Big data journey_to_value_v5_john_sing

© 2014 John Sing – All Rights Reserved

Big Data’s Journey to Value

Making Data Actionable

Opening video

John Sing, Executive IT Architect

https://www.youtube.com/watch?v=CxQHwmhJXX4


University of South Florida - Spring 2015

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John Sing 32 years of experience in enterprise servers, storage, and software

– 2015: IBM Product Manager – Spectrum Scale Storage

– 2014: Director of Technology, 4cube – Infrastructure for Tomorrow

– 2009 – 2013: IBM Executive IT Consultant: IT Strategy and Planning, Enterprise Large Scale Storage, Internet Scale Workloads and Data Center Design, Big Data Analytics, HA/DR/BC

– 2002-2008: IBM IT Data Center Strategy, Large Scale Systems, Business Continuity, HA/DR/BC, IBM Storage

– 1998-2001: IBM Storage Subsystems Group – Worldwide Marketing, Technical Support, Product Planner, Product Manager

– Before that: • IBM Hong Kong, IBM China, IBM USA

[email protected]

Follow me on Twitter: http://twitter.com/john_sing

Follow me on Slideshare.net:– http://www.slideshare.net/johnsing1

Blog: – http://johnsing.technology

LinkedIn:– http://www.linkedin.com/in/johnsing

mailto:[email protected]

http://twitter.com/john_sing




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Data + Analytics = Information

Insight

Desired Outcomes

Information + Context =

Insight + Actions =



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You know howmuch data there is…



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You know how to analyze Big DataGoal: Analyze *all* the data real time

Original source: Wikibon.org, “Big Data”, http://wikibon.org/blog/ten-%E2%80%9Cbig-data%E2%80%9D-realities-and-what-they-mean-to-you/

Very large

Looselystructured

Often incomplete

Sampling not strategically competitive

http://wikibon.org/blog/ten-%E2%80%9Cbig-data%E2%80%9D-realities-and-what-they-mean-to-you/



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TimeCom

puti

ng P

ower

Gro

wth

Traditional business “sensemaking” capability

Available datafor observation

ContextEnterpriseAmnesia

What “Big Data” solves:

Chart by: Jeff Jonas/Las Vegas/IBM, Chief Scientist, Context Computing: http://jeffjonas.typepad.com/

http://jeffjonas.typepad.com/



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Enterprise Amnesia, definition

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



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TimeCom

puti

ng P

ower

Gro

wth

Traditional business “sensemaking” capability

Available datafor observation

ContextEnterpriseAmnesia

Enterprise Amnesia examples…..

Chart by: Jeff Jonas/Las Vegas/IBM, Chief Scientist, Context Computing: http://jeffjonas.typepad.com/

http://jeffjonas.typepad.com/



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TimeCom

puti

ng P

ower

Gro

wth

Data + Analytics = “Information”

Traditional business“sensemaking”

Available ObservationSpace

Context Big Dataacquisition

= New, Useful InformationAdd: Analytics

What comes after “Information”?



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Insight

Desired Outcomes


Insight + Actions =



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Context

More about Jeff Jonas, IBM Chief Scientist, Context Computing: http://bit.ly/1g3z9ZQ

Jeff Jonas, IBM Chief Scientist

Context Computing

https://www.youtube.com/watch?v=ipxRA7ira4c

http://news.nationalgeographic.com/news/innovators/2014/05/140505-jeff-jonas-big-data-gambling-computers-technology-ibm/

https://www.youtube.com/watch?v=ipxRA7ira4c



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Here’s morefrom IBM’s

Jeff Jonas

about “Context”:

Tubechop: http://www.tubechop.com/watch/5634618

http://www.tubechop.com/watch/5634618

http://www.tubechop.com/watch/5634618



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No Context

[email protected]



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Context, definition

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



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Information in Context … = Insights

Top 200Customer

Job Applicant

IdentityThief

CriminalInvestigation

[email protected]



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The Puzzle Metaphor: what we mean by “Context”

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



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Here’s a “context” example…….. “Puzzling”

270 pieces90%

200 pieces66%

150 pieces50%

6 pieces2%(pure noise)

30 pieces10% (duplicates)



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First Discovery



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More Data Finds Data



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Duplicates in Front Of Your Eyes



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First Duplicate Found Here



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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!”



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150 pieces50%



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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.”



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Context Accumulates….. Into “Insights”

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

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!



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WhatCan you See in

Context

now?



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Big Data [in context] = Insights.

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



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Insight

Desired Outcomes


Insight + Actions =



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Now that I create Insights..…. how do I take Action?

Chart in public domain: IEEE Massive File Storage presentation, author: Bill Kramer, NCSA: http://storageconference.org/2010/Presentations/MSST/1.Kramer.pdf:

http://storageconference.org/2010/Presentations/MSST/1.Kramer.pdf



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Answer: build actionable systems that use the insights

Chart in public domain: IEEE Massive File Storage presentation, author: Bill Kramer, NCSA: http://storageconference.org/2010/Presentations/MSST/1.Kramer.pdf:

n d

ActionableSystems



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Projected traffic Insights

•10 minute-ahead volume forecast (blue) vs. actual value (black)

•10 minute-ahead speed forecast (blue) vs. actual value (black).

Black line: actions via signals = desired outcome Stockholm: http://www.youtube.com/watch?v=rfMylzF4lv8

Actionable traffic signals

Blue line: analytics prediction 10 minutes in advance



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Insights based on crime actions: where to deploy of officers

Blue CRUSH predictive analysis for officer deployment & risk management generated easy-to-read crime maps every four hours Richmond, VA: Violent crime decreased in the first year by 32%, another 40% thereafter,

moving Richmond from #5 on the list of the most dangerous US cities to #99

Memphis Blue CRUSH MapMemphis Blue CRUSH Map

Police videos: http://www.youtube.com/watch?v=8SJQtn4RO7I

Playvideo

https://www.youtube.com/watch?v=_xsffIAHY3I



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Local Applications: Big Data’s Journey to Value


Insight

Desired Outcomes


Insight + Actions =



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Local examples



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Local examples



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Local examples



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Quiz: in following Futuristic videosee if you can identify:


Information + Context = Insight

Insight + Actions = Desired Outcomes



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Cognitive Video

The Future – Creating Actionable Big Data



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Final Quiz: Big Data’s Journey to Value


Insight

Desired Outcomes


Insight + Actions =



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Thank YouMerci

Grazie

ObrigadoDankeJapanese

Hebrew

English

French

Russian

German

Italian

Brazilian PortugueseArabic

Traditional Chinese

Simplified Chinese

Hindi

Tamil Korean

Thai

TesekkurlerTurkish



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Does Corning understand“Actionable” data?

Predicting the future …..https://www.youtube.com/watch?v=PfgmlVxLC9w