Smarter Planet: How Big Data changes our world
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From the Huset Markedsføring Big Data conference december 5th 2012
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Smarter Planet: How Big Data changes our world
- 1. SMARTER PLANET - How big data transforms our world Kim Escherich Executive Innovation Architect, IBM Global Business Services 1
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- 3. 340.282.366.920.938.463.463.374.607.431.768.211.456 Connected things 50 40 Billion 30 20 10 People 0 2003 2010 2015 2020 3 Source: Cisco IoT 2011 infographic
- 4. IBM Global Business Services Billions of RFID-tags embedded into our world and across ecosystems Billions of camera phones 4 INSTRUMENTED © 2011 IBM
- 5. IBM Global Business Services +2 billion Internet-subscribers +20 billion Connected devices 5 INTERCONNECTED © 2011 IBM
- 6. IBM Global Business Services Petaflop Super computers Zettabytes of Internet traffic Scientists working to prevent influenza outbreaks, model the IP-traffic exceeded 1 Zettabyte in viruses with a super-computer 2011 – a 540.000x increase from operating at one petaflop 2003. 6 INTELLIGENT © 2011 IBM
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- 9. Neurowear Necomimi http://en.necomimi.com/ 9
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- 12. By 2015, 80% of all available data will be uncertain: Veracity By 2015 the number of networked devices will be double the entire global population. All 9000 sensor data has uncertainty. 8000 100 Global Data Volume in Exabytes 90 The total number of social media 7000 accounts exceeds the entire global Aggregate Uncertainty % 80 population. This data is highly uncertain 6000 in both its expression and content. 70 5000 60 4000 Data quality solutions exist for 50 enterprise data like 3000 40 customer, product, and address data, but this is only a fraction 30 of the total enterprise data. 2000 20 1000 10 0 Multiple sources: IDC,Cisco 2005 2010 2015 12 © 2012 IBM Corporation
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- 14. What can you do with big data? Analyze a Variety of Information Analyze Information in Motion Social media/sentiment analysis Smart Grid management Geospatial analysis Multimodal surveillance Brand strategy Real-time promotions Scientific research Cyber security Epidemic early warning system ICU monitoring Market analysis Options trading Video analysis Click-stream analysis Audio analysis CDR processing IT log analysis RFID tracking & analysis Discovery & Experimentation Analyze Extreme Volumes Sentiment analysis of Information Brand strategy Scientific research Transaction analysis to create insight-based Ad-hoc analysis product/service offerings Model development Fraud modeling & detection Hypothesis testing Risk modeling & management Transaction analysis to create insight- Social media/sentiment analysis based product/service offerings Environmental analysis Manage and Plan Operational analytics – BI reporting Planning and forecasting analysis Predictive analysis … 14 © 2009 IBM Corporation
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- 17. Erik Fischer The Geotaggers’ World Atlas: Copenhagen http://www.flickr.com/photos/walkingsf/sets/72157623971287575/ 17
- 18. IBM Sensor Solutions IBM City-in-Motion analyses multi-modal-traffic in real-time © 2011 IBM Corporation
- 19. IBM Mobile Solutions Vestas optimizes capital investments based on 2.5 Petabytes of information 19 © 2012 IBM Corporation
- 20. NYPD: Real Time Crime Center NYPD provides mobile access to more than 120 million criminal complaints, arrests and 911 records 20
- 21. Intelligent City Operations Center for Smart Cities IBM Mobile Solutions 21 © 2012 IBM Corporation
- 22. Watson: From Jeopardy winner to healthcare provider 22
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- 24. We are ushering in a new wave of innovation Age 6th Wave Age of IT & of Oil, Cars Telecom Age and Mass of Steel, Production 5th Wave Electricity and Heavy Engineering 4th Wave Smarter Innovation Age 3rd Wave of Steam and Railways Products The Industrial Instrumented, interc Revolution 2nd Wave onnected, and intelligent 1st Wave Building blocks for a smarter planet Sustainability 1770 1830 1875 1920 1970 2010 Source: “Next Generation Green: Tomorrow’s Innovation Green Business Leaders”, Business Week, Feb 4, 2008 and Nicolai Kontratiev: “The Major Economic Cycles” (1925)
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- 26. 6th Wave 26 James Bradfield Moody & Bianca Nogrady: The Sixth Wave – How to succeed in a ressource-limted world
- 27. Price 27
- 28. Biodynamic Organic Conventional Pesticides Production method Herpacides Price Chemicals Production environment Child labor Sustainability Packaging Climate Geosphere Profiled change Environmental Health footprint consequences Positive Biosphere GMO Track Negative Sociosphere Health impact Transport Sustainability History/Origin 28
- 29. Findings from the research collaboration of IBM Institute for Business Value and Saïd Business School, University of Oxford Analytics: The real-world use of big data How innovative enterprises extract value from uncertain data ©2012 IBM Corporation
- 30. Study overview More than 1100 business and IT professionals around the globe provided a snapshot of current big data activities Global respondents Functional breadth 5% 14% 13% 8% 10% 46% 46% 54% Information 23% Business technology professionals 15% professionals 37% n = 1144 8% 16% 4% Information technology Executive management Asia Pacific Europe Latin America Middle East and Africa Marketing / sales Research and product development North America No country identified General management / Operations Finance / risk management Total respondents n = 1144 Source: Analytics: The real-world use of big data, a collaborative research study by the IBM 30 | ©2012 IBM Corporation Institute for Business Value and the Saïd Business School at the University of Oxford. © IBM 2012
