“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

1. Steps Towards
Business Intelligence
Ahsan Kabir ,MVP

2. What is BI
Business Intelligence is an umbrella term that
includes the applications, infrastructure and
tools, and best practices that enable decision
makers to make proper decisions.

3. • What happened?
• What is happening?
• Why did it happen?
• What will happen?
Past
Present
Future
“Understand the pulse of the
Organization”
Why BI

4. How BI ?

5. Microsoft
BI Technologies

6. What is DW?
“…is designed specifically to be
a central repository for all data
in a company separated from
transactional systems.”
“…is designed to be the source
of analysis and reports.”
“But it’s not a copy of a source
database.”

7. Why DW
• Central Repository
• Reduce extra load
• sources unaffected
• Empower Business Users
• Improve data quality
• Single version of the truth

8. 1) Data volumes,
2) Real-time data,
3) New sources and types of data,
and
4) Cloud-born data
But …..

9. Now
• The data warehouse is unable to
keep up with explosive volumes.
• The data warehouse is falling
behind the velocity of real-time
performance requirements.
• The data warehouse is slower than
desired in adopting a variety of
new data sources, slowing time–to-
value
• The platform costs more, while
performance lags.

10. Planning
1. Analytical and Report
requirement
2. Business Process
3. Prioritization
4. Identify Source Data
5. Dimensional Model
6. Documentation
7. Design Data Warehouse

11. Data Warehouse
vs.
Data Mart
Data Warehouse
Enterprise-wide
Union of all data marts
Data Mart
Departmental or Business line
Single business process

12. Kimball
 Bottom-Up
 Data marts
 Logical data warehouse
 Decentralized
 Quick results, iterative approach
Inmon
 Top-Down:
 Enterprise data model
 Centralized
 Later create data marts
 More upfront work but less redo
Kimball vs. Inmon
Methodology

13. Data Model
OLAP cube /Multidimensional
modeling :
“…is based on the OLAP cube
and is fitted with measures and
dimensions”
In-memory tabular model:
“…is based on a new In-memory
engine for tables “

14. OLAP cube/
Multidimensional
modeling
Fact or measure
“… are numeric and additive values
“
Foreign keys
Dimension
“…Descriptive information”
Surrogate key.
Business key.

15. SSAS Loaded into in-Memory engine called
xVelocity in-memory.
Tabular modeling allows you to create a
table-based model from existing data in
Warehouse and and create a relationship
between models.
Data Analysis Expression (DAX) is an
expression language for SSAS Tabular, which
helps you create calculations and measures
based on existing columns and
relationships.
Tabular Model

16. Schema Design
The layout indicate the relationship
between facts and dimensions is called a
schema.
Star Schema :
For each fact entity join with single level
of dimension entities.
Snowflake Schema :
If there are dimensions with large
numbers of attributes, it might be
necessary to break the dimensions down
into sub dimension entities
Star Schema
Snowflake Schema

17. DEMO

18. Analysis
Services (SSIS)
1. Develop Cubes and
2. Create dimensions and measures.
3. Creating hierarchies
4. MDX queries will be compiled,
parsed, and executed
in the SSAS engine

19. ETL
“…is a program that periodically runs.”
Extract
Fetching data from the source
relational databases, web services, and
SharePoint lists.
Transform
“..Cleansing the data and converting to a
OLAP-friendly data model”
Load
“..loading data into the data warehouse
as fact and dimensions”

20. DEMO

21. Data is kept in a
specific business
line wise.
Before enter into warehouse
Data is processed
(cleansed and transformed)
Warehouse Data Marts
Users query
the data
warehouse
“…staging area is an area where we
fetch data from different sources
exactly as it is into our integrated
database. “
Staging

22. Data Quality
Services
Data quality issues can be divided into the following
categories:
Uniqueness
Validity
Accuracy
Standardization
Completeness
Name Address City House No DoB State Country
Ahsan CDAAvenue CTG 181/1 05/11/1978 BD
Kabir RB Avn CTG 41/6 23/04/1991 DHK Bangladesh
Before
After
Accuracy Consistency Completeness Conformity
Name Address City House
No
DoB Stat
e
Country
Ahsan CDA Avenue CTG 181/1 05/11/1978 CT Bangladesh
Kabir RB Avenue DHK 41/6 23/04/1991 DHK Bangladesh

23. Start DQS
Knowledge Base Management
Knowledge Base Management is
where you can create and manage
Knowledge Base, domains, and
domain rules
Data quality projects
projects apply the Knowledge Base
and matching rules on an existing
dataset and provide results.
Administration
Configuration and administration
tasks can be performed here

24. Components
in DQS
1. Cleansing,
Cleansing is about cleaning data based on a
Knowledge Base and domains.
2. Matching,
Matching would match data based on the
similarity rules and threshold defined in a
Knowledge Base.
3. Monitoring
Monitoring will show the status of records
during the cleansing and matching projects.
4. Profiling.
Profiling will help in creating business rules or
changing the domain rules and Knowledge Base
from what the existing data profiling results are.

