IBM IOD Conference 2011 Opening Keynote Deck

Post on 02-Dec-2014

3.571 views 0 download

Tags:

description

Opening keynote at the IBM Information On Demand (IOD) Conference, Las Vegas, 2011. 12,000 peeps.

Transcript of IBM IOD Conference 2011 Opening Keynote Deck

© 2011 IBM Corporation

Big Data. Deep Analytics. New Physics.The Journey from Enterprise Amnesia

to Enterprise Intelligence

Jeff Jonas, IBM Distinguished EngineerChief Scientist, IBM Entity Analytics

Email: jeffjonas@us.ibm.comBlog: www.jeffjonas.typepad.com

Twitter: http://www.twitter.com/jeffjonas

© 2011 IBM Corporation

State of the Union:Enterprise Amnesia

© 2011 IBM Corporation

Amnesia, definition

A defect in memory, especially resulting from brain damage.

© 2011 IBM Corporation

Enterprise Amnesia, definition

A defect in memory, resulting in missed opportunity, wasted resources, lower revenues, unnecessary fraud losses, and other bad news.

© 2011 IBM Corporation

Time

Trend: Organizations Are Getting Dumber

Sensemaking Algorithms

Available Observation

Space

Structured data Unstructured data Social media Cyber audit logs Geospatial data

Com

pu

tin

g P

ow

er

Gro

wth

EnterpriseAmnesia

© 2011 IBM Corporation

Data Volumes Exploding

“Every two days now we create as much information as we did from the dawn of civilization up until 2003.”

~ Eric Schmidt, CEO Google

© 2011 IBM Corporation

WHY?

Trend: Organizations Are Getting Dumber

Time

Sensemaking Algorithms

Available Observation

Space

Com

pu

tin

g P

ow

er

Gro

wth

© 2011 IBM Corporation

Algorithms at Dead End.

You Can’t Squeeze Knowledge

Out of a Pixel.

© 2011 IBM Corporation

No Context

scrila34@msn.com

© 2011 IBM Corporation

Context, definition

Better understanding something by taking into account the things around it.

© 2011 IBM Corporation

Information in Context … and Accumulating

Top 200Customer

Job Applicant

IdentityThief

CriminalInvestigation

scrila34@msn.com

© 2011 IBM Corporation

The Puzzle Metaphor

Imagine an ever-growing pile of puzzle pieces of varying sizes, shapes and colors

What it represents is unknown – there is no picture on hand

Is it one puzzle, 15 puzzles, or 1,500 different puzzles?

Some pieces are duplicates, missing, incomplete, low quality, or have been misinterpreted

Some pieces may even be professionally fabricated lies

Until you take the pieces to the table and attempt assembly, you don’t know what you are dealing with

© 2011 IBM Corporation

Puzzling

270 pieces90%

200 pieces66%

150 pieces50%

6 pieces2%(pure noise)

30 pieces10% (duplicates)

© 2011 IBM Corporation

© 2011 IBM Corporation

© 2011 IBM Corporation

First Discovery

© 2011 IBM Corporation

More Data Finds Data

© 2011 IBM Corporation

Duplicates in Front Of Your Eyes

© 2011 IBM Corporation

First Duplicate Found Here

© 2011 IBM Corporation

© 2011 IBM Corporation

© 2011 IBM Corporation

Incremental Context – Incremental Discovery

6:40pm START

22min “Hey, this one is a duplicate!”

35min “I think some pieces are missing.”

37min “Looks like a bunch of hillbillies on a porch.”

44min “Hillbillies, playing guitars, sitting on a porch, near a barber sign … and a banjo!”

© 2011 IBM Corporation

150 pieces50%

© 2011 IBM Corporation

Incremental Context – Incremental Discovery

47min “We should take the sky and grass off the table.”

2hr “Let’s switch sides, and see if we can make sense

of this from different perspectives.”

2hr10m “Wait, there are three … no, four puzzles.”

2hr17m “We need a bigger table.”

2hr18m “I think you threw in a few random pieces.”

