1. Business Intelligence
Data Warehouse
DSS
Business Intelligence

2. Moving beyond one-way info delivery to true BPM
BI Evolution
Source: META Group Inc.
History
Legacy
Current
2005+
MIS
Reports
Decision
Support
Systems
Business
Intelligence
Business
Performance
Management
Hand coded
Report writers
OLAP
Dashboard/mining
Single system data
Joined operating data DW
Enterprise portals
Summary metrics
Statistical metrics
Predictive metrics
Recommendations
Extreme latency
Extreme cost
Extreme ‘infoglut’
Extreme integration
Moving beyond one-way info delivery to true BPM

3. BI Questions What happened? What’s happening? Why? What will happen?
What were our total sales this month?
What’s happening?
Are our sales going up or down, trend analysis
Why?
Why have sales gone down?
What will happen?
Forecasting & “What If” Analysis
What do I want to happen?
Planning & Targets
Source: Bill Baker, Microsoft

4. “The key in business is to know something thatBI
Business Intelligence (BI) is the process of gathering meaningful information to answer questions and identify significant trends or patterns, giving key stakeholders the ability to make better business decisions.
“The key in business is to know something that
nobody else knows.” -- Aristotle Onassis
PHOTO: HULTON-DEUTSCH COLL
“To understand is to perceive patterns.”
— Sir Isaiah Berlin
"The manager asks how and when,
the leader asks what and why."
— “On Becoming a Leader” by Warren Bennis

5. BI Definition
Business intelligence provides the ability to transform data into usable, actionable information for business purposes. BI requires:
Collections of quality data and metadata important to the business
The application of analytic tools, techniques, and processes
The knowledge and skills to use business analysis to identify/create business information
The organizational skills and motivation to develop a BI program and apply the results back into the business

6. Business Intelligence
Increasing potential
to support
business decisions (MIS)
End User
Making
Decisions
Data Presentation
Business
Analyst
Visualization Techniques
Data Mining
Data
Analyst
Information Discovery
Data Exploration
OLAP, MDA,
Statistical Analysis, Querying and Reporting
Data Warehouses / Data Marts
DBA
Data Sources
(Paper, Files, Information Providers, Database Systems, OLTP)

7. Inmon's Definition of Data Warehouse – Data View
A warehouse is a
subject-oriented,
integrated,
time-variant and
non-volatile
collection of data in support of management's decision making process.
Bill Inmon in 1990
Source:

8. Inmon's Definition Explain
Subject-oriented: They are organized around major subjects such as customer, supplier, product, and sales. Data warehouses focus on modeling and analysis to support planning and management decisions v.s. operations and transaction processing.
Integrated: Data warehouses involve an integration of sources such as relational databases, flat files, and on-line transaction records. Processes such as data cleansing and data scrubbing achieve data consistency in naming conventions, encoding structures, and attribute measures.
Time-variant: Data contained in the warehouse provide information from an historical perspective.
Nonvolatile: Data contained in the warehouse are physically separate from data present in the operational environment.

9. The Data Warehouse Process
Data Marts and cubes
Source
Systems
Data
Warehouse
Clients
Query Tools
Reporting
Analysis
Data Mining 1 2 3 4
Design the Populate Create Query Data Warehouse Data Warehouse OLAP Cubes Data

10. BI Architecture
Source:

11. BI Infrastructure Components

12. Key Concepts in BI Development Lifecycle
Technology
Data
Application

13. Performance Dashboards for Information Delivery

14. OLTP Versus Business Intelligence: Who asks what?
OLTP Questions
When did that order ship?
How many units are in inventory?
Does this customer have unpaid bills?
Are any of customer X’s line items on backorder?
Analysis Questions
What factors affect order processing time?
How did each product line (or product) contribute to profit last quarter?
Which products have the lowest Gross Margin?
What is the value of items on backorder, and is it trending up or down over time?

15. Classification of Entity Types

16. Transaction Level Order Item Fact Table

17. OLTP Versus OLAP OLTP Questions When did that order ship?
How many units are in inventory?
Does this customer have unpaid bills?
Are any of customer X’s line items on backorder?
OLAP Questions
What factors affect order processing time?
How did each product line (or product) contribute to profit last quarter?
Which products have the lowest Gross Margin?
What is the value of items on backorder, and is it trending up or down over time?

