Information and Data - Relevance to Business

Prepared by Sharath Bhujani Oracle India

IASA India

Agenda IASA India

•Evolution: Long Story Short •Data Warehouse: Terms & Concepts •Architecture Overview •OLTP Vs Data Warehouse •Data Modeling •Data Warehouse Challenges

Data Warehousing - Evolution

• Terms, dimensions, and facts were developed way back in 1960s. • The concept of data warehousing dates back to the late 1980s. • Using operational data for decision making became a necessity. • Access to valuable information in the quickest possible time was

key to success. • There was a need for an architectural framework to move data

from operational systems to a decision support environment. • Forefathers: Bill Inmon and Ralph Kimball

Data Warehouse: Definition

“A data warehouse is a subject-oriented, integrated, nonvolatile, and time-variant collection of data in support of management’s decisions.”

— W.H. Inmon

“An enterprise-structured repository of subject-oriented, time-variant, historical data used for information retrieval and decision support. The data warehouse stores atomic and summary data.”

— Oracle’s definition of a data warehouse

Data Warehouse: Terms & Concepts

• Fact • Dimension • Data Mart • Conformed Dimension

Architecture Overview

Metadata repository

Source systems

Staging area

Presentation area

Access tools

OLTP Database vs Data Warehouse

OLTP Database Data Warehouse

Transactional data (current) Data analysis (historical)

Stores detailed data Stores summarized data

Data is dynamic (insert, update) Data is largely static (no updates)

Transactions are repetitive Ad hoc reporting

Application-oriented design Subject-oriented design

Data Warehouse Challenges

• Changing business needs vs changing IT infrastructure • Dealing with unstructured data

Agenda IASA India

•Data Warehousing & Big Data •Big Data Information Architecture •Oracle Integrated Hardware & Software Solution •Change / Evolution

Big Data

Data Warehousing and Big Data

• What is Big Data? • Big Data characteristics: Volume, Velocity, Variety. • Big data and data warehousing share the same basic goals. • Type of data: big data Vs data warehouse • Bringing Big Data into Enterprise Data Warehouse.

Enterprise Unstructured Data Growth

Traditional Information Architecture Approach

Big Data Information Architecture Approach

Data Modeling – Structured Vs Unstructured

Dimensional Modeling - Star Schema

Data Modeling – Structured Vs Unstructured

Key Value ID 172

Name Sony LED TV WXYZ Category 1 TV Category 2 LED TV Model WXYZ Make Sony

A row from ‘Product’ table ID Name Category 1 Category 2 Model Make

172 Sony LED TV WXYZ TV LED TV WXYZ Sony

Dimensional Modeling - Star Schema

Oracle’s Integrated Hardware & Software Solution

Oracle Engineered Systems

Oracle Integrated Software Solution

Oracle Big Data Appliance With the recent introduction of Oracle Big Data Appliance, Oracle became one of the first vendor to offer a complete and integrated engineered solution to address the full spectrum of enterprise big data requirements. Oracle’s Big Data strategy: Evolve your current enterprise data architecture to incorporate big data and deliver business value.

Change / Evolution

Analyzing Data - New Possibilities

Traditional Data Sources – Reporting

New Data Sources - Predicting

How Big Data Can Bring Chance: Insurance Domain

Acquire: • Driving habits, breaking pattern, average driving distance etc.

Organize: • Derive information on your driving habits, breaking pattern

etc.

Analyze: • Analyze derived data with other information such as traffic

conditions & your profile data. Perform risk analysis etc.

Decide: • Decide on the premium i.e. you can have a personalized

insurance plan.

Thank you

• Evolution: Long story short • Data Warehouse Architecture • OLTP Vs Data Warehouse • Data Warehouse Challenges • Big Data Information Architecture • Tools for Big Data • Change / Evolution

Conclusion