Smarter planet and mega trends presentation 2012
•Als PPT, PDF herunterladen•
1 gefällt mir•200 views
This presentation was held at the Nordic GTS SO Client visit to IBM Innovation Center in La Gaude in 2012
Empfohlen
Empfohlen
Weitere ähnliche Inhalte
Was ist angesagt?
Was ist angesagt? (20)
Ähnlich wie Smarter planet and mega trends presentation 2012
Ähnlich wie Smarter planet and mega trends presentation 2012 (20)
Kürzlich hochgeladen
Kürzlich hochgeladen (20)
Smarter planet and mega trends presentation 2012
- 1. © 2012 IBM Corporation Smarter Planet megatrends - Jørgen Floes, Chief Architect Nordic SO Delivery
- 2. Smarter Planet megatrends Smarter Planet megatrends - © 2012 IBM Corporation The IT world is changing - driven by shifts in market dynamics and client needs Analytics2 Through 2015, more than 90% of business leaders contend information is a strategic asset, yet fewer than 10% will quantify its economic value Big Data1 Through 2015, more than 85 percent of Fortune 500 organizations will fail to effectively exploit big data for competitive advantage Mobile Enterprise5 66% of CIOs ranked mobility as a top investment priority in 2012 Social Business3 By 2014, 20% of business users will replace email as the primary interpersonal communications with social networking Security7 Through 2016, the financial impact of cybercrime will grow 10 percent per year, due to the continuing discovery of new vulnerabilities Cloud6 63% of companies already are using (Cloud) or expect to use it in 2012, up from 49% last year Growth Markets8 By 2015, IDC expects emerging markets to generate over 33% of all IT spending Smarter Planet4 IBM Smarter industry solutions will grow 5x faster than total solutions Customer Sets Software Technology Services Hardware Business Services
- 3. Smarter Planet megatrends Smarter Planet megatrends - © 2012 IBM Corporation IBM’s transformation has gone through distinct phases in the past decade; the “smarter” phase of the journey is underway 2002 Sharing & partnering 2010 Making things smarter Instrumented, interconnected, intelligent Enable growth and productivity Optimize the whole system 2006 Globally integrating Right skills, right place, right cost Rationalize support functions for greater efficiency Radically simplify processes Consistent set of processes worldwide Leverage best practices Standardize and reduce waste Governance and performance discipline Being essential New era of computing Changing client Evolving IBMer 2012
- 4. © 2012 IBM Corporation Global Technology Outlook
- 5. Smarter Planet megatrends Smarter Planet megatrends - © 2012 IBM Corporation IBM Global Technology Outlook Objectives GTO identifies significant technology trends early. It looks for high impact disruptive technologies leading to game changing products and services over a 3-10 year horizon. Technology thresholds identified in a GTO demonstrate their influence on clients, enterprises, & industries and have high potential to create new businesses.
- 6. Smarter Planet megatrends Smarter Planet megatrends - © 2012 IBM Corporation GTO 2012 – The Genetic Map
- 7. © 2012 IBM Corporation Technology example: The Watson System
- 8. Smarter Planet megatrends Smarter Planet megatrends - © 2012 IBM Corporation 8 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
- 9. Smarter Planet megatrends Smarter Planet megatrends - © 2012 IBM Corporation Watson – a Workload Optimized System Hardware 90 x IBM Power 750 servers 2880 POWER7 3.55 GHz cores 500 GBps on-chip bandwidth 15 Terabytes of memory 500 GB of data (in memory) Software IBM DeepQA, UIMA and UIMA AS Apache Hadoop
- 10. Smarter Planet megatrends Smarter Planet megatrends - © 2012 IBM Corporation 10 Learning Systems Roadmap to Meet the Challenge Greater Autonomy Static Learning Systems 2010 2015 2020 Expert teams identify features across industries, create first commercial learning systems Dynamic Learning Systems Dynamic Data Corpus Expand Hypothesis Generation to different domains (leverage crowd-sourcing) Add Scorers for Different Input Modalities: images, video, voice, environmental, biological; leverage new devices & hardware acceleration Deeper Reasoning: Allow higher-levels of semantic abstraction. Leverage new hardware. Domain Adaptation Tools Autonomous Learning Systems Achieve understanding of natural language, images and other sensory information. Hypothesis and question generation across arbitrary domains; meta-heuristic to automate algorithm choices Keyword Search Delivers lists based on keywords & human filters 1985 Biological Inspiration: Cognitive Process Understanding Autonomously & accurately identify essential features across multiple domains. Learning systems must understand context to disambiguate. Watson 2025
- 11. © 2012 IBM Corporation Big Data and Predictive Governance Speaker: Steven Adler
- 12. Smarter Planet megatrends Smarter Planet megatrends - © 2012 IBM Corporation Internet of Things Scenarios Show a Pattern of Customer Pain Points Collecting information to provide better service and work smarter Establishing the smarter planet! Food SafetyWater Management Grid TransportationHome HealthcareLogistics
- 13. Smarter Planet megatrends Smarter Planet megatrends - © 2012 IBM Corporation 13 Forecasting a hurricane (www.noaa.gov) Fitting a curve to data Model Uncertainty All modeling is approximate Process Uncertainty Processes contain “randomness” Uncertainty of data arises from many sources Uncertain travel times Semiconductor yield Intended Spelling Text Entry Actual Spelling GPS Uncertainty ? ? ? Rumors Contaminated? {John Smith, Dallas} {John Smith, Kansas} Data Uncertainty Data input is uncertain Ambiguity {Paris Airport}Testimony Conflicting Data ? ? ?
