Martin Wildberger Presentation

© 2011 IBM Corporation

IBM Big Data and Integration Portfolio Overview

Bringing Big Data to the Enterprise

Martin Wildberger

2 © 2011 IBM Corporation

Martin WildbergerVice President, Information Management DevelopmentIBM Software Group


A Big Data Platform Addresses Big Data Use Cases …

Identify criminals and threats from disparate video, audio, and data feeds

Make risk decisions based on real-time transactional data

Predict weather patterns to plan optimal wind turbine usage, and optimize capital expenditure on asset placement

Detect life-threatening conditions at hospitals in time to intervene

Multi-channel customer sentiment and experience a analysis

Big Data Platform


…But Can’t Do It Alone

� Big Data will be a permanent part of your information architecture

� It cannot be a silo – it must be fully integrated in order to leverage its value

� It must be easy to deploy and integrate

What does Big Data mean for your Information Architecture?

Enterprise Integration

Big Data PlatformData Warehouse

Traditional Sources New Sources


IBM’s Big Data Platform Vision

Big Data Enterprise EnginesBig Data Enterprise Engines

IBM Big Data Solutions

Internet Scale AnalyticsStreaming Analytics

Developers End Users Administrators

Big Data User EnvironmentsBig Data User Environments

Bringing Big Data to the Enterprise

Client and Partner Solutions

Open Source Foundational Components

Hadoop HBase Pig Lucene Jaql

AG

EN

TS

INT

EG

RA

TIO

NInform

ation Server

Marketing

Warehouse Appliances

Data Warehouse

Database

Content Analytics

Business Analytics

Master Data Mgmt

InfoSphere Warehouse

Netezza

InfoSphere MDM

DB2

Cognos & SPSS

Unica

Data Growth Management

InfoSphere Optim

ECM


One Example - The 360°Multi-Channel Customer Sentime nt Analysis

Master Data Management

Business Processes

Big Data Platform

Call Detail Reports (CDRs)

Call Behavior and Experience Insight

Data Warehouse

Website LogsSocial Media

Streaming Analytics

Internet Scale Analytics

Web Traffic and Social Media Insight

Events and Alerts

Information Integration

Cognos Consumer Insight

Campaign Management


IBM’s Big Data Platform Addresses the Key Requirements

1. Platform for V 3 – Variety, Velocity, Volume� Variety - manage data & content “As Is”

� Handle any velocity - low-latency streams and large volume batch

� Volume - huge volumes of at-rest or streaming data

2. Analytics for V 3

� Analyze Sources in their native format - text, data, rich content

� Analyze all of the data - not just a subset

� Dynamic analytics - automatic adjustments and actions

3. Ease of Use for Developers and Users� Developer UIs, common languages & automatic optimiz ation

� End-user UIs & visualization

4. Enterprise Class� Failure tolerance, Security and Privacy

� Scale Economically

5. Extensive Integration Capabilities� Integrate wide variety of sources

� Leverage enterprise integration technologies

Big Data Platform


1. Platform for V3 – Addresses All 3 V’s

Variety

Optimize capital investments based on 6 Petabytesof information

Volume

Analyze 100k records/ second to address customer satisfaction in real time

Velocity

Analyze telemetry, fuel consumption, schedule and weather patterns to optimize shipping logistics.Big Data Platform


2. Analytics for V3 – Built-for-Purpose, Built-for-Variety

� Leading analytics from IBM Research

� Built-for-purpose to analyze data in its native format

Text

Image & Video

Acoustic

Financial

Times Series

IBM Differentiator – significant research investment in analytics; designed for use with Big Data.

Statistics

Mining

Predictive

Geospatial

Mathematical


3. Ease of Use for Developers and Users

End-user Visualization

Data exploration, crawling, and analytics

Development Environment

Familiar coding and tooling environment, testing, and optimization


4. Enterprise Class

Failure Tolerance

Security & Privacy

Scale Economically

High availability architecture to support hardware or application failure.

Runs on scalable hardware with the ability to dynamically add additional nodes.

Security protection for granular data access control.

