Business Intelligence

“You can’t manage what you can’t measure.

You can’t measure what you can’t describe”

Ahsan Kabir

“A broad category of applications and technologies for gathering, storing,

analyzing, sharing and providing access to data to help enterprise users make

better business decisions” -Gartner

Why BI ?

Performance management

Identify trends

Cash flow trend

Fine-tune operations

Sales pipeline analysis

Future projections

business Forecasting

Decision Making Tools

Convert data into information

How to Think ?

• What happened?

• What is happening?

• Why did it happen?

• What will happen?

• What do I want to happen?

ERP CRM 3PtySCMBlack

books

Past

Present

Future

Data

Major Players in BI Market

Improving organizations by providing business

insights to all employees leading to better, faster,

more relevant decisions

Advanced Analytics

Self Service Reporting

End-User Analysis

Business Performance Management

Operational Applications

Microsoft Business Intelligence Vision

BI implementations

– Corporate BI

Commonly design, implement and maintain data warehouses , data models and

integrated reporting and analytics. It require significant time, expertise and money but

total business is not covered .

– Self-service BI (SSBI)

SSBI is to empower analysts so that they can design, customize and maintain their

own BI solutions. SSBI is a combination of corporate BI and extensions to empower

analysts to more fully exploit it .

– Managed BI

Ensuring responsible BI by managing review, approve and audit solutions

Data is delivered in a compliant, responsive and secure way and access permissions

are enforced

Analysis Services(SSAS)

Reporting Services(SSRS)

Integration Services(SSIS)

Master Data Services(MDS)

SharePoint

Collaboration

Excel Workbooks

PowerPivotApplications

SharePointDashboards &

Scorecards

Microsoft Business Intelligence Components

DQS

ERP/CRM DB Cloud Born Data Social Network

Step 1 : Business Analysis

Step 2 : SSIS

Different Source of Data (RDBMS, FTP, Web Services, XML, CSV, EXCEL, etc.)

DQS (Data Quality Services) Integration, cleansing, profiling

MDS (Master Data Service ) Centrally managing organizational master data

ETL (Extraction, Transformation and Loading) framework

Step 3 : SSAS

Create an OLAP multi-dimensional structure making data available for analytics and reporting

SSAS can pre-calculates, summarizes and stores the data in a highly compressed format

Reporting is provided by data through SSAS cubes

Step 4 : SSRS

SSRS (SQL Server Reporting Services) allows creating formatted and interactive reports

Step 5 :

PowerPivot, Power View, Excel services provide rapid data exploration, visualization, and

presentation experience for users . It allows users to interrogate the data from various aspects

by using charts, graphs, drill-down paths etc.

Excel and PowerPivot services can be used for deploying Excel or PowerPivot to SharePoint in

order to make it available to other people, turning Personal BI into Organizational BI.

Microsoft Business Intelligence Road Map

“…was designed specifically to be a central repository for all data in a company disparate data from transactional systems”

Data Warehouse

Characteristics DW

DW is a relational database that is designed for query and analysis

Ship and integrate data from different sources to the analyst

Contains data derived from transaction, internal-external data & archived data

But it’s not a copy of a source database

High query performance

Analysis queries place extra load on transactional systems

Query optimization is hard to do well

Queries not visible outside warehouse

Local processing at sources unaffected

Can operate when sources unavailable

Can query data not stored in a DBMS

Summarized and Extremal data at warehouse

Advantages of Warehousing

Data is kept in a

specific business

line wise.

Before enter into warehouse

Data is processed

(cleansed and transformed)

DW Architecture

Warehouse

Data Marts

Users query

the data

warehouse

Data WarehouseCorporate/Enterprise-wide

Union of all data marts

Organized on E-R model*

Data MartDepartmental

Single business process

Star-join*

DW vs. Data Mart

Transactional Databases vs. Data warehouse

1. ER modeling is used

2. 3NF Normalized

3. Data is spited into tables

4. Hard to visualize

5. Slows down the response time of the query and report

1. Dimensional modeling

2. De-normalized

3. Data is kept in fact and dimension

4. Flexible for user perspective

5. Response time and increases

the performance

Transactional Databases Warehouse Database

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Requisition

RID(PK)

CID (FK)

WID (FK)

UID (FK)

Requestion_Date

Warehouse

WID (PK)

Location

Address

district

WU_Code

User_Profile

UId (PK)

Name

Address

Email

CellNo

Product_Profile

PID (PK)

description

brand

category

Client_Information

CID (PK)

Name

Address

Credit_Limit

Requisition_Details

RID (PK)

RDD (FK)

PID (FK)

promotion_key (FK)

dollars_sold

units_sold

dollars_cost

Entity Relation Diagram

17

TIME

time_key (PK)

SQL_date

day_of_week

month

STORE

store_key (PK)

store_ID

store_name

address

district

floor_type

CLERK

clerk_key (PK)

clerk_id

clerk_name

clerk_grade

PRODUCT

product_key (PK)

