Download Business Intelligence Components

Chapter 6 - Enhancing Business Intelligence
Using Information Systems
Managers need high-quality and
timely information to support
decision making
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Agenda
• Today is all about business intelligence
My argument, at root, is that BI is about the
effective capture, manipulation, and
interpretation of data
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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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Why Does an Organization Need BI?
• Respond to threats and opportunities
– How will we know something is a threat unless we
track it
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Why Does an Organization Need BI?
• Exploitation of the Big Data Movement
– Information is freely available
– “Sometimes I have difficulty talking to people who don’t
race sailboats.” – Bruno Gianelli
• 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 Does an Organization Need BI?
• Effective Planning is Continuous
– Strategy is a multi-period game
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Why Organizations Need Business
Intelligence: Continuous Planning
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Why Does an Organization Need BI?
• Responding to threats an opportunities
• Big Data Movement
• Effective Planning is Continuous
Speculation with the future of the firm is the
hallmark of a bankrupt intellect when data is
AVAILABLE and FREE
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Ok, so BI is important…
• The first thing we need is a place to store all of
this data
• Where might we do that?
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Databases are cool
• All databases do is provide an efficient way to
store information in a way that is efficient,
stable, and secure
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Foundations
Attributes
• The traditional database you may have seen is
Entities
called an RDBMS
– Relational Database Management System
• It has three levels
– Entities, or tables
• What we are storing information about
Attributes
– Attributes, or columns
• The things that describe the entity
– Rows, or records, or tuples
• The actual data that you are storing
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Records
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Why do we do it this way?
• In the dark ages we would manage things in
giant lists
– Flat files
• How do you properly update this information?
• How do you change it?
• How do you manage it?
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BLARG!
• Low quality information example
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MORE BLARG!
Employee
Employee
Skill
Current Work Location
Current Work Location
Brown
73 Industrial Way
Harrison
73 Industrial Way
Jones
114 Main Street
Brown
Light Cleaning
73 Industrial Way
Brown
Typing
73 Industrial Way
Harrison
Light Cleaning
73 Industrial Way
Jones
Shorthand
114 Main Street
Brown
Light Cleaning
Jones
Typing
114 Main Street
Brown
Typing
Jones
Whittling
114 Main Street
Harrison
Light Cleaning
Jones
Shorthand
Jones
Typing
Jones
Whittling
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Employee
Skill
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How do we manage these data?
• The data resides in a database
– The MyFirm database
• The database sits on a server
– A powerful machine with lots of memory
• The data manipulation is done through a
database engine or a work bench
– SQL Server, Oracle, etc.
• The language we speak to the database in is
called SQL
– Structured Query Language
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SQL
• SQL is the simplest language conceived by man
• Four general commands
–
–
–
–
Select  retrieve data
Alter  Change the form of data
Update  Change the data itself
Drop  Get rid of things
Select [customer_name] from [customer_list]
where name = ‘Bob’
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And there are some super dooper benefits to
them
• Enable interactive website
• An example?
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Then we define the goal
• The RDBMS is the king of the database world
• But, slight modifications can help us achieve
more strategic goals
– i.e. specialized individual data models to increase
efficiency of use
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Databases & Data Warehouses
Operational
Databases
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Data Warehousing and Data Marts
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Hypercubes…
Create multi-dimensional
“cubes” of information
that summarize
transactional data across
a variety of dimensions.
OLAP vs. OLTP
Envisioned by smart
businesspeople, built by
the IT pros
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Types of Decisions You Face
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Scenario – Warehouse Manager
• You know you have too much cash tied up in
inventory. You want to reduce inventory levels.
• You get a lot of heat when orders are placed and
you can’t fill the order from inventory.
• What information do you need, how would you
like to see it and how do you make decisions
about adjusting inventory levels?
• Are these structured or unstructured decisions?
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Recap of Last Time
• Business Intelligence
– Why we need it – In Generalities…
• Data management fundamentals
–
–
–
–
–
–
In a database… why do we use entities?
Entities are described by what?
The actual data which populates the entity is what?
Why would we need multiple databases?
If we put many databases together it is called what?
If we split data off of the data warehouse, what do we
call it?
– Why would we do this?
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Agenda
•
•
•
•
How to store data
Where we might pull data from
How we might process these data
How we might present these data
Today’s class will harken back to the transformation
of data into knowledge. Having data is awesome,
but only if we can leverage it
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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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Business Intelligence Components
• Three types of tools
– Information and knowledge discovery
– Business analytics
– Information visualization
• Information and Knowledge Discovery
– Search for hidden relationships.
– Hypotheses are tested against existing data.
• For example: Customers with a household income over
$150,000 are twice as likely to respond to our marketing
campaign as customers with an income of $60,000 or less.
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Ad Hoc Reports and Queries
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Online Analytical Processing (OLAP)
• Complex, multidimensional analyses of data
beyond simple queries
• OLAP server —main OLAP component
• Key OLAP concepts:
–
–
–
–
–
–
–
Measures and dimensions
Cubes, slicing, and dicing
Data mining
Association discovery
Clustering and classification
Text mining and Web content mining
Web usage mining
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Cubes
• Cube—an OLAP data
structure organizing
data via multiple
dimensions
• Cubes can have any
number of dimensions
– Be careful, most people
can’t comprehend after
3 dimensions!
– When might we need
more?
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A cube with three dimensions
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Slicing and Dicing
• Slicing and dicing—analyzing the data on subsets
of the dimensions
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Data Mining
• Used for discovering “hidden” predictive relationships in the data
– Patterns, trends, or rules
– Example: identification of profitable customer segments or fraud
detection
– Any predictive models should be tested against “fresh” data.
• Data-mining algorithms are run against large data warehouses.
– Data reduction helps to reduce the complexity 0f data and speed up
analysis.
• ENDOGENEITY! - Bias resulting from omitted variables etc.
– Relationship between ice cream sales and crime
– Relationship between news media and firm founding
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Text mining the Internet
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Textual Analysis Benefits
• Marketing—learn about customers’ thoughts, feelings, and
emotions.
• Operations—learn about product performance by analyzing
service records or customer calls.
• Strategic decisions—gather competitive intelligence.
• Sales—learn about major accounts by analyzing news coverage.
• Human resources—monitor employee satisfaction or
compliance to company policies (important for compliance with
regulations such as the Sarbanes-Oxley Act).
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Web Usage Mining
• Used by organizations such as Amazon.com
• Used to determine patterns in customers’ usage
data.
– How users navigate through the site
– How much time they spend on different pages
• Clickstream data—recording of the users’ path
through a Web site.
• Stickiness—a Web page’s ability to attract and keep
visitors.
http://www.google.com/analytics/
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Web Usage Mining
• Google Analytics?
• News Related Information
– http://www.rhsmith.umd.edu/files/Documents/Cen
ters/Dingman/EntrepreneurshipResearchBooklet.pd
f#page=21
• Crowd Funding
– http://pubsonline.informs.org/doi/abs/10.1287/isre
.1120.0468
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Twitter Feeds
• Have you ever heard of anyone mining Twitter
feeds?
• As a business person, what kind of information
could you learn about your customers if you
subscribed to every Twitter feed imaginable and
mined the data?
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Presenting Results
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Any Danger?
• Is there any danger in a business student becoming
too “tech savvy”?
• Is there an danger in a business student not
becoming “tech savvy” enough?
• What is a “program” and is there anything that is
more nerdy that being a “programmer”?
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Business Analytics
• BI applications to support human and
automated decision making
– Business Analytics—predict future outcomes
– Decision Support Systems (DSS)—support human
unstructured decision making
– Intelligent systems
– Enhancing organizational collaboration
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Decision Support Systems (DSS)
• Decision-making support for recurring problems
– Structured or Unstructured?
• Used mostly by managerial level employees
(can be used at any level)
• Interactive decision aid
• What-if analyses
– Analyze results for hypothetical changes
– Example: Microsoft Excel
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Architecture of a DSS
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Common DSS Models
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Intelligent Systems

