For every industry and business, there's a wise Big Data Journey to Value formula that goes like this: * (Big) Data + Analytics = Information * Information + Context = Insight * Insight + Actionable Systems = Desired Outcomes You'll notice that in the game of Big Data, the accumulation of (valuable new technology-driven) data (about your customers, about your internal efficiencies) is only the first step. Innovation, business competitive advantage and results, only comes out of Big Data if you have *Actionable Systems* that can create Desired Outcomes. Make sure you add this 2nd step to any of your conversations around business competitiveness in today's hyper-rich innovation, data-rich, internet-scale environment.

1. © 2014 John Sing – All Rights Reserved
Big Data’s Journey to Value
Making Data Actionable
Opening video
John Sing, Executive IT Architect

2. © 2015 John Sing – All Rights Reserved
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
 john@johnsing.us
 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

3. © 2015 John Sing – All Rights Reserved
University of South Florida - Spring 2015
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Big Data’s Journey to Value
Data + Analytics = Information
Insight
Desired Outcomes
Information + Context =
Insight + Actions =

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University of South Florida - Spring 2015
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You know how
much data
there is…

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University of South Florida - Spring 2015
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You know how to analyze Big Data Goal: 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
Loosely
structured
Often
incomplete
Sampling not
strategically
competitive

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University of South Florida - Spring 2015
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Time
ComputingPowerGrowth
Traditional business
“sensemaking” capability
Available data
for
observation
ContextEnterprise
Amnesia
What “Big Data” solves:
Chart by: Jeff Jonas/Las Vegas/IBM, Chief Scientist, Context Computing: http://jeffjonas.typepad.com/

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University of South Florida - Spring 2015
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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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University of South Florida - Spring 2015
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Time
ComputingPowerGrowth
Traditional business
“sensemaking” capability
Available data
for
observation
ContextEnterprise
Amnesia
Enterprise Amnesia examples…..
Chart by: Jeff Jonas/Las Vegas/IBM, Chief Scientist, Context Computing: http://jeffjonas.typepad.com/

9. © 2015 John Sing – All Rights Reserved
University of South Florida - Spring 2015
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Time
ComputingPowerGrowth
Data + Analytics = “Information”
Traditional business
“sensemaking”
Available
Observation
Space
Context Big Data
acquisition
= New, Useful
InformationAdd: Analytics
What comes after “Information”?

10. © 2015 John Sing – All Rights Reserved
University of South Florida - Spring 2015
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Big Data’s Journey to Value
Data + Analytics = Information
Insight
Desired Outcomes
Information + Context =
Insight + Actions =

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University of South Florida - Spring 2015
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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

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University of South Florida - Spring 2015
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Here’s more
from IBM’s
Jeff Jonas
about “Context”:
Tubechop: http://www.tubechop.com/watch/5634618

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University of South Florida - Spring 2015
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No Context
scrila34@msn.com

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University of South Florida - Spring 2015
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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 200
Customer
Job
Applicant
Identity
Thief
Criminal
Investigation
scrila34@msn.com

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University of South Florida - Spring 2015
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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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University of South Florida - Spring 2015
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Here’s a “context” example…….. “Puzzling”
270 pieces
90%
200 pieces
66%
150 pieces
50%
6 pieces
2%
(pure noise)
30 pieces
10%
(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 pieces
50%

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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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University of South Florida - Spring 2015
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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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University of South Florida - Spring 2015
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What
Can 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

34. © 2015 John Sing – All Rights Reserved
University of South Florida - Spring 2015
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Big Data’s Journey to Value
Data + Analytics = Information
Insight
Desired Outcomes
Information + Context =
Insight + Actions =

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University of South Florida - Spring 2015
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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:

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University of South Florida - Spring 2015
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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
Actionable
Systems

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University of South Florida - Spring 2015
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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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University of South Florida - Spring 2015
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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
Play
video
https://www.youtube.com/watch?v=_xsffIAHY3I

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University of South Florida - Spring 2015
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Local Applications: Big Data’s Journey to Value
Data + Analytics = Information
Insight
Desired Outcomes
Information + Context =
Insight + Actions =

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University of South Florida - Spring 2015
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Local examples

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Local examples

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Local examples

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University of South Florida - Spring 2015
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Quiz: in following Futuristic video
see if you can identify:
Data + Analytics = Information
Information + Context = Insight
Insight + Actions = Desired Outcomes

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University of South Florida - Spring 2015
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 Cognitive
Video
The Future – Creating Actionable Big Data

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University of South Florida - Spring 2015
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Final Quiz: Big Data’s Journey to Value
Data + Analytics = Information
Insight
Desired Outcomes
Information + Context =
Insight + Actions =

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University of South Florida - Spring 2015
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Thank You
Merci
Grazie
ObrigadoDanke
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University of South Florida - Spring 2015
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Does Corning understand
“Actionable” data?
Predicting the future …..
https://www.youtube.com/watch?v=PfgmlVxLC9w