trends in content analytics
DESCRIPTION
How are analytics evolving, what do companies aspire to do in analytics, the new buzzwords, big data, cognition, DeepQA - perspectivesTRANSCRIPT
© 2012 IBM Corporation
Trends in analyticsAnders Quitzau - innovationschef
Findwise seminar 29.2.2012
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What is happening out there?
1770 1875 19201830 20101970
Inno
va.o
n
1st Wave
2nd Wave
3rd Wave
4th Wave
5th Wave
6th Wave
Smarter Products§ Instrumented, interconnected, and intelligent
§ Building blocks for a smarter planet
§ Sustainability
The Industrial Revolu2on
Age of Steam
and Railways
Age of Steel, Electricityand Heavy Engineering
Age of Oil, Carsand Mass Produc2on
Ageof IT &Telecom
*Source: “Next Genera2on Green: Tomorrow’s Innova2on Green Business Leaders”, Business Week, Feb 4, 2008
We are ushering in a new wave of innova.on
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© 2012 IBM Corporation
As the planet gets smarter, the information explosion and rapid change create new challenges for CIOs
Projected growth rate for digital data from 2009 to 20201
Size of the world's total digital content in 20101
(1 zettabyte = 1 billion terabytes)
1.2 ZB 44x
Number of devices connected to the Internet by 20202
22 billionThe volume of global Internet traffic expected by 20143
(1 exabyte = 1 million terabytes)
64 EB/mo
Source: 1The Digital Universe - Are You Ready?, John Gantz and David Reinsel, IDC, May 2010 2Internet Connected Devices About to Pass the 5 Billion Milestone, IMS Research press release, August 16, 2010 3Cisco Visual Networking Index (VNI) : Global Mobile Data Traffic Forecast Update, 2009-2014, February 2010
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Predictive analytics, big data, social analytics, virtual assistants on Gartners Hypecycle
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Analytics is a major trend that will impact all business processes and transforming the way decisions are made
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Source: Forrester
From ToOffline / Back office Embedded / Realtime
Detailed Reports Dashboards
Historical Predictive
Structured Unstructured
Behind Firewall Cloud / Mobile
Trends in Analytics
Types of Analytics
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The key will be to leverage information and analytics to enable informed, real-time decisions at the point of impact
Skilled analytics experts
Sense and respond
Instinct and intuition
Back office
Automated
The breakaway approach
Predict and act
Real-time, fact-driven
Everyone
Point of impact
Optimized
Lack of insight
Inefficient access
Inability to predict
Customer relations will require near real-time information and the ability to change on the fly, which will require innovation.
– Energy and Utility CIO
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We are crossing a threshold in our ability to produce information that delivers insight and drives action
Disease treatment Disease prevention
Traffic management Intelligent highways and cars
Financial crime and fraud
Fraud detection and prevention
Speculative oil exploration Informed exploration and targeted drilling
From To
© 2012 IBM Corporation
Big data - analyticsAnders Quitzau - innovationschef
Findwise seminar 29.2.2012
© 2012 IBM Corporation11
Every Smarter Planet Solution has Big Data and Needs Big Analytics
Smarter Planet
Data at RestData in MotionDeep AnalyticsReactive AnalyticsPredictive ModelsReal-time AwarenessDeeper InsightsFaster Decisions
Big Data
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Smarter Computing
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Integrating new approaches such as Big Data will unlock new insights.
Traditional ApproachStructured, analytical, logical
New ApproachCreative, holistic thought, intuition
UnstructuredExploratoryIterativeBrand sentimentProduct strategyMaximum asset utilization
StructuredRepeatable
LinearMonthly sales reports
Profitability analysisCustomer surveys
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How will Content Analytics be like in 2020?
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Up to 10,000 Times larger
Up to 10,000 times faster
Traditional Data Warehouse and Business Intelligence
Dat
a Sc
ale
Dat
a Sc
ale
yr mo wk day hr min sec … ms µs
Exa
Peta
Tera
Giga
Mega
Kilo
Decision FrequencyOccasional Frequent Real-time
Data in Motion
Dat
a at
Res
t
New “Big Data” Brings New Opportunities, Requires New Analytics
Telco Promotions100,000 records/sec, 6B/day10 ms/decision270TB for Deep Analytics
DeepQA 100s GB for Deep Analytics 3 sec/decision
Smart Traffic250K GPS probes/sec630K segments/sec2 ms/decision, 4K vehicles
Homeland Security600,000 records/sec, 50B/day1-2 ms/decision320TB for Deep Analytics
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Big Data Systems Require a Data-centric Architecture for Performance
Data lives on disk and tapeMove data to CPU as neededDeep Storage Hierarchy
Data lives in persistent memoryMany CPU’s surround and useShallow/Flat Storage Hierarchy
Old Compute-centric Model New Data-centric Model
Massive ParallelismPersistent Memory
Largest change in system architecture since the System 360 Huge impact on hardware, systems software, and application design
Flash Phase Change
Manycore FPGA
inputoutput
© 2011 IBM Corporation
Watson - IBMs DeepQA- den største IT-revolution siden transistoren
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From the final match......
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From battling humans at Jeopardy! to transforming business
Tap into a broad, open domain of clues
Do all of that in under three seconds
Be highly precise at finding the answer and put a confidence
behind that answer
Parse complex human language
To compete at Jeopardy!, humans and computers need to:
Watson-like capabilities can now be applied to your business:
Manage massive amounts of data from business operations
and an instrumented world
Do it at the speed of business, in real time
Have a high level of confidence in analytics to make key
business decisions
Deal with structured and unstructured data to gain
meaningful insight into your business
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Watson’s analytics are more than search
§ Web search returns a ranked list of ‘possible’ web pages containing the requested data ‣ Search engine results are based on
popularity and page ranking;‣ User must still analyze results – sift
through a web page -- to find the best answer.
§ Watson’s analytics understand the structure and wording of the question asked‣ Finds a specific answer;‣ Ranks its answer and provides a level of
‘confidence’ that this is correct based on previous experience.
§ Watson answers ‘natural language’ questions‣ May contain puns, slang, jargon and
acronyms that must all be evaluated.
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Announced Aug 18, 2011 - IBM has developed prototype of ‘brainlike’ cognitive chip
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Future Systems – The Learning Paradigm
Training and Learning Engines To Build Models and Define Insight
Hypothesis Engines To Understand and Plan Actions
Policy Engine Business, Legal
and Ethical Rules
Verification Engines (e.g. Simulations)
Active Learning
(Natural Interfaces)
Outcome Engine Actuation and Validation
Society Nature Institutions Archives
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Year 2020?
© 2012 IBM Corporation23
Prepare yourself and your organisation for 6.th wave!