Three general types
 Expert Systems
 Neural Networks
 Intelligent Agent
Systems

http://www.radiolab
.org/story/137407talking-to-machines/
Spencer Platt/Getty Images, Inc.
© 2002 Paramount Pictures/Courtesy:
.Everett Collection.
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Expert Systems
• Likely the best example is WebMD
– http://symptoms.webmd.com/#introView
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Could You Use an Expert System?
• Talk to the person next to you about the various
jobs that you have had.
• Discuss situations where a decision tree could be
used to lead an employee who wasn’t really an
expert through a series of questions and eventually
to the answer they are looking for.
• Where is the intelligence…in the employee or the
decision tree?
• What benefit does the decision tree provide?
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Can you recognize patterns and be trained?
• You see a new breed of dog
• How do you know it is a
dog?
• How do you know it is even
an animal?
• How do you know if an
animal is a mammal?
• How about a whale?
• How about a platypus?
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Scenario – Loan Officer
• You need to make approval/rejection decisions on
loan applications?
• What information do you look at to make your
decisions?
• Do you make decisions based on individual pieces
of information or combinations of information?
• What combinations correlate with good/bad loans?
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Example: Neural Network System
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Intelligent Agent Systems
•
•
•
Program working in the background
Bot (software robot)
Provides service when a specific event occurs
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Types of Intelligent Agent Systems
• User agents
– Performs a task for the user
• Buyer agents (shopping bots)
– Search for the best price
• Monitoring and sensing agents
– Keep track of information and notifies users when it changes
•
Data-mining agents
– Continuously browse data warehouses to detect changes
• Web crawlers (aka Web spiders)
– Continuously browses the Web
• Destructive agents
– Designed to farm e-mail addresses or deposit spyware
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Knowledge Management
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Benefits and Challenges of Knowledge-Based Systems
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Information Visualization
• Display of complex data
relationships using
graphical methods
– Enables managers to quickly
grasp results of analyses
– Visual analytics
– Dashboards
– Geographic information
systems
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Digital Dashboards
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Dashboards
• Dashboards use various graphical elements to
highlight important information.
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Thematic Maps
• A thematic map showing car thefts in a town
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Geographic Information System (GIS)
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Recap
•
•
•
•
Online Analytical Processing
Data Mining
Decision Support Systems
Information Visualization
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PROJECT 1!
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