18. Requirements

19. Dimensional Design Process
Business
Requirements
Select the business process to model
Declare the grain of the business process/data in the fact table
Choose the dimensions that apply to each fact table row
Identify the numeric facts that will populate each fact table row
Data
Realities

20. Star Schema
Source: Moody and Kortink, "From ER Models to Dimensional Models: Bridging the Gap between OLTP and OLAP Design, Part I," Business Intelligence Journal, Summer 2003, pp

21. Identifying Measures and Dimensions
Performance Measures for KPI
Performance Drivers
Measures
Dimensions
The attribute varies
continuously:
Balance
Unit Sold
Cost
Sales
The attribute is perceived as
a constant or discrete value:
Description
Location
Color
Size

22. A Dimensional Model for a Grocery Store Sales
Why?

23. The Fact Table contains keys and units of measure
Facts Table
Measurements of business events.
DateID
ProductID
CustomerID
Units
Dollars
Dimensions
Measures
The Fact Table contains keys and units of measure

24. Fact Tables Fact tables have the following characteristics:
Contain numeric measures (metric) of the business
May contain summarized (aggregated) data
May contain date-stamped data
Are typically additive
Have key value that is typically a concatenated key composed of the primary keys of the dimensions
Joined to dimension tables through foreign keys that reference primary keys in the dimension tables

25. Store Dimension
It is not uncommon to represent multiple hierarchies in a dimension table. Ideally, the attribute names and values should be unique across the multiple hierarchies.

26. Inside a Dimension Table
Dimension table key: Uniquely identify each row. Use surrogate key (integer).
Table is wide: A table may have many attributes (columns).
Textual attributes. Descriptive attributes in string format. No numerical values for calculation.
Attributes not directly related: E.g., product color and product package size. No transitive dependency.
Not normalized (star schemar).
Drilling down and rolling up along a dimension.
One or more hierarchy within a dimension.
Fewer number of records.

27. Product Dimension SKU: Stock Keeping Unit Hierarchy:
Department  Category  Subcategory  Brand  Product

28. Hierarchy

29. Levels and Members Year Quarter Month 1999 Quarter 1 Jan Feb Mar
Apr
May
Jun
Quarter 3
Jul
Aug
Sep
Quarter 4
Oct
Nov
Dec

30. Operations in Multidimensional Data Model
Aggregation (roll-up)
dimension reduction: e.g., total sales by city
summarization over aggregate hierarchy: e.g., total sales by city and year -> total sales by region and by year
Selection (slice) defines a subcube
e.g., sales where city = Palo Alto and date = 1/15/96
Navigation to detailed data (drill-down)
e.g., (sales - expense) by city, top 3% of cities by average income
Visualization Operations (e.g., Pivot)

31. Drilling down in a data mart is nothing more than adding row headers from the dimension tables. Drilling up is removing row headers.
We can drill down or up on attributes from more than one explicit hierarchy and with attributes that are part of no hierarchy.

32. Avoid Null Key in the Fact Table
Include a row in the corresponding dimension table to identify that the dimension is not applicable to the measurent.
11: No Promotion
Sales Fact Table

33. Querying the Retail Sales Schema

34. Dragging and dropping dimensional attributes and facts into a simple report

35. ETL ETL = Extract, Transform, Load
Moving data from production systems to DW
Checking data integrity
Assigning surrogate key values
Collecting data from disparate systems
Reorganizing data

36. Building The Warehouse
Transforming Data

37. Pivot Table in Excel

38. Use of Data Mining Customer profiling Market segmentation
Buying pattern affinities
Database marketing
Credit scoring and risk analysis

39. OLAP and Data Mining Address Different Types of Questions
While reporting and OLAP are informative about past facts, only data mining can help you predict the future of your business.
OLAP 
Data Mining 
What was the response rate to our mailing? 
 What is the profile of people who are likely to respond to future mailings?
 How many units of our new product did we sell to our existing customers?
 Which existing customers are likely to buy our next new product?
 Who were my 10 best customers last year?
 Which 10 customers offer me the greatest profit potential?
 Which customers didn't renew their policies last month?
 Which customers are likely to switch to the competition in the next six months?
 Which customers defaulted on their loans?
 Is this customer likely to be a good credit risk?
 What were sales by region last quarter?
 What are expected sales by region next year?
 What percentage of the parts we produced yesterday are defective?
 What can I do to improve throughput and reduce scrap?
Source:

40. UA’s Existing Data Warehouse
Balanced Scorecard
Improve Stakeholder Value
Financial Perspective
Revenue Growth Strategy
Stakeholder Value
ROCE
Productivity Strategy
Build the Franchise
Increase Customer Value
Improve Cost Structure
Improve Asset Utilization
New Revenue Services
Profitability
Cost per Unit
Asset Utilization
Customer Acquisition
Customer Retention
Customer Profitability
Product Leadership
Customer Intimacy
Customer Perspective
Customer Value Proposition
Operational Excellence
Product/Service Attributes
Relationship
Image
Price
Quality
Time
Function
Service
Relations
Brand
Customer Satisfaction
“Build the Franchise”
“Increase Customer Value”
“Achieve Operational Excellence”
“Become a
Good Neighbor”
Internal Perspective
(Innovation Processes)
(Customer Management Processes)
(Operations & Logistics Processes)
(Regulatory & Environmental Processes)
Learning & Growth Perspective
A Motivated and Prepared Workforce
Strategic Competencies
Strategic Technologies
Climate for Action