IntroductionCourse Syllabus

Course text BooksReference Books

What is Data Warehouse ?

To familiarize the students with the concepts of◦ databases, ◦ decision support systems, ◦ data Warehouses

To provide an in-depth insight into their architectural types. Various activities Design, Loading, Extraction Usage of transformed data for various functional areas. The course also provides an insight into another related

area that helps mines useful information from loads of data. Various data mining techniques are examined to assess

their relevance in respective areas of mining information.

Objectives

 

Module I: Data Warehousing in Business◦ Data Warehousing goals and objectives, ◦ Failures of past Decision support systems, ◦ Operational versus Decision support systems, ◦ Warehousing as a viable solution, and definition of data warehousing

Module II: Data Warehouse: The building blocks◦ Defining Features, ◦ Data warehouses and data marts, ◦ Overview of components and metadata in the data warehouse.

  Module III: Data Warehousing Planning & requirements

◦ Key issues is planning data warehouse,◦ Development Phases, ◦ Process flow within a data warehouse, ◦ Dimensional analysis

Module IV: Data warehouses architecture◦ Data warehouse architecture model, ◦ components & framework, ◦ importance of Metadata.

 

Course Contents

  Module V: Data warehouse design

◦ From requirements to data design, ◦ Dimensional Modeling Concepts - Star Schema, Snowflake Schema

  Module VI : OLAP in the Data Warehouse

◦ Data warehouse versus Operational systems, ◦ Need for multidimensional analysis, ◦ major features and functions, ◦ OLAP models,◦ OLAP implementation considerations.

Module VIII: Data Mining Basics & techniques◦ Data Mining definition, ◦ Knowledge discovery process, ◦ OLAP vs. data Mining, ◦ Major Data Mining Techniques, ◦ Data Mining Applications.

Text Books◦ Data warehousing fundamentals, Paulraj

Ponniah, John Wiley & sons, 2005◦ Building the Data Warehouse, W. H. Inmon, John

Wiley & Sons.2, 2004 Reference Books

◦ Data Warehousing in Real world, Sam Anahory and Dennis Murray. Addison Wesley, 2004

◦ Modern Data Warehousing, Mining, and Visualization: Core Concepts, George M. Marakas, Publisher: Prentice Hall, 2002

Books

In modern organization, at least four levels of analytical processing should be supported by information systems

◦ First level: Consists of simple queries and reports against current and historical data

◦ Second level: Goes deeper and requires the ability to do “what if” processing across data store dimensions

Four Levels of Analytical Processing

◦ Third level: Needs to step back and analyze what has previously occurred to bring about the current stat of the data

◦ Fourth level: Analyzes what has happened in the past and what needs to be done in the future in order to bring some specific change

Four Levels of Analytical Processing

Since 1970s, organizations gained competitive advantage through systems that automate business processes to offer more efficient and cost-effective services to the customer.

This resulted in accumulation of growing amounts of data in operational databases.

The Evolution of Data Warehousing

Organizations now focus on ways to use operational data to support decision-making, as a means of gaining competitive advantage.

However, operational systems were never designed to support such business activities.

Businesses typically have numerous operational systems with overlapping and sometimes contradictory definitions.

The Evolution of Data Warehousing

Organizations need to turn their archives of data into a source of knowledge, so that a single integrated / consolidated view of the organization’s data is presented to the user.

A data warehouse was deemed the solution to meet the requirements of a system capable of supporting decision-making, receiving data from multiple operational data sources.

The Evolution of Data Warehousing

Characteristics:1. A central database that is loaded from

multiple operational databases for the Purpose of end-user access and decision Support.

2. A data warehouse differs from an operational system in that the data it contains is normally static and updated in a scheduled manner through massive loading procedures.

3. A data warehouse is developed to accommodate random, ad hoc queries and to allow users to ‘drill down’ to minute levels of detail.

What is a Data Warehouse?

Data Warehousing is a decision support system. It has the Following characteristics:

Enable workers to make better and wiser decisions

A data warehouse is specifically developed to allow users to explore data in an unlimited number of ways, accommodating essentially any query a manager could dream up and providing access to the data sources that are behind the results. For example, information gleaned from a data warehouse can change pricing information.

The Benefits of Data Warehouse

Identify hidden business opportunities

A data warehouse performs a second, and very valuable function by searching data for trends and abnormalities which users may not know to look for.

For example: Assisting companies in spotting

sales trends, and detecting erroneous or fraudulent billings.

The Benefits of Data Warehouse

Bending with the customer

A data warehouse can help companies by really understanding who their customers are and what services they are using.

For example, by collecting and analyzing

internet portal click stream data, companies are able to build extensive user profiles to boost profits through sales channel.

The Benefits of Data Warehouse

Precision Marketing

A data warehouse can aid in detecting segments of the marketplace (geographically and demographically) which remain untapped, and help show the best way to reach out to these potential customers (rapid response to market and technology trends).

The Benefits of Data Warehouse