- 14. Smarter Planet megatrends Smarter Planet megatrends - © 2012 IBM Corporation 14 GlobalDataVolumeinExabytes Sensors (InternetofThings) Multiple sources: IDC,Cisco 100 90 80 70 60 50 40 30 20 10 AggregateUncertainty% VoIP 9000 8000 7000 6000 5000 4000 3000 2000 1000 0 2005 2010 2015 By 2015, 80% of all available data will be uncertain Enterprise Data Data quality solutions exist for enterprise data like customer, product, and address data, but this is only a fraction of the total enterprise data. By 2015 the number of networked devices will be double the entire global population. All sensor data has uncertainty. Social Media (video, audio and text) The total number of social media accounts exceeds the entire global population. This data is highly uncertain in both its expression and content.
- 15. Smarter Planet megatrends Smarter Planet megatrends - © 2012 IBM Corporation Every Smarter Planet Solution has Big Data and Needs Big Analytics Smarter Planet Data at RestData in Motion Deep AnalyticsReactive Analytics Predictive ModelsReal-time Awareness Deeper InsightsFaster Decisions Big Data
- 16. Smarter Planet megatrends Smarter Planet megatrends - © 2012 IBM Corporation Up to 10,000 Times larger Up to 10,000 times faster Traditional Data Warehouse and Business Intelligence DataScale DataScale yr mo wk day hr min sec … ms µs Exa Peta Tera Giga Mega Kilo Decision Frequency Occasional Frequent Real-time Data in Motion DataatRest New “Big Data” Brings New Opportunities, Requires New Analytics Telco Promotions 100,000 records/sec, 6B/day 10 ms/decision 270TB for Deep Analytics DeepQA 100s GB for Deep Analytics 3 sec/decision Smart Traffic 250K GPS probes/sec 630K segments/sec 2 ms/decision, 4K vehicles Homeland Security 600,000 records/sec, 50B/day 1-2 ms/decision 320TB for Deep Analytics
- 17. © 2012 IBM Corporation Smarter Computing: Expert Integrated Systems Speaker: Jean-Michel Rodriguez
- 18. Smarter Planet megatrends Smarter Planet megatrends - © 2012 IBM Corporation Big Data Systems Require a Data-centric Architecture for Performance Data lives on disk and tape Move data to CPU as needed Deep Storage Hierarchy Data lives in persistent memory Many CPU’s surround and use Shallow/Flat Storage Hierarchy Old Compute-centric Model New Data-centric Model Massive Parallelism Persistent Memory Largest change in system architecture since the System 360 Huge impact on hardware, systems software, and application design Flash Phase Change Manycore FPGA input output
- 19. Smarter Planet megatrends Smarter Planet megatrends - © 2012 IBM Corporation HDD cost advantage continues, 1/10 SCM cost, but SCM dominates in performance, 10,000x faster than HDD Storage Class Memory - The Tipping Point for Data-centric Systems Relative Cost Relative Latency DRAM 100 1 SCM 1 10 FLASH 15 1000 HDD 0.1 100000 Source: Chung Lam, IBM FLASH (Phase Change) SCM in 2015 $0.05 per GB $50K per PB$0.10 / GB $0.01 / GB
- 20. Smarter Planet megatrends Smarter Planet megatrends - © 2012 IBM Corporation 2015 Vision: The Peta2 Data-centric System Goal – Data-centric, latency optimized, massive capacity system for long-running analytics and simulation applications Requirements – 1 Petaflop performance, massively parallel – 1 Petabyte BPRAM – Extremely low latency, high bandwidth interconnect (Xapt) Assumptions – Majority of data stays resident in memory for long periods, minority of data updated continually – Persistent Memory becomes primary storage This system can become the System 360 equivalent for the Era of Analytics Petascale = Petaflop OR Petabyte Petaflop x Petabyte = Peta2 2015 Peta2 Data-centric System POWER 8/A2, BPRAM ITEs on Harrier Peta2 Analytics Appliance + Reactive+Deep Analytics Platform Relative System Density per Rack Peta2 1x 12x 1.4x 2.3x 281x 336x 35.7x
