A Futuristic Reality: The Big Data Platform

Eric.kavanagh@bloorgroup.com

Twitter Tag: #briefr 7/24/2012

Reveal the essential characteristics of enterprise
software, good and bad

Provide a forum for detailed analysis of today’s
innovative technologies

Give vendors a chance to explain their product to
savvy analysts

Allow audience members to pose serious questions...
and get answers!

Twitter Tag: #briefr

July: Disruption

August: Analytics

September: Integration

October: Database

November: Cloud

December: Innovators


Disruptive Innovation produces an unexpected new
market and value network, and is usually geared
toward a new set of customers.

The goal, of course, is to turn a disruptive
technology into a sustaining technology, ie., one
that overtakes or adequately competes with
incumbent technologies.

Disruptive technologies are game-changers and often
pave the way for major improvements in the way
things get done.


Mr. Krishnan is a recognized expert worldwide in the strategy,
architecture and implementation of high performance data
warehousing solutions and unstructured Data. He is a visionary
data warehouse thought leader and an independent analyst,
writing and speaking at industry leading conferences, user groups
and trade publications. He co-authored “Building the
Unstructured Data Warehouse” along with Bill Inmon. He also has
written eBooks, over 150 articles, viewpoints and case studies in
Big Data, Business Intelligence, Data Warehousing, Data
Warehouse Appliances and High Performance Architectures.

A recognized authority on unstructured data integration, text
mining and text analytics, Mr. Krishnan currently is promoting
the next generation of data warehousing, focusing on Big Data,
Semantic Technologies, Crowdsourcing, Analytics, and Platform
Engineering.

Mr.Krishnan presents and speaks at TDWI, DAMA, IRM UK, Wilshire
Conferences, MIT Symposium and other industry conferences. He
leads the Data Warehouse Appliance and Architectures Expert
Channel at BEyeNetwork.com and publishes with
www.beyenetwork.comkkrishnan.


Offers the InfoSphere Platform, a full suite of Big Data analytics solutions
and appliances

InfoSphere BigInsights leverages Hadoop to provide management of
structured and unstructured data

InfoSphere Streams allows user-developed applications to rapidly ingest,
analyze and correlate information in real time; it also performs complex
analytics of heterogeneous data types including text, images, audio,
voice, VoIP, video, web traffic, email, GPS data, financial transaction
data, satellite data, sensors, and any other type of digital information

According to Analyst Merv Adrian, “Stream-based computing will change
the world, and the power of IBM's engine will be a big accelerator of the
change.”


Anjul Bhambhri is the Vice President of Big Data
at IBM. She was previously the Director of IBM
Optim application and data life cycle
management tools. She is a seasoned
professional with over twenty-two years in the
database industry. Over this time, Anjul has held
various engineering and management positions at
IBM, Informix and Sybase. Prior to her assignment
in tools, Anjul spearheaded the development of
XML capabilities in IBM's DB2 database server. She
is a recipient of the YWCA of Silicon Valley's
“Tribute to Women in Technology” award for
2009. Anjul holds a degree in Electrical
Engineering.

You may contact her at bhambhri@us.ibm.com.


July, 2012

A Futuristic Reality: The Big Data Platform
The Briefing Room with Krish Krishnan and IBM

Anjul Bhambhri
VP, Big Data, Information Management, IBM

© 2012 IBM Corporation

Where is big data coming from?
4.6
30 billion RFID billion
tags today
camera
12+ TBs (1.3B in 2005)
phones
of tweet data world wide
every day

100s of
millions
of GPS
data every day
? TBs of

enabled
devices
sold
annually

25+ TBs of 2+
log data every billion
day people on
the Web
76 million smart by end
meters in 2009… 2011
200M by 2014
10

New era of computing requires

Information Radical Extreme
from Everywhere Flexibility Scalability

Volume Velocity Variety

12 terabytes
of Tweets created daily
5 million
trade events per second
100’s
from surveillance cameras
video
feeds

11

More Mission-Critical Apps Ride on Big Data Platforms

• Integrate and manage the full variety,
velocity and volume of data

• Apply advanced analytics to
information in its native form

• Visualize all available data for ad-hoc
analysis and discovery

• Development environment for
building new analytic applications

• Integration and deploy applications
with enterprise grade availability,
manageability, security, and
performance
12 © 2012 IBM Corporation

The new era of analytics delivers value across the enterprise
Network Operations
...identify network bottlenecks in real-
time for faster resolution
Customer Service
Representatives GPS

...offer personalized External Data
price promotions to
different customer Executive Leaders
segments in real-time ...get real-time reports and analysis
based on data inside as well as
outside the enterprise (web, social
media etc.)

Business Analysts
... analyze social media buzz
for the new services/offerings
to gauge initial success and
any course correction needed

Finance
...analyze all Call Detail Records
Business Development (CDRs) to identify and reduce
... find and deliver new revenue leakage due to unbilled
mechanisms to monetize / underbilled CDRs
network traffic and partner Marketing
with upstream content ... analyze subscriber usage pattern
providers in real-time and combine that with the
profile for delivering promotional or
13 retention offers © 2012 IBM Corporation

Vestas optimizes
capital investments
based on 2.5
Petabytes of
information.
• Model the weather to optimize
placement of turbines,
maximizing power generation
and longevity.
• Reduce time required to identify
placement of turbine from weeks
to hours.
• Incorporate 2.5 PB of structured
and semi-structured information
flows. Data volume expected to
grow to 6 PB.


