Business Intelligence

Learning Outcomes: 
After learning the course the students should be able to
• Design and implement OLTP, OLAP and Warehouse concepts
• Design and develop Data Warehouse using Various Schemas & Dimensional modelling
• Use the ETL concepts, tools and techniques to perform Extraction, Transformation, and Loading of data
• Report the usable data by using various reporting concepts, techniques/tools, and use charts, tables for reporting in BI
• Use Analytics concepts like data mining, Exploratory and statistical techniques for predictive analysis in Business Intelligence
• Demonstrate application of concepts in BI
Syllabus: 
Unit NoTopics
1

Introduction to Business Intelligence

Business Intelligence and Information Exploitation: Improving Decision making process, Why Business Intelligence, Advantage of Information Asset, Actionable Intelligence.

Value of B.I. : Value Drivers and Information Use, Performance Metrics and Key Performance Indicators, Horizontal&Vertical Use Case s for Business Intelligence, 

2

Business Intelligence Roadmap & Environment

A Business Intelligence Strategy, The Business Intelligence and Analytics Spectrum, The Business Intelligence Roadmap- Planning the B.I. Plan, Aspects of a Business Intelligence and Analytics, The Organizational B.I.Framework,  Services and System Evolution,  Management Issues,

Business Processes and Information Flow: Analytical Information Needs and Information Flows,  Information Processing and Information Flow,  Information Processing and Information Flow,  Modelling Frameworks, 

3

The Data Warehouse Environment

The Structure of the Data Warehouse, Subject Orientation, Granularity, Partitioning as a Design Approach, Structuring Data in the Data Warehouse, Data Homogeneity

/ Heterogeneity, Reporting and the Architected Environment, 

4

The Data Warehouse and Design

Beginning with Operational Data, Data/Process Models and the Architected Environment, The Data Warehouse and Data Models, dimensional modelling process, Star schema, Snow flake schema, and Fact Constellation schema, Grain of dimensional model, transactions, Recurring Snapshots, Accumulating Snapshots, Dimensions (SCD types, conformed dimensions), Facts (additive, semi-additive, non-additive), Hierarchy in dimensions, parent child relationships, Many-Many Dimensional relationship, Multi Valued Dimensions and Dimension Attributes, Distributed Data Warehouse, External/Unstructured Data and the Data Warehouse.

5

ETL

ETL Data Structures & Architecture. Extracting process- Building the Logical Data Map, Integrating Heterogeneous Data Sources, Mainframe Sources, Flat Files, XML Sources, Web Log Sources. Cleaning and Conforming-  : Design Objectives & cleaning deliverables, Data Quality, Data profiling, Data enrichment, data duplication,

 concept and Change data capture, Transformation concept, lookups, time lag, formats, consistency, Loading concept, Initial and Incremental loading, late arriving facts, ETL &  Metadata-  Defining Metadata,  Business Metadata,  Technical Metadata,  ETL-Generated Metadata,

6

REPORTING

Metadata Layer, Presentation Layer, Data Layer, Use of different layers and overall Reporting architecture, Various report elements such as Charts, Tables, prompts Data aggregation: Table based, Materialized views, Query rewrite, OLAP, MOLAP, Dashboards, Ad-hoc reports, interactivity in analysis (drill down, drill up), Security: report level, data level (row, column), Scheduling 

7

Self Learning Module

Big data like HIVE, PIG and DW appliances like Netezza, Teradata, Smart Change data capture using log based techniques, Real time BI, Operational BI, Embedded BI, Agile BI, BI on cloud, BI applications (Case study on BI tools like: QlikView, Pentaho, Tableau, MyReport, Spotfire, OR any other BI tool)

8

Text and Web Mining

Concepts, natural language processing, text mining applications, process, tools

Text Books: 
Name : 
Business Intelligence: The Savvy Manager’s Guide
Author: 
David Loshin
Publication: 
MK Publication
Edition: 
2nd
Name : 
Building the Data Warehouse
Author: 
W. H. Inmon
Publication: 
Wiley Computer
Edition: 
3rd
Name : 
The Data Warehouse ETL Toolkit
Author: 
Ralph Kimball Joe Caserta
Publication: 
Wiley Publishing
Reference Books: 
Name: 
Building the Data Warehouse
Author: 
William Inmon
Publication: 
Wiley publication
Edition: 
4th
Name: 
Decision Support And Data Warehouse Systems
Author: 
Efrem G. Mallach
Publication: 
Tata McGraw-Hill Education
Edition: 
1st
Name: 
Business Intelligence
Author: 
Efraim Turban
Ramesh Sharda
DursunDelen
David King
Publication: 
Prentice Hall
Name: 
Business Modeling and Data Mining
Author: 
Dorian Pyle
Publication: 
Elsevier Publication MK
Syllabus PDF: 
AttachmentSize
PDF icon BI.pdf290.37 KB
branch: 
BDA
Course: 
2018
Stream: 
B.Tech