business intelligence and multidimensional database

Business Intelligence and

Multi Dimensional Database

Contributed by

Md. Rezaunnabi

Junior Officer,

Department of Engineering.

Russel Chowdhury

Assistant Manager,

Department of Engineering.

Initiated and supervised by,

Muhammad Mahbub Hussain

Managing Director,CIBL Technology Consultant Limited.

Contents How a general Business Application runs?

Example of Relational Database of a Sales Application

Problems of normal Business Application with Relational DB

Multidimensional Database(MDB)

Why Multidimensional Database?

Multidimensional Database Design & Architecture

Cube : Basic operations

Business Intelligence System(BI)

Steps Involved in a BI System : Data warehouse, Dimension modeling,OLAP

Business Intelligence System providing Industry in Bangladesh

How a general business application

runs?

It uses Relational Database.

Relational Database model uses a two-dimensional structure of rows and

columns to store data.

Tables can be linked by common key values.

Example of Relational Database of a

Sales Application

Product Table

Sales Table

Customer Table

Product Id Name Unit Price Sales Id

1 Headset Ball

Bearings

23 1

2 Chaining Nut 4 2

3 Mountain End

Caps

15 3

Sales Id Date Amount Customer Id

1 23-2-2016 4 2

2 28-2-2016 2 3

3 1-3-2016 6 1

Customer Id Name City Phone

1 Ruben Torres New York 500 555-0162

2 Christy Zhu San Francisco 500 555-0110

3 Marco Mehta Chicago 500 555-0162

A Marketing Analyst of this system might ask following questions:

• How did a product sell last month?

• How does this figure compare to sales in the same month over

the last five years?

• How did the product sell by region, territory?

• Did this product sell better in particular regions? Are there

regional trends?

Problems of normal Business Application

with Relational DB

Problems of normal business application

with relational DB

A normal Business Application with Relational Database may be

able to give answer to these questions . But will face some

difficulties -

• Accessing data requires complex joins of many tables where

there is a large number of tables.

• Complex queries takes huge time to return the results if there is a

lots of data.

Multidimensional Database(MDB)

• These overheads of relational database can be managed easily by

using Multidimensional Database(MDB).

Multidimensional Database is defined as "a variation of the relational

model that uses multidimensional structures to organize data and

express the relationships between data".

The structure is broken into cubes

and the cubes are able to store and

access data within the confines of

each cube(dimensions).

In a multi-dimension database

system each individual data value

is contained within a cell accessible

by multiple indexes.

Why Multidimensional Database ?

Databases are developed according to user's preferences, in

order to be used for specific types of retrievals.

Enables interactive analyses of large amounts of data for decision-

making purposes

Rapidly process the data in the database so that answers can be

generated quickly.

Enhance data presentation and navigation by intuitive

spreadsheet like views that are difficult to generate in Relation

Database.

Multidimensional Database Design &

Architecture

The multidimensional data model is composed of logical cubes,

measures and facts, dimensions and dimensions categories.

Cube: It is a multidimensional data structure that holds data

that's been aggregated to return data quickly when a query fires.


Architecture Dimensions

• Dimensions are a group of

attributes based on columns

of tables of a view.

• Dimensions are always

independent of a cube so

they can be used in

multiple cubes.

• Dimensions are the criteria

onto which analysis of

business data is performed,

like time, geography and so

on.


Architecture Categories and Hierarchies

• Each dimension includes different levels of categories.

• Categories can be at different levels of information within a dimension.


Architecture Measures and Facts

• The Measures are the actual data values that occupy the cells as

defined by the dimensions selected.

• Fact is a Measure Group that is always

associated directly with at least one dimension.

Example : Sales, Performance, Tax etc.

• Calculated Measures are created using

Multidimensional Expressions(MDX) with/

without base measures.

Example: Total Sales , Average Sales.


Three important operations associated with data cubes –

Slicing

Dicing

Rotating

Slicing

• The term slice most often refers to a two dimensional page selected

from the cube.

• Subset of a multidimensional array corresponding to a single value for

one or more members of the dimensions not in the subset.

• Two dimensions vary and one is kept fixed.


Slicing

Slicing-Wireless Mouse


Slicing


Dicing

• A related operation to slicing.

• In the case of dicing, we define a sub cube of the original space.

• Dicing provides you the smallest available slice.

• All dimensions are kept fixed to obtain a point of data.


Dicing


Rotation

• Some times called pivoting.

• Rotating changes the dimensional orientation of the report from the

cube data.

• For example :

Rotating may consist of swapping the rows and columns, or moving

one of the row dimensions.


