Unit 14 business intelligence
6 Multidimensional Data Models and Analysis
1
Why Data Model?
▪ To make better decisions faster, knowledge workers required
useful information and those information flexible enough to
analysis.
▪ The problem many organization faced was how to convert these
raw data gathered in to information that ready for analysis.
▪ Responsibility of Business Intelligent system (BI) to convert these
raw data to flexible easy to analyze information by properly
structuring and presenting them using various data models.
2
Raw Data…. Where They Come From ?
▪ Raw data come to the organization via operational data
sources. These may include
▪ OLTP systems.
▪ File systems and individual files
▪ Legacy systems.
▪ External data sources
3
Raw Data… Why We Can’t Analyze
▪ OLTP data structures are not efficient for answer analysis
queries
▪ The raw data available OLTP is not very useful for analysis.
▪ Limited/pre made reporting and visualization options.
▪ Not easy to handle the calculation, sorting, summarizing .
▪ Analysis queries reduce OLTP system performance.
▪ System contain non aggregated, system specific data (Data
Silos)
▪ OLTP does not support Multi dimensional data analysis
4
OLAP
E.F Codd inventor of relational model coined the term
OLAP. He gives three must have capabilities OLAP
Systems.
▪ Must support multidimensional analysis.
▪ Fast information retrieval time
▪ Must have robust calculation engine to handle calculations
efficiently across the all dimension
5
OLAP Key Words
▪ Dimension
All the members within the domain that can be group/aggregate
together or compared together.
▪ Bike Brand Honda, Yamaha, Suzuki, Kawasaki measurement
can compared together
▪ January ,February ,March can compare each other and
aggregate for 1st Quarter,Four Quarters can aggrigate to Year
and so on
▪ Measurement:
Any quantitative expression or value that analyzing in OLAP
system
▪ Sales value, Sales quantity , Number of registration
6
Data Cube or "Multi-Dimensional" Data Stores
▪ A long time a go sales data present as a sheet of paper.
Branch
Product
▪ Over the time each and every month add new sheet and
now it is a stack by creating cube
Branch
Product
Month
7
Multi-Dimensional Analysis
▪ This concept of data cube is
▪ Natural.
▪ Easy to understand.
▪ Effective. When analyzing data
▪ Even before BI and OLAP system proposed analyst and
managers interpret data in multi-dimensionally
▪ What was the total value of sales by branch by brand by each quarter.
▪ Each and every “By” qualify as Dimension
8
Example-Nippon Bike Shop
(Not a real data set)
9
Example-Nippon Bike Shop Aggregated Data
(Not a real data set and definitely not accurate)
10
Example-Nippon Bike Shop
Analyst Can easily convert
pervious report in to this
model
▪ Axis/Dimension.
▪ Branch
▪ Brand
▪ Time(Quarter ,Month, Year)
▪ Values/Measurement.
▪ Sales QTY
▪ Sales value ($)
11
Data Cubes
▪ Even though called this
structure data cube, the data
structure can add n… number
of dimensions. The correct
name is multidimensional
database.
▪ Could be store in computer
hard drive or RAM memory
12
Multi Dimensional Benefits
▪ Multi dimensional, Data structure can handle n.. number of
dimensions. Because of that analysis can conduct over
large number of dimensions.
▪ Simple structure for user to understand.
▪ Can be extract and analyze data effectively via prestructured and aggregated data
▪ Drill down achieved either within the cube or through other
cubes of lesser grain, or by accessing the Data Warehouse
13
Multi Dimensional Drawback
• Pre-design and storage of data necessary for the
Cubes
• Because of creating separate physical or logical data
base it is consume huge computer resources
• Rather inflexible as regards non pre-defined
requirements
• Data extraction gives operational overhead.
• Weaknesses in scarcity handling, can lead to increased
cube sizes
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Multi Dimensional Operation
(Slicing Data Cube)
▪ Slicing Data Cube men
selecting values from one
dimension.
▪ All sales from Kurunegala branch
by brand and by quarter.
▪ All sales of Yamaha motor bikes
by branch and by quarter.
▪ Example query
Select Branch, Quarter, count(*)
From Sales
Where Brand= ‘Yamaha ’
Group by Branch, Quarter
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Multi Dimensional Operation
(Dicing Data Cube)
▪ Select Values from two or
more dimension.
▪ Honda Bike sales In
Kurunegala.
▪ 1st Quarter sales in Colombo
Branch
▪ Example query
Select Quarter, Count( *)
From Sales
Where Brand= ‘Yamaha’ and Branch =‘Colombo’
Group by Quarter
16
Multi Dimensional Operation
(Drill down data cube)
▪ When knowledge worker required more detail view of data
they can drill down data cube .
▪ For example Nippon Bike shop data cube created for
quarterly sales values but if someone required to access
monthly or daily sales that is also possible if data is
available in data warehouse
▪ Example query
Select Year ,Month,Sum (sales_values)
From sales
Group by Year, Month
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Multi Dimensional Operation
(Roll up cube)
▪ When knowledge worker required more abstract view of
data they can Roll up or drill up data cube.
▪ After successfully running years If top management in
Nippon Bike shop management required yearly sales
figures. Then they should roll up sales values
▪ Example query
Select Year ,Sum (sales_values)
From sales
Group by Year
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Multi dimensional Operation
(Pivoting)
▪ Pivoting is the operation that re-organizing dimension to
change the view of data cube.
▪ Pivoting helps to make an effective data visualization by
re arranging X,Y,Z axis
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Multi dimensional Operation
(Pivoting)
Table Pivoted by quarterly sales by Branch By Brand
Honda
Q1
Q2
Q3
Q4
Yamaha
Suzuki
Kawasaki
Kurunegala
150
200
250
600
Colombo
200
100
50
250
Mathara
150
300
400
450
Kandy
450
50
200
100
50
450
100
200
Colombo
450
300
400
150
Mathara
100
200
250
50
Kandy
250
600
150
200
Kurunegala
200
100
50
250
Colombo
150
200
250
600
Mathara
450
50
200
100
Kandy
150
300
400
450
Kurunegala
450
300
400
150
Colombo
50
450
100
200
Mathara
250
600
150
200
Kandy
100
200
250
50
Kurunegala
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Multi dimensional Operation
(Pivoting)
Table Pivoted by Branch sales by quarter by brand
Honda
Kurunegala
Colombo
Mathara
Kandy
Yamaha
Suzuki
Kawasaki
Q1
150
200
250
600
Q2
50
450
100
200
Q3
200
100
50
250
Q4
450
300
400
150
Q1
200
100
50
250
Q2
450
300
400
150
Q3
150
200
250
600
Q4
50
450
100
200
Q1
150
300
400
450
Q2
100
200
250
50
Q3
450
50
200
100
Q4
250
600
150
200
Q1
450
50
200
100
Q2
250
600
150
200
Q3
150
300
400
450
Q4
100
200
250
50
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Bibliography
▪ Lamont, M. (2014). Business Intelligence: Multidimensional
Analysis. [online] YouTube. Available at:
https://www.youtube.com/watch?v=IhFkNmVmwn4&t=1285
s /> [Last Accessed 2 May 2020].
▪ Boyer, J. (2010) Business Intelligence Strategy. MC Press
(US).
▪ Marr, B. (2015) Big Data: Using Smart Big Data, Analytics
and Metrics to Make Better Decisions and Improve
Performance. 1st Ed. John Wiley & Sons, Ltd.
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Next Week
▪ Implementing BI Strategy and BI Systems
▪ What is Business Intelligence Strategy?
▪ Why Organizations Need BI System
▪ Creating Effective BI strategy
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