- 31. Macro findings Nearly two out of three respondents reports realizing a competitive advantage from information and analytics Realizing a competitive advantage Competitive advantage enabler A majority of respondents reported analytics and information (including big data) creates a 2012 competitive advantage within their 63% market or industry Represents a 70% increase 2011 since 2010 58% 70% increase Organizations already active in big data activities were 15% 2010 37% more likely to report a competitive advantage Respondents were asked “To what extent does the use of information (including big A higher-than-average percentage data) and analytics create a competitive advantage for your organization in your of respondents in Latin industry or market.” Respondent percentages shown are for those who rated the America, India/SE Asia and ANZ extent a [4 ] or [5 Significant extent]. The same question has been asked each year. reported realizing a competitive 2010 and 2011 datasets © Massachusetts Institute of Technology Total respondents n = 1144 advantage Source: Analytics: The real-world use of big data, a collaborative research study by the IBM 31 | ©2012 IBM Corporation Institute for Business Value and the Saïd Business School at the University of Oxford. © IBM 2012
- 32. Introduction to big data Respondents define big data by the opportunities it creates Defining big data Greater scope of information Integration creates cross-enterprise view External data adds depth to internal data New kinds of data and analysis New sources of information generated by pervasive devices Complex analysis simplified through availability of maturing tools Real-time information streaming Digital feeds from sensors, social and syndicated data Instant awareness and accelerated decision making Respondents were asked to choose up to two descriptions about how their organizations view big data from choices above. Choices have been abbreviated, and selections have been normalized to equal 100%. Source: Analytics: The real-world use of big data, a collaborative research study by the IBM 32 | ©2012 IBM Corporation Institute for Business Value and the Saïd Business School at the University of Oxford. © IBM 2012
- 33. Macro findings Three out of four organizations have big data activities underway; and one in four are either in pilot or production Big data activities Early days of big data era Almost half of all organizations surveyed report active discussions about big data plans Big data has moved out of IT and into business discussions Getting underway More than a quarter of organizations have active big data pilots or implementations Tapping into big data is becoming real Acceleration ahead The number of active pilots underway suggests big data implementations will rise exponentially in the next few years Once foundational technologies are Respondents were asked to describe the state installed, use spreads quickly across the of big data activities within their organization. Total respondents n = 1061 organization Totals do not equal 100% due to rounding Source: Analytics: The real-world use of big data, a collaborative research study by the IBM 33 | ©2012 IBM Corporation Institute for Business Value and the Saïd Business School at the University of Oxford. © IBM 2012
- 34. Key findings Five key findings highlight how organizations are moving forward with big data 1 Customer analytics are driving big data initiatives Big data is dependent upon a scalable and extensible 2 information foundation Initial big data efforts are focused on gaining insights 3 from existing and new sources of internal data 4 Big data requires strong analytics capabilities The emerging pattern of big data adoption is 5 focused upon delivering measureable business value Source: Analytics: The real-world use of big data, a collaborative research study by the IBM 34 | ©2012 IBM Corporation Institute for Business Value and the Saïd Business School at the University of Oxford. © IBM 2012
- 35. Recommendations An overarching set of recommendations apply to all organizations focused on creating value from big data 1 2 3 Commit initial Develop Start with existing efforts at customer- enterprise-wide data to achieve centric outcomes big data blueprint near-term results 4 5 Build analytical Create a business capabilities based on case based on business priorities measurable outcomes Source: Analytics: The real-world use of big data, a collaborative research study by the IBM 35 | ©2012 IBM Corporation Institute for Business Value and the Saïd Business School at the University of Oxford. © IBM 2012
- 36. Download the study and access additional resources www.ibm.com/2012bigdatastudy www.ibm.com/gbs/bigdata@work Other IBV analytics studies are available at: www.ibm.com/iibv | ©2012 IBM Corporation
- 37. Thank you Kim Escherich IBM Global Business Services escherich@dk.ibm.com +45 2880 4733 internetofthings.dk escherich.biz @kescherich /in/escherich /escherich kescherich@gmail.com 37
Editor's Notes
- This year, we surveyed more than 1100 business and IT professionals from 95 countries around the globe. Our global survey – offered online to anyone interested in big data – was conducted in the summer of 2012.About 60% of our survey respondents were from mature markets in North America and Europe, with the remainder coming from growth markets or unidentified.More than half of the respondents were from the business, and just under half were from IT.This tells us that big data is beginning to transition from an IT issue of hardware and software to a business imperative that executives want to understand better.