- 21. Smarter Planet megatrends Smarter Planet megatrends - © 2012 IBM Corporation Announcing the First Two Members of the IBM PureSystems Family Infrastructure System: Expert at sensing and anticipating resource needs to optimize your infrastructure Platform System: Expert at optimally deploying and running applications for rapid time-to-value
- 22. Smarter Planet megatrends Smarter Planet megatrends - © 2012 IBM Corporation Applications Storage Networking Virtualization ManagementCompute Tools Flexible and open choice in a fully integrated system IBM PureFlex System is Integrated by design Expert Integrated Systems
- 23. © 2012 IBM Corporation Mobility: Enabling mobile workstyles and embrasing cloud Speaker: Erik Pedersen
- 24. Smarter Planet megatrends Smarter Planet megatrends - © 2012 IBM Corporation Advancing the Workplace of the Future 24
- 25. Smarter Planet megatrends Smarter Planet megatrends - © 2012 IBM Corporation25 Mobile device adoption, focused on accelerating security
- 26. © 2012 IBM Corporation Social Business Speaker: Stuart McRae
- 27. Smarter Planet megatrends Smarter Planet megatrends - © 2012 IBM Corporation Adoption of Social Tools is Increasing Rapidly Public Companies plan to increase spending on social despite recessionGrowth in Adult Social Network Site Use, 2005-2009 Enterprise Fortune Global 100 Companies Companies with ... 65% 54% 50% 33% Twitter Accounts Facebook Fan Pages YouTube Channels Corporate Blogs Twitter Accounts 65% 72% 71% 40% 67% Total US Europe Asia LatAm Facebook Fan Pages 54% 69% 52% 40% 33% Total US Europe Asia LatAm 50% 59% 52% 35% 33% Total US Europe Asia LatAm YouTube Accounts US Mobile Subscribers Accessing Social Sites Source:Burston-Marsteller Jan-09 Jan-10 Change Facebook 11,874 25,137 112% MySpace 12,338 11,439 -7% Twitter 1,051 4,700 347% (Thousands) Source:comScore MobiLens
- 28. Smarter Planet megatrends Smarter Planet megatrends - © 2012 IBM Corporation Convergence of Social and Analytic Technologies Transform the Way Businesses Operate Socially Synergistic Enterprise Solutions New top-line opportunities, better relationships with customers and partners, enhanced talent pool, increased resiliency and efficiency Data Data aggregation Smart filtering Meaning extraction Consumable analytics Process orchestration Stream processing Analytics Customer Sentiment Unmet Needs Talent Discovery Reasoning and Decision Support Crowdsensing, Crowdsourcing Teaming, Incentives, Motivation Society Organizations Teams Individuals Social Social Data from and about People Physical Sensors & Streams Enterprise Business Process Transformation
- 29. Smarter Planet megatrends Smarter Planet megatrends - © 2012 IBM Corporation Socially Synergistic Enterprise Solutions Can Provide Differentiating Value Physical Meets DigitalCustomer Care and Insight Workforce Optimization Financial Operations Smarter Commerce Advanced Case Management
- 30. Smarter Planet megatrends Smarter Planet megatrends - © 2012 IBM Corporation Thank you!
