Transcript Business Intelligence

Business Intelligence
BI Fundamentals
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Business Transactions
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Data Bases
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Data Warehouses
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Data Marts
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Data Mining
Data bases (DB or DBMS)
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“A collection of information organized in such
a way that a computer program can quickly
select desired pieces of data.”
An electronic filing system
Organized by
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Fields: a single piece of information
Records: one complete set of fields
Files: a collection of record
Data warehouse (DW)
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“Contain a wide variety of data that present a
coherent picture of business conditions at a
single point in time.”
“A database system which contains
periodically collected samples or summarized
(aggregated) transactional data; e.g., daily
totals, or monthly averages”
Typically a compilation of information from
multiple transactional databases
Data mart
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“A database, or collection of databases,
designed to help managers make strategic
decisions about their business.”
A smaller and more focused form of a data
warehouse.
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Usually created for a particular department or position
A data mart created as a subset of data warehouse
data are referred to as a “dependent data mart”.
Data mining
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“A class of database applications that look for
patterns in data to be used to predict and
direct future behavior.”
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Increasingly being used by marketers to find
consumer data through the web and store
purchases.
What is BI?
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The new technology for understanding the past and predicting the
future
A broad category of technologies that allows for
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Gathering, storing, accessing and analyzing the data business users
make better decisions
Analyzing business performance through data-driven insight
A broad category of applications, which includes the activities of
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Decision support systems
Query and reporting
OLAP
Statistical, forecasting and data mining
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BI vs. AI
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AI systems make decisions for the users
BI systems help users make the right
decisions, based on the available data
However, many BI techniques have roots in
AI
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BI Processes
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Business Intelligence
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Data-Information–Knowledge–Decision Making Cycle
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What does BI seek to find?
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Patterns
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What kind of patterns?
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Sales
Stocks
Anything useful
Techniques for Finding
Patterns
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Statistics
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Trends
Correlation
(searching for a
best fit)
Patterns continued
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Combinatorial
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If-then relationships
Example
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If we put chips on sale on a Friday, then
we also sell more soda.
Leading the Industry
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Cognos
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BI software company
Software
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Used for reporting, analysis, scorecarding,
dashboards, business event management, and
data integration
Cognos
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Multiple Solutions
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Industry
 Banking
 Education
 Defense
 Government
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Department
 Executive Management
 Finance
 Marketing
Open Source Tools for BI
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ETL (Extract, Transform, Load) tools
OLAP (Online Analytical Processing) servers
OLAP clients
DBMSs (Data Base Management System)
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ETL Tools
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Bee
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CloverETL
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ROLAP (Relational OLAP) oriented ETL tool
ROLAP oriented ETL tool
Implemented in Java and uses JDBC to transfer
data
cloveretl.berlios.de
Octopus
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ROLAP oriented ETL tool
Implemented in Java and uses JDBC
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OLAP Servers
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Bee
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Lemur
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ROLAP oriented server
Uses mySQL to manage the DB
sourceforge.net/products/bee/
HOLAP oriented server
www.nongnu.org/lemur
Mondrian
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ROLAP oriented server
Implemented in Java
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OLAP Client
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Bee
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Web-based, used with Bee OLAP server
Generates pie, bar, chat, etc. (in 2D & 3D)
Export data to Excel, PDF, PNG, Powerpoint,
XML
Jpivot
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Web-based, used with Mondrian OLAP server
Generates 2D & 3D graphics
Export data to PDF
jvipot.sourceforge.net
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DBMSs
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MonetDB
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MySQL
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Run on Linux, Windows, Mac OS, etc.
monetdb.cwi.nl
Run on Linux, Windows
www.mysql.com/products/mysql
MaxDB
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Formely SAP DB (by SAP AG)
Run on Linux, Windows
www.mysql.com/products/maxdb
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PostgreSQL
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www.postgresql.org
Run on Linux, Unix, Windows (versi > 8.0)
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PALO OLAP
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Palo OLAP Server
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Palo ETL Server
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http://www.jedox.com/
Open source MOLAP server
be installed locally or in a company network
enables the efficient extraction of mass data from
heterogeneous data sources, ie. all common
relational database systems and flat files
Palo OLAP Client
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http://www.jpalo.com/en/
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Data Mining Softwares
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Open sources
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Borgelt data mining suite
Gnome data mine
Weka
RapidMiner
Commercials
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See5 (Rulequest)
Clementine (SPSS)
Enterprise Miner (SAS)
GhostMiner (Fujitsu)
Statistica Data Miner (StatSoft)
Oracle Data Miner (Oracle)
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Borgelt Data Mining Suite
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Tasks:
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Association: apriori, eclat
Classification: bayesian networks, decision
trees, naive bayes
Regression: neural networks
Clustering: self-organizing maps (SOM)
Platforms: Linux, Unix, MS Windows
Website:
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http://fuzzy.cs.unimagdeburg.de/~borgelt/software.html
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Genome Data Mine
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Tasks:
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Platforms: Linux, Unix, MS Windows
Website:
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Association: apriori
Classification: decision trees
http://www.togaware.com/datamining/gdatamine
Owner: Togaware, Canberra, Australia.
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WEKA
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Tasks:
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Association: apriori
Classification: decision trees, support vector machines,
conjunctive rules
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Platforms: Linux, Unix, MS Windows
Website:
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Clustering: k-means
http://www.cs.waikato.ac.nz/ml/
Owner: University of Waikato, Hamilton,
New Zealand
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RapidMiner
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http://rapid-i.com/
The world-leading open-source system for
knowledge discovery and data mining
Multiplaftorm: implemented in Java
Supports about 400 operators data mining
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Who uses BI?
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Businesses
The Government
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What are some ethical implications of the use
of BI?