- When we look at the broader analytics market – as we have done for the past three years in the IBV -- we found that 63 percent – nearly two-thirds – of respondents report that the use of information (including big data) and analytics is creating a competitive advantage for their organizations. This compares to 37 percent of respondents in our 2010 research – a 70 percent increase in just two years. That’s how quickly this market is maturing … in 2012, 63% of organizations did not feel analytics was creating a competitive advantage, today, 63% recognize the value analytics brings to their organization.For a little global perspective, respondents from North America and Europe were consistent at about 63% realizing a competitive advantage. A higher-than-average percentage of respondents in Latin America, India/SE Asia and ANZ reported realizing a competitive advantageAs an increasingly important segment of the broader information and analytics market, big data is having an impact. Respondents whose organizations had implemented big data pilot projects or deployments were 15 percent more likely to report a significant advantage from information (including big data) and analytics compared to those relying on traditional analytics alone.
- Much of the confusion about big data begins with the definition itself. To understand our study respondents’ definition of the term, we asked each to select up to two characteristics of big data. Rather than any single characteristic clearly dominating among the choices, respondents were divided in their views on whether big data is best described by today’s greater volume of data, the new types of data and analysis, or the emerging requirements for more real-time information analysis.[go to bullets]Greater scope of informationIntegration creates cross-enterprise view External data adds depth to internal dataNew kinds of data and analysisNew sources of information generated by pervasive devicesComplex analysis simplified through availability of maturing toolsReal-time information streamingDigital feeds from sensors, social and syndicated dataInstant awareness and accelerated decision makingOne surprising study finding is the relatively small impact of social media data on the current big data marketplace
- Our survey confirms that most organizations are currently in the early stages of big data development efforts, with the majority focused either on understanding the concepts (24 percent) or defining a roadmap related to big data (47 percent, see Figure 3). However, 28 percent of respondents are in leading-edge organizations where they are developing proofs of concepts (POCs) or have already implemented big data solutions at scale.
- By analyzing the responses of these early adopters, five key study findings show some common and interesting trends and insights:Across industries, the business case for big data is strongly focused on addressing customer-centric objectives A scalable and extensible information management foundation is a prerequisite for big data advancementOrganizations are beginning their pilots and implementations by using existing and newly accessible internal sources of dataAdvanced analytic capabilities are required, yet often lacking, for organizations to get the most value from big dataAs organizations’ awareness and involvement in big data grows, we see four stages of big data adoption emerging.
- IBM analysis of our Big Data @ Work Study findings provided new insights into how organizations at each stage are advancing their big data efforts. Driven by the need to solve business challenges, in light of both advancing technologies and the changing nature of data, organizations are starting to look closer at big data’s potential benefits. To extract more value from big data, we offer a broad set of recommendations to organizations as they proceed down the path of big data. Commit initial efforts at customer-centric outcomes.Develop enterprise-wide big data blueprint.Start with existing data to achieve near-tern results.Build analytical capabilities based on business priorities.Create a business case based on measurable outcomes.[If only using this recommendations page, please use summary below][If using “recommendations on a page” approach, skip to next slide][If using “recommendations by stage” please summarize these over-arching recommendations before proceeding].Summary:Commit initial efforts to customer-centric outcomes It is imperative that organizations focus big data initiatives on areas that can provide the most value to the business. For many industries, this will mean beginning with customer analytics that enable better service to customers as a result of being able to truly understand customer needs and anticipate future behaviors. Develop an enterprise-wide big data blueprint A blueprint encompasses the vision, strategy and requirements for big data within an organization, and is critical to establishing alignment between the needs of business users and the implementation roadmap of IT. It creates a common understanding of how the enterprise intends to use big data to improve its business objectives. An effective blueprint defines the scope of big data within the organization by identifying the key business challenges to which it will be applied, the business process requirements that define how big data will be used, and the architecture which includes the data, tools and hardware needed to achieve it. It is the basis for developing a roadmap to guide the organization through a pragmatic approach to develop and implement its big data solutions in ways that create sustainable business value. Start with existing data to achieve near-term results To achieve near-term results while building the momentum and expertise to sustain a big data program, it is critical that companies take a pragmatic approach. As respondents confirmed, the most logical and cost-effective place to start looking for new insights is within the enterprise. Looking internally first allows organizations to leverage their existing data, software and skills, and to deliver near-term business value and gain important experience as they then consider extending existing capabilities to address more complex sources and types of data. Most organizations will want to do this to take advantage of the information stored in existing repositories while scaling their data warehouse(s) to handle larger volumes and varieties of data. Build analytics capabilities based on business priorities Throughout the world, organizations face a growing variety of analytics tools while also facing a critical shortage of analytical skills. Big data effectiveness hinges on addressing this significant gap. In short, organizations will have to invest in acquiring both tools and skills. As part of this process, it is expected that new roles and career models will emerge for individuals with the requisite balance of analytical, functional and IT skills. Create a business case based on measurable outcomes To develop a comprehensive and viable big data strategy and the subsequent roadmap requires a solid, quantifiable business case. Therefore, it is important to have the active involvement and sponsorship from one or more business executives throughout this process. Equally important to achieving long-term success is strong, ongoing business and IT collaboration. Additional recommendations by stage: Start where you are Certain key activities are characteristic of each stage in the big data adoption lifecycle. In our study, we offer recommendations by stage, which offer a proven and practical approach for moving from one stage to the next.