtesy of Microsoft Corporation

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Databases: �Operational Systems and BI

Online Transaction Processing (OLTP)

Systems that interact with customers and run a business in real time

Master Data Management

Data deemed most important in the operation of a business
Shared among multiple organizational units

Data Warehouses

Integrate data from multiple databases and other data sources
Contain historic as well as current data

Data Marts

Mini data warehouse, limited in scope to organizational unit

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Operational vs. Informational Systems

Characteristic	Operational System	Informational System
Primary purpose	Run the business on a current basis	Support managerial decision making
Type of data	Current representation of state of the business	Historical or point-in-time (snapshot)
Primary users	Online customers, clerks, salespersons, administrators	Managers, business analysts, and customers (checking status and history)
Scope of usage	Narrow and simple updates and queries	Broad and complex queries and analyses
Design goal	Performance	Ease of access and use

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Extract, Transform, and Load

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Business Intelligence Components

Business Intelligence
Describe the concept of business intelligence and how databases serve as a foundation for gaining business intelligence.

Business Intelligence Components
Explain the three components of business intelligence: information and knowledge discovery, business analytics, and information visualization.

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Information and Knowledge Discovery:�Common Reports and Queries

Report/Query	Description
Scheduled reports	Reports produced at predefined intervals—daily, weekly, or monthly—to support routine decisions
Key-indicator reports	Reports that provide a summary of critical information on a recurring schedule
Exception reports	Reports that highlight situations that are out of the normal range
Drill-down reports	Reports that provide greater detail, so as to help analyze why a key indicator is not at an appropriate level or why an exception occurred
Ad hoc queries	Queries answering unplanned information requests to support a non-routine decision; typically not saved to be run again

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Information and Knowledge Discovery:�Online Analytical Processing (OLAP)

An OLAP cube is a multidimensional database structured to support slicing, dicing, and drill-down

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OLAP Terms and Concepts

Measures

Facts, numerical data that can be aggregated

Dimensions

Perspectives on which to view the facts
Hierarchically arranged to enable drill-down and roll-up

Cubes

Multidimensional structure of dimensions and measures

Slicing and Dicing

Analyzing data on a subset of dimensions

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Data Mining

Discovering “hidden” predictive relationships in the data
Complicated algorithms run on large data warehouses
Types of data mining algorithms

Association discovery
Clustering
Classification
Text and web content mining

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Information and Knowledge Discovery:�Unstructured Data Analysis

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Presenting the Results of Data Mining

Data mining results can be delivered to users in a variety of ways

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Business Analytics to Support Decision Making

Business Analytics
Decision Support Systems
Intelligent Systems

Machine learning (e.g., neural networks)
Expert systems
Intelligent agents

Knowledge Management Systems

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Architecture of a �Decision Support System (DSS)

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Architecture of an Expert System (ES)

The basic systems model is composed of input, process, and output is also incorporated in the architecture of expert systems.

An expert system (ES) is a type of intelligent system that uses reasoning methods based on knowledge about a specific problem domain in order to provide advice, much like a human expert. Whereas a DSS involves use of models (mostly mathematical), an expert system makes use of a knowledge base, often composed of IF-THEN rules applied to facts.

Often expert systems make use of fuzzy logic, in which rules are represented using approximations or subjective values in order to handle situations where information about a problem is incomplete.

The processing in an ES is called inferencing, which consists of matching facts and rules, determining the sequence of questions presented to the user, and drawing a conclusion.

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Machine Learning: Neural Networks

Neural networks approximate the functioning of the brain by creating common patterns in data and then comparing new data to learned patterns to make a recommendation

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Knowledge Management Systems

Explicit knowledge

Easily codified and documented

Tacit knowledge

Embedded in people’s minds
Hard to get at
Important for best practices

Goal: gain the greatest value from knowledge assets

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Information Visualization

Dashboards

Comprised of key performance indicators (KPIs)
Visual display of summary information
Aid in situational awareness and decision making

Visual Analytics

Interactive graphics for complex analysis

Geographic Information Systems

Visualizing geographic information

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Information Visualization: �Digital Dashboard of Business Data

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Information Visualization: �Geographical Data

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END OF CHAPTER CONTENT

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Managing in the Digital World: FBI and CIA—Gathering Intelligence Using Social Media

Social media is pervasive

Changing perspectives on privacy, reshaping relationships, increased information sharing

Organizations monitor and digest social media information flow
Police, first responders, NSA, also tap into social media to track crises, forecast social upheavals, and predict economic changes
Analyzing the huge volume of social media interactions is a Big Data problem

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When Things Go Wrong: Twitter Fever—Where Good Conscience Meets Bad Intelligence

Twitter allows very rapid dissemination and sharing of information
People often retweet something they find touching without verifying its accuracy first
The shortness of tweets also limits background and context, so information is easily distorted
Stories can gain rapid momentum regardless of truth or fact, and have damaging consequences

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Coming Attractions:�Intelligence Through Drones

University of Pennsylvania is doing research on autonomous flying robots (quadcopters) that can work together in formation
Amazon.com announced work on drones that can deliver packages 30 minutes after order
University of Aberdeen is developing drone technology that gathers geographic data to help farmers find ideal planting locations

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Brief Case: �Quality Assurance Through Call Recording

Different customer personalities relate better to different customer service approaches
E-loyalty software can classify a customer and direct a customer service representative how to handle the call

Personality types: Spock, Princess Diana, Rush Limbaugh, Robin Williams, Donald Trump, Yoda

Algorithms use past experience and call analysis to infer patterns
E-loyalty lowers call center operating expenses by 20 percent because of more productive calls

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Ethical Dilemma:�Are You Being Tracked?

Mobile phones emit a Temporary Mobile Subscriber Identifier (TMSI)
Software known as Footpath can triangulate this TMSI and track customers as they walk through malls
This tracking can shed light on shoppers’ individual tastes, tapping into their shopping patterns
Privacy concerns; what are the legal ramifications?

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Key Players: SAS, MicroStrategy, and other Business Intelligence Leaders

Business intelligence is becoming big business

Independent Pure Play analytic companies

SAS
MicroStrategy

Larger integrated companies

IBM
SAP
Oracle
Microsoft

Specialized companies also exist that focus on very specific aspects of business intelligence

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Who’s Going Mobile:�AroundMe

Mobile devices and location are a perfect match for each other
Search engines (Google, Bing) provide users with information on nearby restaurants
AroundMe—a popular location-based service

Uses phones GPS to find user’s location, and finds list of interesting places nearby
Makes use of ad platform Google Dynamic Mobile Advertisement

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Industry Analysis:�Health Care

Health care providers and insurance companies are turning to information systems

Electronic medical records and prescriptions
Digital imaging systems
Telemedicine
Robotic surgery

Consumers are turning to Web sites for health information

Dedicated sites such as WebMD
Social media postings related to a condition or symptoms