Big Data Platform


5. Enterprise Integration

� Trusted Information & Governance

– Companies need to govern what comes in, and the insights that come out

� Data Management– Insights from Big Data

must be incorporated into the warehouse

Big Data PlatformData Warehouse

Enterprise Integration

Traditional Sources New Sources


Building with the Open Source Community

jaqljaqlPIG

ZooKeeper

Leveraging Open Source Innovation …

…and Giving

Back

…Contributing…

Big Data Platform


Announcing: InfoSphere BigInsights v 1.1

Platform for V 3

� Hadoop foundation� Large-scale indexing

Analytics for V 3

� Integrated text analytics

Usability� Development Studio� Admin console (incl. HDFS

explorer)

Enterprise Class� Provisioning, storage, and

advanced security

Integration Capabilities� Integrated install� Connectivity with DB2,

InfoSphere Warehouse and IBM Smart Analytics System.

Deployment Sizes

Ent

erpr

ise

Cla

ss

HadoopUp-and-running

POC Pilot EnterpriseDeployment

Too

ling

Pla

tform

ApacheHadoop

BigInsights Basic Edition

BigInsights Enterprise Edition

Licensed

DB2/RDBMS and Data Warehouse Integration

Provisioning and Advanced Security Job and workflow management

Large Scale IndexingText Analytics

Free download with24 x 7 Web

support


Internet-Scale Analytics in Action

Utilities� Weather impact analysis on

power generation� Smart meter data analysis

E Commerce� Analyze internet behavior

and buying patterns� Digital asset piracy

Multi-channel Integration� Integrated customer behavior

modeling

Transportation� Weather and traffic

impact on logistics and fuel consumption

Call Centers� Voice-to-text mining for

customer behavior understanding

Financial Services� Improved risk decisions� Customer sentiment analysis� AML

IT� Transition log analysis

for multiple transactional systems

Telecommunications� Operations and failure

analysis from device, sensor, and GPS inputs


Announcing: InfoSphere Streams v 2.0

A Platform for V 3

� Runtime optimizations delivering performance improvements. � Improved Java™ support allows shared Java Virtual Machines for

better resource utilization and improved extensibility

Analytics & Usability� New toolkits that delivers more operators and functions out of the

box� Analytics for text, data mining, statistics, among others

Enterprise Class� Improved monitoring capabilities and deployment flexibility to

enhance availability and simplify administration

Integration Capabilities� Connectivity is expanded to support Netezza TwinFin, Microsoft

SQLServer, and MySQL, in addition to DB2, Informix®, solidDB®, and Oracle databases.

InfoSphere Streams


Streaming Analytics in Action

Stock Market� Impact of weather on securities prices� Analyze market data at ultra-low latencies

Fraud Prevention� Detecting multi-party fraud� Real time fraud prevention

e-Science� Space weather prediction� Detection of transient events� Synchrotron atomic research

Transportation� Intelligent traffic

management

Manufacturing� Process control for

microchip fabrication

Natural Systems� Wildfire management� Water management

Telephony� CDR processing� Social analysis� Churn prediction� Geomapping

Other� Smart Grid� Text analysis� Who’s talking to whom?� ERP for commodities� FPGA acceleration

� Real-time multimodal surveillance� Situational awareness� Cyber security detection

Law Enforcement, Defense & Cyber Security

Health & Life Sciences� Neonatal ICU monitoring� Epidemic early warning

system� Remote healthcare

monitoring


Derive a 360 degree view of customer behavior across all channels and Identify opportunities for more targeted marketing activities.

Enable real-time customer analysis that processes billions of records per day.

Support IT and business requirements for sophisticated analytics in real-time, with a focus on churn prevention.

Integration: Integration: Integration:

Process and correlate large volumes of physiological data streams in conjunction with persistent data, such as lab test results to uncover hidden patternsin test results that would otherwise be very difficult to identify.

POS data sourced from existing data warehouse.

Improve analytics performance of warehouse by offloading record processing.

Use data store to define rules for streaming data analytics. Iteratively refine rules.

IBM clients have embraced the Big Data opportunity and are stretching beyond the traditional frontiers of Business Intelligence


Leading Organizations are Partnering with IBM for Big Data

Leveraging the Broader IBM� InfoSphere Information Integration and Governance portfolio� InfoSphere Warehouse, Netezza appliances and IBM Smart Analytics

System� Cognos Consumer Insight – Big Data social media analytics solution� ECM – content management and analytics� Tivoli – integrated service management� Smarter Computing – efficient and innovative IT infrastructure� GBS – Business Analytics and Optimization services

IBM’s Big Data Platform� Broadest platform to bring Big Data to the Enterprise� A Platform for V3 – Analyzing the Variety, Velocity and

Volume of structured and unstructured data

Big Data Platform

Documents

Martin Wildberger Presentation