MCDA 5560

1. .
MCDA 5560 – Business
Intelligence
Business Intelligence Overview
Slide 4 – Business Report

2. .
Recap last class
• Data Visualization
• History
• Visual Analytics
• Trend
2

3. .
Things we learn today
• Business Report
3

4. .
Business Reporting
Definitions and Concepts
• Report = Information à Decision
• Report?
• Any communication artifact prepared to convey
specific information
• A report can fulfill many functions
• To ensure proper departmental functioning
• To provide information
• To provide the results of an analysis
• To persuade others to act
• To create an organizational memory…
Slide 2-4

5. .
What is a Business Report?
• A written document that contains information regarding
business matters.
• Purpose: to improve managerial decisions
• Source: data from inside and outside the organization
(via the use of ETL)
• Format: text + tables + graphs/charts
• Distribution: in-print, email, portal/intranet
Data acquisition à Information generation à Decision
making à Process management
Slide 1- 5Slide 2-5

6. .
Business Reporting
Slide 1- 6
Data
Repositories
Business Functions
UOB 1.0 X
UOB 2.2
UOB 2.1 X UOB 3.0
1
Machine
Failure
Symbol Count Description
Exception Event
Transactional Records
PHASE 5
DEPT 4
DEPT 3
DEPT 2
DEPT 1
PHASE 4PHASE 3PHASE 2PHASE 1
DEPLOYMENT CHART
1 2 3 4 5
Information
(reporting)
Decision
Maker
Action
(decision)
Data
Slide 2-6

7. .
Reporting Styles of a Modern BI System
7
Advanced reporting
Monitoring and
alerting
Advanced data
analytics

8. .
Application
Case
Flood of
Paper Ends
at FEMA

9. .
Review Question
• 1. What is a report? What are reports used for?
• 2. What is a business report? What are the main characteristics of a good business report?
• 3. Describe the cyclic process of management, and comment on the role of business
reports.
• 4. List and describe the three major categories of business reports.
• 5. What are the main components of a business reporting system?
9

10. .
The Art and Science of Data
Preprocessing
• The real-world data is dirty, misaligned, overly
complex, and inaccurate
• Not ready for analytics!
• Readying the data for analytics is needed
• Data preprocessing
• Data consolidation
• Data cleaning
• Data transformation
• Data reduction
• Art – it develops and improves with experience
Slide 2-10

11. .
The Art and
Science of Data
Preprocessing
• Data reduction
1. Variables
• Dimensional reduction
• Variable selection
2. Cases/samples
• Sampling
• Balancing / stratification

12. .
Data Preprocessing Tasks and Methods

13. .
Decision Support Data
• Effectiveness of BI depends on quality of data
gathered at operational level
• Operational data
• Seldom well-suited for decision support tasks
• Stored in relational database with highly normalized
structures
• Optimized to support transactions representing daily
operations
13

14. .
Decision Support Data
• Differ from operational data in:
• Time span
• Granularity
• Drill down: Decomposing a data to a lower level
• Roll up: Aggregating a data into a higher level
• Dimensionality
14
PRODUCT
The ORDER Fact Table contains the Total Value of the orders for a given year,
region, agent, and product. The dimension tables are YEAR, REGION, AGENT
and PRODUCT
Year
Region
Agent
Product
Total_Value
The Star Schema for the ORDER Fact Table
ORDER AGENT
REGION
YEAR

15. .
Contrasting Operational and Decision
Support Data Characteristics
15

16. .
Decision Support Database
Requirements
• Database schema
• Must support complex, non-normalized data
representations
• Data must be aggregated and summarized
• Queries must be able to extract multidimensional time
slices
16

17. .
Decision Support Database Requirements
• Data extraction and loading
• Allow batch and scheduled data extraction
• Support different data sources and check for inconsistent
data or data validation rules
• Support advanced integration, aggregation, and
classification
• Database size should support
• Very large databases (VLDBs)
• Advanced storage technologies
• Multiple-processor technologies
17

18. .
The Data Warehouse as an Active
Decision Support Framework
• Data warehouse:
• Is not a static database
• Is a dynamic framework for decision support that is
always a work in progress
• Data warehouse is critical component of modern BI
environment
• Design and implementation must be examined as
part of entire infrastructure
18

19. .
Characteristics of Data Warehouse and
Operational Database
19

20. .
DW Architecture
• Three-tier architecture
1. Data acquisition software (back-end)
2. The data warehouse that contains the data & software
3. Client (front-end) software that allows users to access
and analyze data from the warehouse
• Two-tier architecture
• First two tiers in three-tier architecture are combined
into one
… sometimes there is only one tier?

21. .
DW Architectures
Tier 2:
Application server
Tier 1:
Client workstation
Tier 3:
Database server
Tier 1:
Client workstation
Tier 2:
Application & database server

22. .
A Web-based DW Architecture
Web
Server
Client
(Web browser)
Application
Server
Data
warehouse
Web pages
Internet/
Intranet/
Extranet

23. .
Data Warehousing Architectures
• Issues to consider when deciding which
architecture to use:
• Which database management system (DBMS) should be
used?
• Will parallel processing and/or partitioning be used?
• Will data migration tools be used to load the data
warehouse?
• What tools will be used to support data retrieval and
analysis?

24. .
Alternative DW Architectures
Source
Systems
Staging
Area
Independent data marts
(atomic/summarized data)
End user
access and
applications
ETL
(a) Independent Data Marts Architecture
Source
Systems
Staging
Area
End user
access and
applications
ETL
Dimensionalized data marts
linked by conformed dimentions
(atomic/summarized data)
(b) Data Mart Bus Architecture with Linked Dimensional Datamarts
Source
Systems
Staging
Area
End user
access and
applications
ETL
Normalized relational
warehouse (atomic data)
Dependent data marts
(summarized/some atomic data)
(c) Hub and Spoke Architecture (Corporate Information Factory)

25. .
Source
Systems
Staging
Area
Normalized relational
warehouse (atomic/some
summarized data)
End user
access and
applications
ETL
(d) Centralized Data Warehouse Architecture
End user
access and
applications
Logical/physical integration of
common data elements
Existing data warehouses
Data marts and legacy systmes
Data mapping / metadata
(e) Federated Architecture
Alternative DW Architectures
• Each architecture has advantages and disadvantages!
• Which architecture is the best?

26. .
Ten Factors that Potentially Affect the
Architecture Selection Decision
1. Information interdependence
between organizational units
2. Upper management’s
information needs
3. Urgency of need for a data
warehouse
4. Nature of end-user tasks
5. Constraints on resources
6. Strategic view of the data
warehouse prior to
implementation
7. Compatibility with existing
systems
8. Perceived ability of the in-
house IT staff
9. Technical issues
10. Social/political factors

27. .
Last class recap
• Operational data VS decision support data
• 4 properties of data warehouse
27

28. .
Learn today
• Data Marts;
• Star Schema;
• - Fact, Dimension, Attribute, Attribute Hierarchy;
• Data warehouse performance;
28

29. .
Data Marts
• Small, single-subject data warehouse subset
• Provide decision support to a small group of people
• Benefits over data warehouses
• Lower cost and shorter implementation time
• Technologically advanced
• Inevitable people issues
29

30. .
Twelve Rules
for a Data
Warehouse
30

31. .