25. DEMO

26. Technology
SSDT
“…is the integrated IDE for SSIS, SSRS, and
SSAS. SSDT was formerly known as Business
Intelligence Development Studio (BIDS). “
SSIS
SSIS was released with this name for the first
time in 2005, but prior to that, it was named
Data Transformation Services (DTS). DTS was
available even in SQL Server 2000

27. SSRS
“is a data Visualization tools for
developing and publishing reports”
ReportServer DB
Report definition,
Snapshot,
Execution log etc.
ReportServer TempDB
Session and
Cached information.
Report Server web application
Report Manager web application
Reporting Services Configuration Manager.

28. Master Data
Service (MDS)
“…is data shared across computer systems in
the enterprise.”
“… is the dimension or hierarchy data in
data warehouses and transactional systems”
“… is core business objects shared by
applications across an enterprise
-The processes and technology to produce
and maintain a single clean copy of master
data
Customer
ABC
PQR
XYZ
Country
Europe
Norway
Sweden

29. Features
Domain management
models, entities, attributes, and hierarchies.
Business rules
Data validation is also provided.
Import and export master data
Data cleanup

30. Architecture
SQL Server database for storing data and
metadata.
MDS engine read and write information to
that database by : WebUI and Excel Add-ins.
MDS uses subscription views to export
information from MDS to other systems
Staging mechanism to import data from
other systems, which is called entity-based
staging.

31. DEMO

32. Resources:
1. Microsoft SQL Server Analysis Service (SSAS)
Demo Program: Step By Step: Develop ETL Process using SQL Server Integration Services (SSIS)
https://gallery.technet.microsoft.com/Design-Cube-in-SQL-Server-49ee6e1c
2. Microsoft SQL Server Integration Service (SSIS)
Demo Program: Step By Step: Develop ETL Process using SQL Server Integration Services (SSIS)
https://gallery.technet.microsoft.com/Step-By-step-Creating-a-d0e3e71d/edit?newSession=True
3. Microsoft SQL Server Reporting Service (SSRS)
Demo Program: Step by Step SSRS Report Design Using CUBE
https://gallery.technet.microsoft.com/Step-by-Step-SSRS-Report-8de35ea8
Resources:
To know more about SQL Server 2014
https://www.microsoft.com/en-us/server-cloud/products/sql-server/Resources.aspx
Microsoft SQL Server Data Tools - Business Intelligence for Visual Studio 2013
https://www.microsoft.com/en-us/download/details.aspx?id=42313
Adventure Works 2014 Sample Databases
https://msftdbprodsamples.codeplex.com/releases/view/125550

33. Resources:
Data Warehouse Architecture – Kimball and Inmon methodologies: http://bit.ly/SrzNHy
SQL Server 2012: Multidimensional vs tabular: http://bit.ly/SrzX1x
Data Warehouse vs Data Mart: http://bit.ly/SrAi4p
Fast Track Data Warehouse Reference Guide for SQL Server 2012: http://bit.ly/SrAwsj
Complex reporting off a SSAS cube: http://bit.ly/SrAEYw
Surrogate Keys: http://bit.ly/SrAIrp
Normalizing Your Database: http://bit.ly/SrAHnc
Difference between ETL and ELT: http://bit.ly/SrAKQa
Microsoft’s Data Warehouse offerings: http://bit.ly/xAZy9h
Microsoft SQL Server Reference Architecture and Appliances: http://bit.ly/y7bXY5
Methods for populating a data warehouse: http://bit.ly/SrARuZ
Great white paper: Microsoft EDW Architecture, Guidance and Deployment Best Practices:
http://bit.ly/SrAZug
End-User Microsoft BI Tools – Clearing up the confusion: http://bit.ly/SrBMLT
Microsoft Appliances: http://bit.ly/YQIXzM

34. Thanks