Hinweis der Redaktion
- Sources: 1. Big Data: “Gartner Press Release, "Gartner Reveals Top Predictions for IT Organizations and Users for 2012 and Beyond“ Dec 1, 2011 2. Analytics: “Predicts 2012 Information Infrastructure and Big Data,” Mike Blechar, Merv Adrian, Ted Friedman, W. Roy Schulte & Douglas Laney, 29 November 2011, #G00226066 3. Social Business: “Business Gets Social Innovation Key Initiative Overview”, Carol Rozwell, July 22 2011,#G00214406. 4. Smarter Planet: Pike Research, “Smart Cities: Intelligent Information and Communications Technology Infrastructure in the Government, Buildings, Transport, and Utility Domains”, 2Q11, p. 7 5. Mobile Enterprise: IBM New Workplace CIO Study, October 2011 6. Cloud: Information Week, Outlook 2012 7. Security: “Gartner Press Release, "Gartner Reveals Top Predictions for IT Organizations and Users for 2012 and Beyond“ Dec 1, 2011 8. Growth Markets: IDC “Asia/Pacific Excluding Japan 2012 Top ICT Predictions”, Dec 2011
- Evolution of the “computer” from tool for calculation, to data warehouse, to delivery of information to knowledge
- 3 CHANGES to EMPHASIZE: EMPHASIS ON DYNAMIC NATURE OF MODELS, NOT STATIC - ACTIVE LEARNING – Hard - DYNAMIC Engines (Training, Policy, Hypothesis, Outcome, Verification) - Natural interfaces How is our Learning System different from past Machine Learning approaches? Our Learning System will automatically identify key features. Key Features selection is the technique of selecting a subset of relevant features for learning models. For example, key features to diagnose an illness may be a person's temperature, white blood cell count, pH level, etc. Current state of the art either has (A) humans identifying what are the key features for different domains or (B) allowing machine learning programs to extract key features based on expert rules (provided by humans) or statistical methods which may lead to false conclusions in domains that involve semantic ambiguity. The Learning System we're building will use crowd sourcing techniques to automatically identify key features for a domain and will proactively ask humans for disambiguation, instead of waiting for humans to notice the model is erroneous (for example models that rated questionable mortgages as AAA or a software program that deduces that Internet Cookies are edible). In this vein, another key difference in our Learning System is active continuous verification. The current trends that provide increasing amounts of digital data (e.g. IBM Smarter Planet sensors) will enable our Learning System to modify itself to prune key features that are no longer relevant. In summary, (1) Automatic Extraction of Key Features (2) Continuous active self verification and (3) The ability to select the appropriate Machine Learning technique (statistical, genetic programming, neural networks, etc), and modify these techniques to changing conditions - all these three features have not been integrated into prior Machine Learning approaches. A hypothesis is necessarily about a problem that is not formalized (if the problem were formalized, then no hypothesis would be required, only a formal solution) Without a formal problem, the task of formulating hypotheses becomes one of creating alternative problem representations and selecting among them, in part, based on possible solutions to each Known systems that attempt to do this require a defined problem space, where the range of possible hypotheses is calculated from a range of possible system states “Real world” problems do not emerge from a range of possible states, however, but instead occur when previously defined ranges (or dimensions) are violated The only known systems capable of formulating hypotheses about arbitrary states and selecting among them are biological cognitive systems Explanation of this is necessary before a system that "Creates Hypotheses" can be introduced, even as a hypothetical
- Some processes are inherently uncertain. We do not know with precision how long it takes to drive from A to B, nor how many and which chips on a wafer will pass quality tests. Uncertainty comes from the data as well, in lots of ways: text (typos, ambiguities, conflict) – are these two people the same? Can I find evidence by looking at lots of data that they are, or are not? If I do then I have reduced that uncertainty. We have already talked some about sensors. It is well known that location-finding techniques do not work well in complex environments, like cities – we will talk more about that later, but the issue is a simple one. One would think that processing geospatial (GPS) data is core to managing a city – how good a job can I do if the location information is poor for assets like maintenance trucks, fire trucks, police. Finally, there are new kinds of data uncertainty that come from things like social media: rumors, lies, falsehoods, wishful thinking. One has to distinguish the nuggets from the ore. One can look online to see many postings about contaminated baby food making its way from China to the US in 2008. Those rumors turned out not to be true – how does a system that processes petabytes of text understand these nuances – how to train it such a system. Finally there are model uncertainties – we often approximate complex environments in order to be able to query them more efficiently – approximating collection of points with a line or forecasting a hurricane. But we must understand that using these models we do not have perfect answers, and we have to take into account these imperfections in business decisions. The good news is that we (IBM) have been managing a business with uncertain processes models, and data for