SKU

description

brand

category

CUSTOMER

customer_key (PK)

customer_name

purchase_profile

credit_profile

Address

City

country

PROMOTION

promotion_key (PK)

promotion_name

price_type

ad_type

Sales - FACT

time_key (FK)

store_key (FK)

clerk_key (FK)

product_key (FK)

customer_key (FK)

promotion_key (FK)

dollars_sold

units_sold

dollars_cost

DIMENSONAL MODEL

Federated Databases vs. Data warehouse

Data warehouse

Create a copy of all the data and Execute queries against the copy

Federated database

Pull data from source systems as needed to answer queries

Data Warehouse Federated Database

Query

Answer

Query

Extraction Rewritten Queries

Answer

Source

Systems

Warehouse

Mediator

Data Quality problems

Name Address City House

No

DoB State Country

Ahsan CDA Avenue CTG 181/1 05/11/1978 BD

Kabir RB Avn CTG 41/6 23/04/1991 DHK Bangladesh

Name Address City House

No

DoB State Country

Ahsan CDA Avenue CTG 181/1 05/11/1978 CT Bangladesh

Kabir RB Avenue DHK 41/6 23/04/1991 DHK Bangladesh

Before

After

Indication : Completeness Accuracy Conformity Consistency

Data Quality Issues

Data Quality Issue Sample Data Problem

Standard Are data elements consistently

defined and understood ?

Gender code = M, F, U in one system and Gender

code = 0, 1, 2 in another system

Complete Is all necessary data present ? 20% of customers’ last name is blank,

50% of zip-codes are 99999

Accurate Does the data accurately represent

reality or a verifiable source?

A Supplier is listed as ‘Active’ but went out of

business six years ago

Valid Do data values fall within acceptable

ranges?

Salary values should be between

60,000-120,000

Unique Data appears several times Both John Ryan and Jack Ryan appear in the

system – are they the same person?

Data Quality Services (DQS)

Data Quality Services (DQS) is a Knowledge-Driven

data quality solution, enabling to easily improve the

quality of their data

DW Design Consideration

Simplicity

Users should understand the design

Data model should match users’ conceptual model

Queries should be easy and intuitive to write

Expressiveness

Include enough information to answer all important queries

Include all relevant data (without irrelevant data)

Performance

An efficient physical design should be possible

Component of Data Warehousing

DW consists of Fact tables and dimensions. The relationship between a Fact table and

dimensions are based on the foreign key and primary key.

Facts are numeric measurements or additive value that represent a specific business aspect or activity.

Examples :

Unit Cost,

Sale Amount,

Quantity Sold

Salary Amount

Purchase amount

Dimension has a primary key, which is called the surrogate key. The primary key of the source system will be stored in the dimension table as the business key

Dimension tables are tables that contain descriptive information. Dimension table contains a list of columns

Example :

Incase of Product

Product Name

Origin

Category

Manufacturer Date

Sales Date

The Fact table is a table with foreign keys pointing to surrogate keys of the dimension tables

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TIME

time_key (PK)

SQL_date

day_of_week

month

STORE

store_key (PK)

store_ID

store_name

address

district

floor_type

CLERK

clerk_key (PK)

clerk_id

clerk_name

clerk_grade

PRODUCT

product_key (PK)

SKU

description

brand

category

CUSTOMER

customer_key (PK)

customer_name

purchase_profile

credit_profile

Address

City

country

PROMOTION

promotion_key (PK)

promotion_name

price_type

ad_type

Sales - FACT

time_key (FK)

store_key (FK)

clerk_key (FK)

product_key (FK)

customer_key (FK)

promotion_key (FK)

dollars_sold

units_sold

dollars_cost

Dimensional Modeling

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The diagram resembles a star

Center of the star consists of one fact table

Points of the star are the dimension tables

Optimizes performance by keeping queries simple and

Providing fast response time

Star schema

26Star Schema for the retailer’s DW

Sales

Date

Product Store

Promotion

Fact table

Dimension tables

ONE fact table 4 dimension tables

TIME

time_key (PK)

SQL_date

day_of_week

month

STORE

store_key (PK)

store_ID

store_name

address

district

floor_type

CLERK

clerk_key (PK)

clerk_id

clerk_name

clerk_grade

PRODUCT

product_key (PK)

SKU

description

brand

category

CUSTOMER

customer_key (PK)

customer_name

purchase_profile

credit_profile

Address

City

country

PROMOTION

promotion_key (PK)

promotion_name

price_type

ad_type

Sales - FACT

time_key (FK)

store_key (FK)

clerk_key (FK)

product_key (FK)

customer_key (FK)

promotion_key (FK)

dollars_sold

units_sold

dollars_cost

DIMENSONAL MODEL

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Goals for Logical Design

Simplicity

Users should understand the design

Data model should match users’ conceptual model

Queries should be easy and intuitive to write

Expressiveness

Include enough information to answer all important queries

Include all relevant data (without irrelevant data)

Performance

An efficient physical design should be possible

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Step 1 : Identify business subjects and fields of information

of relevant subjects

Step 2 : Discover entities and attributes and relationships

Step 3 : Identify which information belongs to a central fact table

Step 4 : Which information belongs to its associated dimension tables

Step 5 : Identify cleansing points

Step 6 : Which data need to mange centrally

Step 7 : Define surrogate key and business key

Step 8 : Make ETL Package

Step 9 : Organize data structures on disk

Steps of DW Implementation

Thanks