The document provides an overview of business intelligence (BI) and the extract-transform-load (ETL) process. It describes a five-layered BI architecture consisting of data source, ETL, data warehouse, end user, and metadata layers. The data source layer identifies internal and external data sources. The ETL layer extracts, transforms, and loads data. The data warehouse layer stores data in an operational data store, data warehouse, and data marts. The end user layer provides tools for users to analyze data. Metadata is managed across all layers.

1. ETL and
its impact on
Business
Intelligence
Presentedby- Isha Pande(23)

2. Index
Introduction to Business Intelligence
Extract-Transform-Load Process
Business Intelligence Architecture
Five-layered BI Architecture
Summary
Bibilography

3. Introduction to
Business
Intelligence

4. Business Intelligence
The term Business Intelligence (BI) refers to technologies, applications and
practices for the collection, integration, analysis, and presentation of
business information. The purpose of Business Intelligence is to support
better business decision making.
RAW DATA BI MEANINGFULDATA

5. Business Intelligence
Business Intelligence means better decision-making:
Decision making is the important factor for the success of the
organization.
BI helps in the accurate and timely decision making by providing all the
business information.
Many large organization making faster, better and accurate decision
making with the help of Business Intelligence.

6. Extract-Transform-
Load Process

7. ETL (Extract-Transform-Load) Process
> Extract, Transform and Load (ETL) process is important component of BI.
Summary
EXTRACT TRANSFORM LOAD ANALYZE

8. ETL (Extract-Transform-Load) Process
> Extract describes the gathering of data from various sources.
> Transformation is modification to match a desired state.
> Load is its import into a database or data warehouse.
> Data Warehouse is predominantly used to store detailed summary data and
metadata(data about data).
> On the other hand, metadata include information on data themselves. They
facilitate a process of extracting, transforming and loading data through
presenting sources of data in the layout of data warehouses. Metadata are
also used to automate summary data creation and queries management.
> ETL processes take up to 80% of the effort in BI projects. A high
performance is thereby vital to be able to process large amounts of data and
to have a up-to-date database.

9. Business Intelligence
Architecture

10. Business Intelligence Architecture
Existing BI architectures typically feature a unidirectional communication
flow between different components:
• The architectures only features a one-way data flow.
• The limitation of unidirectional data flow is that no adjustment or correction
is allowed on data source even if an error is found.
• If organizations want to correct the error, they have to repeat the entire BI
process especially that of the cleansing procedures again. To overcome
these problems, a two-way data integration flow [14] is suggested whereby
the cleansed data can be sent back to data sources to improve accuracy
and reduce cleansing work.

11. Business Intelligence Architecture
Existing BI architectures lack support on metadata management:
• A good BI architecture should include the layer of metadata.
• A metadata repository is essential for business users to store and
standardize metadata across different systems.
• By having a well-structured metadata, organizations will be able to track
and monitor data flows within their BI environment.
• In addition, they will be able to ensure the consistency of definitions and
descriptions of data that support BI components and thus avoid
misunderstanding and misinterpretation of data

12. Business Intelligence Architecture
Some of the architectures do not include operational data store (ODS)
within the BI environment:
• It is essential to implement ODS to provide current or near current
integrated information that can be accessed or updated directly by users.
• Through this way, decision makers will be able to react faster to changing
business environment and requirements.
• It is necessary to consider data staging area in the ETL (Extract Transform-
Load) process. As most of the data from data source require cleansing and
transformation, it is important to create a temporary storage for data to
reside prior to loading into ODS or data warehouse.

13. Five-layered BI
Architecture

14. Five-layered BI
Architecture

15. Five-layered BI Architecture
Data Source Layer:
• The purpose of this layer is to identify the data source. It is very important
for an or-ganization to know where all required data are from.
• This layer can help not only to increase the reliability of data but also to
improve data organizing.
• There are two types of data sources used by BI applications:
(a) Internal data source (b) External data source

16. Five-layered BI Architecture
Data Source Layer:
(a) Internal data source:
The data which is obtained and maintained by the system inside the
organization such as Customer Relationship Management system (CRM) and
Enterprise Resource Planning system (ERP). Internal data source also
includes all business operation related data such as sales data, customers
data etc.
(b) External data source:
All data which are organized outside the organization are called external
source for example, data from partners, data from suppliers, information
from governmentsand so on.

17. Five-layered BI Architecture
ETL Layer:
• This layer focuses on three main processes:
(a) Extraction
(b) Transformation
(c) Loading

18. Five-layered BI Architecture
ETL Layer:
(a) Extraction:
Extraction is used to read data from different sources both internally or
externally.
(b) Transformation:
Transformation is the process that converts the extracted data from its
original form into the form which is needed.
(c) Loading:
Loading process is defined to put data into data warehouse.
• The purpose of ETL layer is to guarantee that all extracted data are
cleansed and are converted into right form and the data loads into target
warehouse.

19. Five-layered BI Architecture
Data Warehouse Layer:
• This layer provides the storage for data which are in desired and reliable
formats for further use whenever and wherever needed.
• There are three components in data warehouse layer:
(a) ODS (b) Data Warehouse (c) Data Marts

20. Five-layered BI Architecture
Data Warehouse Layer:
(a) ODS: Operational data store (ODS) is used to integrate data and load
them into data ware-house. ODS operatesas a short-term memory since the
data in ODS is updated frequently.
(b) Data Warehouse: Data warehouse is a central storage of structured
data. And all data in data warehouse are ready for analytical use. Unlike
ODS, data warehouse is a long-term memory as the data are permanent.
Both current and history data are stored in data warehouses. It is a subject-
oriented, integrated, time-variant and non-volatile collection of data to
support the managements’ decision making process.
(c) Data Marts: Data mart can be considered as a sub-set of data
warehouse. Usually, data mart consists of data from one single subject area
and it is created directly from the data warehouse.

21. Five-layered BI Architecture
End User Layer:
• The end user layer consists of tools that display information in different
formats to different users. These tools can be grouped hierarchically in a
pyramid shape. As one moves from the bottom to the top of the pyramid,
the degree of comprehensiveness at which data are being processed
increases.

22. Five-layered BI Architecture
Metadata Layer:
• Metadata refers to data about data.
• It describes where data are being used and stored,
the source of data, what changes have been made
to the data, and how one piece of data relates to
other information.
• In this architecture, Metadata management is
applied to all other four layers.
• The purpose of this layer is that by using the well-
structured metadata, organizations is able to track
and monitor the data flow in a BI solution.
• Furthermore, it can help to avoid misun-
derstanding of data.

23. Summary
• BI supports decision making.
• Extract, Transform and Load (ETL) process is important component of BI.
• The Five-layered BI architecture draws a big picture showing how BI
solution works.
• Data are gathered from both internal and external sources. Then these
sources will be extracted, transformed and loaded into data warehouse
for further analytical use.
• End users use different tools to analyze the data to meet the business
requirements or needs.
• All the data are managed by the metadata layer in the architecture.

24. Bibilography
1) Nitin Anand. ETL and its impact on Business Intelligence.
New Delhi, February 2014
2) In Lih Ong, Pei Hwa Siew and Siew Fan Wong. A Five-Layered Business
Intelligence Architecture. Selangor, Malaysia, 2011
3) Mengwei Lu. DiscoveringMicrosoft Self-serviceBI solution: Power BI.
Helsinki, Finland, May 2014

25. Thank You!