many years – the semiconductor manufacturing business is based on driving wafer processes until the yield is good enough to scale into production – we are used to uncertainty bars here, at the manufacturing level, and at the micro level where we are studying the physical and chemical processes of nanostructures. The challenge is to understand how we deal with uncertainty when we try to analyze big data – how do we represent uncertain data, reduce uncertainty of data, reason about that data in a way so that we can make business decisions (yes, no). In 2011 we demonstrated in a very public way a system that dealt with uncertainty and made business decisions – Watson playing Jeopardy. The system did a good job at understanding confidence in answers based on a variety of factors, in order to know whether it had the answer right or wrong. Of course sometimes Watson got it wrong. So the kinds of business decisions that we will make based on uncertain data need to be appropriate. Can I route firetrucks if I do not know where they are (Data )what the road status is? How can I plan my plant capacity if my equipment is not predictably functional (Data,Model)? Do I know enough about a potential customer to be able to offer an appropriate sales incentive? [Data]. How about if I have 500,000 such customers? [Scale]
- In another GTO topic, we talk about companies who's value is correlated with their ability to derive insight from their data: today we think about companies like Google or Facebook. But we need to be thinking also about Healthcare companies (their medical records are potential business assets with huge value to them if they can monetize insight). Similarly, the ability of a retail company to be able to use its VIP loyalty data, its billing history, and maybe public social media to be able to do targeted marketing, or assess the acceptance of a product or a brand is potentially enormous. When we look at where this explosive data growth is coming from we see that it is messy stuff: text, images, videos, audio, sensor data This is true inside the firewall as well. Manufacturers have large physical assets that are sensor-connected that have to be managed. Using these sensors with process models for equipment breakdown provides an ability to plan better. Better planning reduces costs. On the next chart we will talk about some of these applications.
- Create innovative capabilities that enable work to be performed anywhere, anytime, with anyone in a secure and socially collaborative way to further IBM's leadership as a smarter workforce
- The time is ripe for Socially Synergistic Solutions, because of the increasing adoption of social software both in consumer space and by enterprises. Fortune Global 100 Study: Data was collected between November 2009 and January 2010 among the top 100 companies of Fortune痴 Global 500 companies. Sample size for countries/regions: U.S. = 29 companies, Europe = 48 companies, Asia-Pacific = 20 companies, Latin America = 3 companies. Because of the low sample size for Latin America, data is only broken out for this region for overall activity rates. Active accounts have at least one post in the past 3 months.Outliers have been noted. Data was collected by Burson-Marsteller global research team. (Burson-Marsteller (www.burson-marsteller.com), established in 1953, is a leading global public relations and communications firm. It provides clients with strategic thinking and program execution across a full range of public relations, public affairs, advertising and web-related services. The firm’s seamless worldwide network consists of 72 offices and 60 affiliate offices, together operating in 85 countries across six continents. Burson-Marsteller is a part of Young & Rubicam Brands, a subsidiary of WPP (NASDAQ: WPPGY).
- We are at a tipping point in the convergence of social software and analytic technologies. Businesses require a tight coupling of the two, in what we call “socially synergistic solutions.” We are at a tipping point in the convergence of social software and analytic technologies, and business will increasingly require a tight coupling of the two to maximize their success. Customers will want to combine data from sensors and streams, their enterprise data, and social data -- that's data from or about people -- and use computation and analytics to bring the information together, aggregate, filter, and correlate it, and analyze it together to generate new insights. (as Pepsi, for example, wants to do to understand how their brands are perceived and how that influences sales in different demographics. And they will want to use analytics to more effectively take advantage of emerging techniques in social software to involve people, get their participation and improve their performance, and influence their behavior (as the city of Dubuque wants to do, by combining sensor data, analytics, and techniques taken from digital games, to reduce the use of resources like water.)These capabilities have traditionally existed in two different silos, with one set of technologies used to analyze a companies formal data like transactions, metrics, and KPIs, and another set of tools used to support the often tacit and unstructured human and collaborative work that goes on. And we've seen some blending of the two, initially with people emailing a spreadsheet, or setting up a discussion space to discuss data from analytic systems. And increasingly, systems like Cognos 10 and Manyeyes integrate collaboration tools with the reporting and visualization ones. What we are describing here goes beyond that, taking the deep analytic capabilities and applying them to the social data.