1. CISB594 – Business Intelligence
Business Analytics and Data Visualization
Part II

2. Reference
Materials used in this presentation are extracted mainly from the following texts, unless stated otherwise.

3. Objectives At the end of this lecture, you should be able to:
Explain the concept of multidimensionality and how can it improve decision making
Explain the concept of cube in multidimensionality
Explain Geographic Information Systems (GIS) and its role in BI
CISB594 – Business Intelligence

4. OLAP vs. OLTP OLTP OLAP Users Clerk, IT professional Knowledge worker
Function
Day to day operations
Decision support
DB Design
Application-oriented
Subject-oriented
Data
Current, up-to-date
detailed, relational
Historical, multidimensional
Usage
Repetitive
Ad-hoc
Access
Read/write
Lots of scans
Unit of Work
Short, simple transaction
Complex query
# Records Accessed
Tens
Millions
# Users
Thousands
Hundreds
DB Size
100MB-GB
100GB-TB

5. Relational Database vs. Dimensional Database
A relational database is a collection of relations or tables
Purpose – relational is designed more for data updating, dimensional is meant more for data retrieving (BI)
By using this model, you can examine the Sales table to find that 5 bolts have been purchased. Then you can check the Order table to find out that the purchase is done by Customer Id AAA002 . Then you can check the Customer table to find out that Customer Id AAA002 is actually Samantha Jones
Imagine this retrieval is done on terabytes of data !

6. Relational Database vs. Dimensional Database
A dimension database model consists of one fact table and multiple dimension tables which are smaller. (Kimball). Common kind of schema used when designing data models for dimension database is a star schema (Dimensional Data Model)
The star schema (sometimes referenced as dimensional model) is the simplest data warehouse schema, consisting of a single "fact table“ with one segment for each "dimension”

7. Relational Database vs. Dimensional Database
All data that represent order transaction are placed into one table ie fact table
The fact table refers to several tables called dimension table – consists data used to retrieve sales information
A simple data ware house structure

8. Relational Database vs. Dimensional Database

9. Multidimensionality
Perhaps the best starting point to approach the multidimensional model effectively is a by looking at the types of queries for which this model is best suited.
Example of common queries in BI
"What is the total amount of receipts recorded last year per state and per product category?"
"What is the relationship between the trend of PC manufacturers' shares and quarter gains over the last five years?"
"Which orders maximize receipts?"
"Which one of two new treatments will result in a decrease in the average period of admission?"
"What is the relationship between profit gained by the shipments consisting of less than 10 items and the profit gained by the shipments of more than 10 items?"

10. Multidimensionality
Another example of query : A manager wants to know the sales of a product, by unit or dollar in a certain geographic area, by a specific salesperson, during a specific month. The answer to such question can be provided fast if the data is organized in multidimensional database or if query or related software products are designed for multidimensionality.

11. Multidimensionality Multidimensionality
The ability to organize, present, and analyze data by several dimensions, such as sales by region, by product, by salesperson, and by time (= four dimensions) is called multidimensionality
Done during Business Analytical application design using OLAP technology (to enable retrieving certain data from data warehouse for further analysis)
An example of OLAP technology is cube database (sometimes the OLAP application points to the data in data warehouse for analysis, and sometimes they can have a capability to retrieve the data and store data using cube database)

12. Understanding cube
Conceptually, a multidimensional database uses the idea of a data cube to represent the dimensions of data available to a user.
For example, "sales" could be viewed in the dimensions of product model, geography, time, or some additional dimension.
The design of cube database is planned and implemented by the database developer based on users requirement

13. Cube Database – allowing for slice and dice
Dice down to the location
Example query:
How many bolts were sold in the year 2005 by the Central branch?
Slice the year 2005
Dice down to the product

14. Advanced Business Analytics
While OLAP concentrates on reporting and queries, a more sophisticated way of analyzing data and information is needed
Users today will want to perform statistical and mathematical analysis such as hypothesis testing, multiple regression, prediction and customer scoring models.
Such investigation cannot be done with basic OLAP and will require special tools, including data mining and predictive analysis – hence, advanced business analytics

15. Advanced Business Analytics
A major step in managerial decision making is forecasting or estimating the results of different alternative courses of actions
Two methods that can be used for advanced business analytics are
Data mining
Predictive analysis

16. Advanced Business Analytics
Data mining
Tools that would automatically extract hidden and search for pattern in large transaction database. OLAP can only answer questions you are certain to ask, whereas data mining answers questions you don’t necessarily know you should ask
Predictive analysis
Use of tools that help determine the probable future outcome for an event or the likelihood of a situation occurring.

17. Geographic Information Systems (GIS)
Geographical information system (GIS)
A computer based system for capturing, storing, modeling, retrieving, checking, analyzing and displaying geographically referenced data by using digitized maps

18. Geographic Information Systems (GIS)
As GIS tools become increasingly sophisticated and affordable, they help more companies and governments understand:
Precisely where their trucks, workers, and resources are located
Where they need to go to service a customer
The best way to get from here to there
The concentration of population based on income

19. Geographic Information Systems (GIS)
GIS and decision making
GIS applications are used to improve decision making in the public and private sectors including:
Dispatch of emergency vehicles
Transit management
Facility site selection
Drought risk management
Wildlife management
Local governments use GIS applications for mapping and other decision-making applications

20. Geographic Information Systems (GIS)
Examples of GIS Applications
Organization
GIS Application
Pepsi Cola Inc
Use GIS in site selection for new Taco Bell and Pizza Huts by combining demographic data and traffic patterns
Kmart
Uses GIS to support planning for truck routes
CellularOne Corporation
Maps its entire cellular network to identify clusters of call disconnects and to dispatch technicians accordingly

21. Geographic Information Systems (GIS)
GIS combined with GPS
Global positioning systems (GPS)
Wireless devices that use satellites to enable users to detect the position on earth of items (e.g., cars or people) the devices are attached to, with reasonable precision
Example of usage :
New York City pioneered CompStat, which uses GIS to map criminal activity and GPS for police deployment by date, time, location. Resulting in reduction of crime rate by 70% in the past decade

22. Objectives At the end of this lecture, you should be able to:
Explain the concept of multidimensionality and how can it improve decision making
Explain the concept of cube in multidimensionality
Explain Geographic Information Systems (GIS) and its role in BI
CISB594 – Business Intelligence