Download Business Intelligence Components Three Types of Tools Info

Business Intelligence Components
Three Types of Tools
 Info. & Knowledge Discovery
o Search for hidden relationships
o Hypotheses are tested against existing data
 Ex: Customers wit a household income over $150,000
are twice as likely to respond
 Business Analytics
 Info. Visualization
Online Analytical Processing
 Complex, multi dimensional analyses of data beyond simple queries
 OLAP Server-main OLAP component
o Measure and dimensions
o Data Mining
o Association discovery
Cubes
 OLAP data structure organizing data via multiple dimensions
 Cubes can have any number of dimensions
o Most can’t comprehend have 3 dimensions
o Why might more than 3 dimensions be needed?
 Ex: Wal-Mart dividing their data into more and more ways into
things like online shopping, mobile shopping, in store, mobile
v. online, etc.
 Slicing and dicing-analyzing the data on subsets of the dimensions
Data Mining
 Used for discovering “hidden” predictive relationships in the data
o Patterns, trends, or rules
 Ex: identification of profitable customer segments or fraud
protection. Credit card companies do this for its customers to
minimize risk of theft.
o Predictive models to be tested against “fresh” data as well
 Data mining algorithms are run against large data warehouses
o Data reduction helps us reduce the complexity of data and speed up
analysis
 ENDOGENENITY: bias resulting from omitting variables
o Ex: relationship between ice cream sales and crime. Temperature
influences both of these, but this variable is omitted, there fore
resulting in endogenenity.
Text Mining the Internet
 Analytical techniques for extracting information from text on webpages
o Ex: Craigslist, Facebook
Textual Analysis Benefits
 Marketing-learn about customer’s thoughts, feelings, and emotions
 Operations-learn about product performance by analyzing service records or
customer calls
 Strategic decisions-gather competitive intelligence
Web Usage Mining
 Used by organizations such as Amazon.com
 Used to determine patterns is customer’s usage data
o How users will navigate through the site
o How much time they spend on different pages
 Clickstream data-recording the user’s path through the website
 Stickiness-a web pages ability to attract and keep users
 Google Analytics
Twitter Feeds
 Have you ever heard of anyone mining Twitter feeds?
o As a businessperson, what kind of information could you learn about
your customers if you subscribed to every twitter feed imaginable and
mined the data?
 Who your demographics are, what the customers thing of the
product, learn what people like, etc.
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Is there any danger in a business student becoming too “tech savy”?
o As a result, it can lead to becoming to dependent on the use of
technology.
Business Analytics
 BI applications to support human and automated decision making
o Used to predict future outcomes
o Decision Support Systems-support human unstructured decision
making
DSS
 Support for recurring problems
o Structured or unstructured?
 Mostly used at the managerial level
 Used for automated decision-making
Intelligence Systems
 Expert Systems
o Uses reasoning methods based on knowledge about a specific
problem domain in order to prove advice, much like a human expert
 Ex: WebMD symtomchecker
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Neural networks
o Network of processing elements that work together to complete a task
and attempt to approximate the functioning of the human brain and
can learn by example
 Trained by having it categorize a large data base of pasting
information for common patterns
Intelligent Agent Systems
o User agents
o Performs a task for users
Buyer agents
o Search for the best price
Monitoring and sensing agents
o Keeps track of key information to notify user when conditions change
Data-mining agents
o Continuously analyze large data warehouses to detect changes
deemed important by a user, sending notifications when changes
occur
Webcrawlers
o Continuously browse web for specific information
Destructive agents
o Malicious agents designed by spammers and other Internet attackers
to farm email addresses off websites or deposit spyware on machines