Rotation

Business Intelligence System(BI)

Business Intelligence is a system for transforming data into

information using MDB cube.

This information helps to make quick decisions.

BI technologies are capable of handling large amounts of

unstructured data to help identify, develop and otherwise create

new strategic business opportunities.

Steps Involved in a BI System Data Collection and storing

• Data is collected by ETL tools.

• It takes the data from various source locations, maybe as a different

data format (for example SQL, txt, xls and so on) and store this data

into a destination (Data Warehouse).

Data analysis

• Business intelligence combines a broad set of data analysis

applications, including ad hoc analysis and querying, online analytical

processing (OLAP), mobile BI, real-time BI, operational BI etc.

Reporting

Steps Involved in a BI System

Steps Involved in a BI System Data Collection and storing

Data, Data everywhereyet ...

• I can’t find the data I need

data is scattered over the network

many versions, subtle differences

• I can’t get the data I need

need an expert to get the data

• I can’t understand the data I found

available data poorly documented

• I can’t use the data I found

results are unexpected

data needs to be transformed from one form

to other


Data Collection and storing

• What is a Data Warehouse?

A single, complete and consistent store of

data obtained from a variety of different

sources made available to end users in a

what they can understand and use in a

business context.

• What is Data Warehousing?

A process of transforming data into information

and making it available to users in a timely enough

manner to make a difference

Data

Information

Steps Involved in a BI System :

Dimensional Modeling

Dimensional modeling (DM) is a technique used in data

warehouse design.

Dimensional Modeling is a logical design technique that present the

data in a standard framework that allows for high-performance

access.

In DM, a model of tables and relations is constituted with the

purpose of optimizing decision support query performance in

relational databases.


Dimensional Modeling Fact Table

• Fact Table consists of the Measures and Facts

of the business process.

• A Fact Table typically has two types of columns:

those that contains Measures(numerical values)

and those that are foreign key to Dimension Tables.

Dimension Table

• The Dimension Table provides the detailed

information about the attributes in the Fact Table.

• Fact Tables connect to one or more Dimension Tables

but Fact Tables do not have direct relationships to

one another.



Star Scheme

• In the star schema design, a fact table sits in the middle and is

connected to other surrounding dimension tables like a star.

• A star schema has one dimension table for each dimension.


Dimensional Modeling Star Scheme



Snowflake Scheme

• Snowflake schemas contain several Dimension Tables for each

dimension.

Advantage and Disadvantage

• The main advantage of the snowflake schema is that it reduces the

space required to hold the data and the number of places where

it need to be updated if the data changes.

• The main disadvantage of the snowflake schema is that it increase

the number of tables that need to join in order to perform the

given query.


Dimensional Modeling Snowflake Scheme


Data Analysis

OLAP (Online analytical processing)

• OLAP is a multidimensional, multiuser, client-server computing

environment for users who need to analyze enterprise data.

• OLAP performs multidimensional analysis of business data from data

warehouse.

• At the core of any OLAP system is an OLAP cube (also called a

'multidimensional cube' or a hyper cube).

• The usual interface to manipulate an OLAP cube is a matrix interface,

like Pivot Tables in a spreadsheet program, which performs projection

operations along the dimensions, such as aggregation or averaging.


Data Analysis

Applications of OLAP

• Finance departments use OLAP for applications such as budgeting,

activity-based costing (allocations), financial performance

analysis, and financial modeling.

• Sales departments use OLAP for sales analysis and forecasting.

• Marketing departments use OLAP for market research analysis,

sales forecasting, promotions analysis, customer analysis, and

market/customer segmentation.

• Typical manufacturing OLAP applications include production

planning and defect analysis.


Data Analysis

OLAP Tools

IBM® Cognos® PowerPlay®

Oracle Essbase

Microsoft SQL Server Analysis Services (SSAS)


Reporting:

• Various Business Intelligence Tools are used report data for Business

Intelligence like JasperReports, Crystal Reports, Microsoft

Sharepoint, Excel, Oracle Reports, Cognos BI etc.

• Some sample reports generated from OLAP cube on the next slide.


Reporting example :

Figure: A OLAP report Using Windows Form Application

Steps Involved in a BI System Reporting example :

Figure: A OLAP report Using Microsoft Sharepoint

Steps Involved in a BI System Reporting example :

Figure: A OLAP report Using Microsoft Power BI

Business Intelligence System

providing Industry in Bangladesh

Thank you!Any questions?

Next Session -Implementation of OLAP with SSAS Retreive data from a data warehouse

Design and deploy a cube

Generate a report from the cube

THE END

business intelligence and multidimensional database

Data & Analytics