© 2011 IBM Corporation

© 2011 IBM Corporation

© 2011 IBM Corporation

© 2011 IBM Corporation

How Context Accumulates

With each new observation … one of three assertions are made: 1) Un-associated; 2) placed near like neighbors; or 3) connected

Must favor the false negative

New observations sometimes reverse earlier assertions

Some observations produce new discovery

As the working space expands, computational effort increases

Given sufficient observations, there can come a tipping point. Thereafter, confidence improves while computational effort decreases!

© 2011 IBM Corporation

Big Data [in context]. New Physics.

More data: better the predictions– Lower false positives

– Lower false negatives

More data: bad data … good– Suddenly glad your data was not perfect

More data: less compute

© 2011 IBM Corporation

Enterprise IntelligenceOne Plausible Journey

Enterprise IntelligenceHow to Get From Here to There

© 2011 IBM Corporation

ObservationSpace

Sense and Respond

What you know

New Observations

© 2011 IBM Corporation

ObservationSpace

Decide

?Relevance

Finds the Sensor(<200ms)

Data Finds Data

Sense and Respond

© 2011 IBM Corporation

Explore and Reflect

ObservationSpace

Decide

?DirectedAttention

Relevance Find You

DeepReflection

CuratedData

PatternDiscovery

RelevanceFinds the Sensor

(<200ms)

Data Finds Data

Sense and Respond

© 2011 IBM Corporation

ObservationSpace

Decide

?DirectedAttentionNEW

INTERESTS

DeepReflection

CuratedData

PatternDiscovery

RelevanceFinds the Sensor

(<200ms)

Data Finds Data

Explore and ReflectSense and Respond

© 2011 IBM Corporation

ObservationSpace

Decide

?

DeepReflection

CuratedData

PatternDiscovery

RelevanceFinds the Sensor

(<200ms)

Data Finds Data

ILog

NetezzaBigInsights

DirectedAttention

Cognos

Explore and ReflectSense and Respond

InfoSphere Streams

NEWINTERESTS

© 2011 IBM Corporation

ObservationSpace

Decide

?DirectedAttentionNEW

INTERESTS

DeepReflection

CuratedData

PatternDiscovery

RelevanceFinds the Sensor

(<200ms)

Data Finds Data

Report and Manage

Explore and ReflectSense and Respond

© 2011 IBM Corporation

Decide

?DirectedAttentionNEW

INTERESTS

CuratedData

PatternDiscovery

RelevanceFinds the Sensor

(<200ms)

Data Finds Data

Info Management Systems

Content ManagementCase Management Data Warehousing

Report and Manage

© 2011 IBM Corporation

ObservationSpace

Decide

?

DeepReflection

CuratedData

PatternDiscovery

RelevanceFinds the Sensor

(<200ms)

Data Finds DataIdentity Insight/Sensemaking

SPSS

ILog

NetezzaBigInsights

DirectedAttention

Cognos

Explore and ReflectSense and Respond

InfoSphere Streams

NEWINTERESTS

© 2011 IBM Corporation

Closing Thoughts

© 2011 IBM Corporation

The most competitive organizations

are going to make sense of what they are observing

fast enough to do something about it

while they are observing it.

© 2011 IBM Corporation

Wish This On The Competition

Time

Sensemaking Algorithms

Available Observation

Space

Com

pu

tin

g P

ow

er

Gro

wth

EnterpriseAmnesia

© 2011 IBM Corporation

The Way Forward: Enterprise Intelligence

Time

Sensemaking Algorithms

Available Observation

Space

Com

pu

tin

g P

ow

er

Gro

wth

Context Accumulation

© 2011 IBM Corporation

Big Data. Deep Analytics. New Physics.The Journey from Enterprise Amnesia

to Enterprise Intelligence

Jeff Jonas, IBM Distinguished EngineerChief Scientist, IBM Entity Analytics

Email: jeffjonas@us.ibm.comBlog: www.jeffjonas.typepad.com

Twitter: http://www.twitter.com/jeffjonas