Cisco turns to IBM big
data for intelligent
infrastructure
management
• Optimize building energy consumption
with centralized monitoring
• Automate preventive and corrective
maintenance

Capabilities Utilized:
• Streaming Analytics
• Hadoop System
• Business Intelligence
Applications:
• Log Analytics
• Energy Bill Forecasting
• Energy consumption optimization
• Detection of anomalous usage
• Presence-aware energy mgt.
• Policy enforcement

15

Dublin City Centre Increases
Bus Transportation
Performance
Capabilities Utilized:
Stream Computing
• Public transportation awareness solution
improves on-time performance and provides
real-time bus arrival info to riders
• Continuously analyzes bus location data to
infer traffic conditions and predict arrivals
• Collects, processes, and visualizes location
data of all bus vehicles
• Automatically generates transportation
routes and stop locations

Results:
• Monitoring 600 buses across 150 routes
• Analyzing 50 bus locations per second
• Anticipated to Increase bus ridership

16

Asian telco reduces
billing costs and
improves customer
satisfaction.
Capabilities:
Stream Computing
Analytic Accelerators

Real-time mediation and analysis of
6B CDRs per day
Data processing time reduced from
12 hrs to 1 sec
Hardware cost reduced to 1/8th
Proactively address issues
(e.g. dropped calls) impacting customer
satisfaction.
17

To-the-minute and historical product insight
Jan 1 Monitoring Period Feb 5th

Super Bowl
5pm 6pm 7pm 8pm 9pm 10pm 11pm

Data Set Information extracted
• 1.1B tweets • Buzz and sentiment
• 5.7M blog and forum posts • Gender, Location and Occupation
• 3.5M relevant messages • Fans
• 97K referencing Product_A • Intent to in purchase
• 18K referencing Product B • Specific attributes of products


IBM Big Data Platform

Analytic Applications
BI / Exploration / Functional Industry Predictive Content
BI /
Reporting Visualization App App Analytics Analytics
Reporting


Cost-effectively
analyze
petabytes of
structured and
unstructured
Hadoop
information
System



BI /
Reporting


Analyze
streaming data
and large data
bursts for real-
time insights
Hadoop Stream
System Computing



BI /
Reporting


Hadoop Stream Data
System Computing Warehouse
Deliver deep
insight with
advanced
in-database
analytics and
operational
analytics



BI /
Reporting


Hadoop Stream Data

Govern data
quality and
manage
information
Information Integration & Governance
lifecycle



BI /
Reporting
Gather, extract Speed time to
and explore data value with
using best of analytic and
breed
application
visualization accelerators
Visualization Application Systems
& Discovery Development Management

Accelerators

Hadoop Stream Data

Information Integration & Governance

Cloud | Mobile | Security © 2012 IBM Corporation

New classes of applications for end-users

Streams
Computing

Application Framework
Internet Scale
Computing

Content
Discovery

Analytics

Public/Private
Cloud

24

Accelerators Improve Time to Value

Telecommunications Retail Customer
CDR streaming analytics Intelligence
Deep Network Analytics Customer Behavior and Lifetime
Value Analysis

Finance Social Media Analytics
Streaming options trading Sentiment Analytics, Intent to
purchase
Insurance and banking DW
models

Public transportation Data mining
Real-time monitoring and Streaming statistical analysis
routing optimization

Over 100 sample User Defined Standard Toolkits Industry Data Models
applications Toolkits Banking, Insurance, Telco,
Healthcare, Retail
25

IBM’s big data business partner ecosystem

100
CC&G Partners

Big Data
Business Partner
Signed


Materials
 For additional information including
whitepapers and demos, please visit:
– Bringing Big Data to the Enterprise
– Smarter Computing

 Education:
– Sign up for our 2-day “BigInsights
Essentials”
course in a city near you.
– Learn about our “InfoSphere Streams
Analytics Acceleration” course.
– Learn about Netezza trainings
– Free online education at
bigdatauniversity.com

27

Thank You!


Big Data Platform
A Futuristic Vision

S

Data of a Corporation

Semi-
Structured
Data


So you are about to start the Big
Data Project

Tools

Data
&
Instructions


Workload Isolation Today

Semi-
Structured
Data


Workload Isolation Future

Semi-
Structured
Data

RDBMS
Hadoop
In-Memory
NoSQL
RDBMS / Real-Time
Hadoop / NoSQL In-Memory Streams

Thank You

Krish Krishnan
rkrish1124@yahoo.com
Twitter Handle: @datagenius


What are the Big Data challenges that IBM has experienced in its
customer community?

What are the advantages of the IBM solution compared to pure
play vendors and SI driven solutions?

What are the TCO and ROI from an Executive’s perspective?

Is IBM’s roadmap of looking at a holistic platform comprised of
the different technologies weaved into the architecture?

What is IBM’s future vision for BigInsights?


Is IBM planning to provide Data Science services?

If sharable, are there imminent acquisitions in the play in this
space?

What is IBM’s take on Open Source solutions for Big Data,
excluding Apache Hadoop, Cassandra and NoSQL?

What are the causes for success, or reasons for slow adoption,
of BigInsights?

Where do Netezza, Cognos and SPSS fit into the Big Data stack?


A Futuristic Reality: The Big Data Platform

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