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Business Intelligence: Big Results with a Small Budget. Jeff Pittges Assistant Professor Radford University www.radford.edu/~jpittges [email protected] / 540-831-5175. Industry Background. Going Global. The following slides were presented by Paul Grossman at the

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business intelligence big results with a small budget

Business Intelligence:Big Results with a Small Budget

Jeff Pittges

Assistant Professor

Radford University

www.radford.edu/[email protected]/ 540-831-5175

going global
Going Global

The following slides were presented

by Paul Grossman at the

February 2009 NCTC Technology & Toast

ExportVirginia.org

the real world
THE REAL WORLD

POPULATION

Source: www.world mapper.org

the real world1
THE REAL WORLD

CONTAINER PORTS

Source: www.world mapper.org

the real world2
THE REAL WORLD

HIGH TECH EXPORTS1990

Source: www.world mapper.org

the real world3
THE REAL WORLD

HIGH TECH EXPORTS 2002

Source: www.world mapper.org

what if
What If
  • You could view your business like these maps of the world?
  • You could identify trends and compare your business to your competitors with respect to the market?
  • You could see opportunities?
business intelligence
Business Intelligence

A set of tools and techniques

that help people and companies

make better decisions

2009 gartner prediction
2009 Gartner Prediction

Because of lack of information, processes,

and tools, through 2012, more than 35 per-

cent of the top 5000 global companies will

regularly fail to make insightful decisions

about significant changes in their business

and markets.

http://en.wikipedia.org/wiki/Business_intelligence

bi technologies
BI Technologies
  • Data Warehousing
  • OLAP
  • Executive Dashboards
  • Data Mining
  • Decision Support Systems (DSS)
  • Expert Systems
warehouses report the facts
Warehouses Report the Facts
  • Who
  • What
  • When
  • Where
  • Why
o n l ine a nalytical p rocessing
OnLine Analytical Processing

OnLine Analytical Processing

OLAP

The process of slicing and dicing data:

  • Drill Down
  • Drill Up
  • Drill Across
olap example
OLAP Example

Analyze quarterly sales

  • Expected 10% increase in revenue
  • Realized a 9.5% increase
  • Why did quarterly revenue fall short of expectations?
investigate the facts
Investigate the Facts
  • Why were sales short of expectations?
  • When
    • Compare sales in Q1 2005 to Q1 2006
  • What -- Product hierarchy
  • Who -- Customers
slide18
When

Time Dimension

Year

Quarter

Month

Week

Day

sales by quarter

$109.5

9.5%

$100

Quarter

Q1 ‘05

Q1 ‘06

Sales by Quarter
slide21

Drill Down

into Department

Department

  • Clothes
  • Electronics
  • Books

Category

Brand

Product

What

Product Hierarchy

product dimension
Product Dimension

2

0

0

5

Q

1

Q

2

e

m

i

Q

3

T

Q

4

2

0

0

6

Q

1

Q

2

C

l

o

t

h

e

s

E

l

e

c

t

r

o

n

i

c

s

B

o

o

k

s

P

r

o

d

u

c

t

sales by department

10.4%

10.3%

8.7%

Q1

‘06

Q1

‘06

10%

Q1

‘06

Dept

Clothes

Electronics

Books

Sales By Department
drill down into books

Category

Drill Down into Books

Product Hierarchy

Department

Brand

Product

sales by book category

6.8%

10.6%

Q1

‘06

10%

Q1

‘06

Category

Novels

Textbooks

Sales by Book Category
slide27
Who

Customer Dimension

  • Age group
  • Gender
  • Marital status
  • Occupation
  • Annual income
drill down into age group

4.2%

10.9%

10.4%

11.1%

Q1

‘06

Q1

‘06

Q1

‘06

10%

Q1

‘06

Age

Under 25

25 - 45

46 - 65

Over 65

Drill Down into Age Group
analysis
Analysis
  • Sales of textbooks to customers under 25 (students) fell well short of expectations
  • What should the company do?
  • Increase advertisements and incentives for textbooks to students
monitoring your business
Monitoring Your Business
  • Management by Objective (MBO)
    • Sales -- revenue targets
    • Customer Support -- customer satisfaction
  • Key Performance Indicators (KPI)
    • Measure performance
  • Dashboard Displays KPIs
    • Color coded GreenYellowRed
slide34

Inventory

Level

Alexandria

Richmond

Roanoke

Clicking on Virginia drills down to

Inventory by City

data mining
Data Mining

Knowledge Discovery

Identify patterns in your data

market basket analysis
Market Basket Analysis

Identify items purchased together

data mining tasks
Data Mining Tasks
  • Predict
    • Churn Analysis
    • Increase response rate
  • Estimate
    • Customer satisfaction and renewal rate
  • Classify
    • Fraud Detection
enterprise architecture

GUI

Reporting

OLAP

Production

Systems

Data

Warehouse

External

Data

Sources

Extract

Load

Data

Mining

Transform

Enterprise Architecture
open source technologies

Pentaho

MySQL

JasperSoft

Weka

Reporting

Data Integration (ETL)

Warehouse

Reporting

Mining

Data

Warehouse

Data

Mining

Extract

Transform

Load

Open Source Technologies
service providers
Service Providers

Software as a Service (SaaS)

On Demand

Hosted Applications

slide43

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Competitive Intelligence

  • Real-time intelligence
    • Companies, people and markets
  • Easy to use, web-based system
  • Customized tracking according to your

company’s lines of business

  • Online dashboard
  • Automated e-mail alerts
  • Extensive web marketing analytics
  • Cost-effective month-to-month subscription
ru can help you

Computer Science

Database

Information Systems

Software Engineering

Networking

Web Development

RU Can Help You
  • Six Concentrations
  • Internships and Permanent positions
  • Small Project Support Center
references
References
  • Attaain
  • JasperSoft
  • MySQL
  • Pentaho
  • Weka
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