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  • Analytics Moving from Enterprise Data to Big DataThe implications of this pattern are clear – a major transformation is underway.  This transformation is fundamentally changing how organizations are structured, how daily operations are managed and where new investments are made to create value. It is being powered by the onset of big data, which in turn is being instrumented and analyzed by new computing systems with deep analytic capabilities.  Analytics has grown beyond enterprise data to big, largely unstructured data from billions and billions of diverse sources: Analytics has grown beyond using enterprise data exclusively to embracing big data – Information (data and content) with massive volume (growing from terabytes to petabytes), velocity (where analysis happens in milliseconds) and variety (unstructured data from diverse sources such as sensors, electronic meters, social media (facebook, twitter), video, audio, fiber optics, sonar, and more.) Volume:In the 12 terabytes of tweets created daily – there’s the opportunity to better understand customer sentiment about their needs, your company, your products and your competitors.In the electronic meters being rolled out around the world, there is the opportunity to better predict power consumption, better manage grid performance and identify problems before they become serious.Velocity:There are certain applications where being able to analyze and act in real time is paramount. For example, it is far better to identify and handle potential fraud while a transaction is happening than after it has occurred.Telcos can use their call detail records to better understand customer issues and prevent churn – a huge problem in the Telco industry where clients become more profitable the longer you retain them.VarietyMuch of the excitement around big data is from unstructured data such as documents, images and video, which can be used for new insights for applications such as law enforcement or to improve customer satisfaction.Note: The intent of the slide is not to represent specific IBM clients (like a series of client case studies) rather it is illustrative of information volume, velocity and variety. With the exception of the twitter example (from a twitter blog in 2011), all of these examples are drawn from either deployed solutions or proof of concepts. There are 340 million tweets sent each day, or roughly 12 TB data. Source:>340M tweets a dayEvery second of high-definition video creates 2,000 times as many bytes as a single page of printed text. In many countries there are thousands of surveillance video cameras in most major cities used for traffic and security monitoring. Banks and insurance companies are identifying fraud patterns by combing different data sources in real time to analyze transactions. Utilities are switching to electronic meters that transmit readings every 15 seconds resulting in billions of meter readings each year.
  • The term “big data” is pervasive, and yet still the notion engenders confusion. Big data has been used to convey all sorts of concepts, including: huge quantities of data, social media analytics, next generation data management capabilities, real-time data, and much more. Whatever the label, organizations are starting to understand and explore how to process and analyze a vast array of information in new ways. In doing so, a small, but growing group of pioneers is achieving breakthrough business outcomes.So what makes today’s big data activities different? Some organizations have already been handling big data for years. A global telecommunications company, for example, collects billions of detailed call records per day from 120 different systems and stores each for at least nine months. An oil exploration company analyzes terabytes of geologic data, and stock exchanges process millions of transactions per minute. For these companies, the concept of big data is not new. However, two important trends make this era of big data quite different: The digitization of virtually “everything” now creates new types of large and real-time data across a broad range of industries. Much of this is non-standard data: for example, streaming, geospatial or sensor-generated data that does not fit neatly into traditional, structured, relational warehouses. Today’s advanced analytics technologies and techniques enable organizations to extract insights from data with previously unachievable levels of sophistication, speed and accuracy.
  • Respondents’ definition of big data align with a useful way of characterizing three dimensions of big data – “the three V’s:” volume, variety and velocity. And while they cover the key attributes of big data itself, we believe organizations need to consider an important fourth dimension: veracity. Inclusion of veracity as the fourth big data attribute emphasizes the importance of addressing and managing for the uncertainty inherent within some types of data The convergence of these four dimensions helps both to define and distinguish big data: Volume: The amount of data. Perhaps the characteristic most associated with big data, volume refers to the mass quantities of data that organizations are trying to harness to improve decision-making across the enterprise. Data volumes continue to increase at an unprecedented rate. However, what constitutes truly “high” volume varies by industry and even geography, and is smaller than the petabytes and zetabytes often referenced. Just over half of respondents consider datasets between one terabyte and one petabyte to be big data, while another 30 percent simply didn’t know how big “big” is for their organization. Still, all can agree that whatever is considered “high volume” today will be even higher tomorrow. Variety: Different types of data and data sources. Variety is about managing the complexity of multiple data types, including structured, semi-structured and unstructured data. Organizations need to integrate and analyze data from a complex array of both traditional and non-traditional information sources, from within and outside the enterprise. With the explosion of sensors, smart devices and social collaboration technologies, data is being generated in countless forms, including: text, web data, tweets, sensor data, audio, video, click streams, log files and more. Velocity: Data in motion. The speed at which data is created, processed and analyzed continues to accelerate. Contributing to higher velocity is the real-time nature of data creation, as well as the need to incorporate streaming data into business processes and decision making. Velocity impacts latency – the lag time between when data is created or captured, and when it is accessible. Today, data is continually being generated at a pace that is impossible for traditional systems to capture, store and analyze. For time-sensitive processes such as real-time fraud detection or multi-channel “instant” marketing, certain types of data must be analyzed in real time to be of value to the business. Veracity: Data uncertainty. Veracity refers to the level of reliability associated with certain types of data. Striving for high data quality is an important big data requirement and challenge, but even the best data cleansing methods cannot remove the inherent unpredictability of some data, like the weather, the economy, or a customer’s actual future buying decisions. The need to acknowledge and plan for uncertainty is a dimension of big data that has been introduced as executives seek to better understand the uncertain world around them.Ultimately, big data is an amalgam of these characteristics that creates an opportunity for organizations to gain competitive advantage in today’s digitized marketplace. It enables companies to transform the ways they interact with and serve their customers, and allows organizations – even entire industries – to transform themselves. Not every organization will take the same approach toward engaging and building its big data capabilities. But opportunities to utilize new big data technology and analytics to improve decision-making and performance exist in every industry.   
  • This year, the IBV partnered with the Saïd Business School at the University of Oxford. The IBV considers these academic partnership essential to creating a deeper understanding of the trends in the marketplace.The Saïd School is equally committed to focusing on issues critical to innovative leaders around the globe.So once we learned of our mutual focus on big data, we seized at the opportunity to collaborate with such globally renowned faculty.IBM has a history of partnership with Oxford that extends back 100 years.In addition to partnering on this study, which included a series of roundtables with faculty from across Oxford this past summer, IBM is also assisting the School with new course curriculum and other faculty engagement opportunities.This year’s study is based on a global survey of more than 1100 business and IT professionals from 95 countries around the globe.Our first indication that big data has moved from being an IT opportunity to a business priority came when we found that more than half of our respondents were business executives representing the breadth of organizational functions.
  • 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.
  • When asked to rank their top three objectives for big data, nearly half of the respondents identified customer-centric objectives as their organization’s top priority. Organizations are committed to improving the customer experience and better understanding customer preferences and behavior. This finding is consistent with other recent IBV studies: Understanding today’s “empowered consumer” was also identified as a high priority in both the 2011 IBM Global Chief Marketing Officer Study and 2012 IBM Global Chief Executive Officer Study Companies clearly see big data as providing the ability to better understand and predict customer behaviors, and by doing so, improve the customer experience. Transactions, multi-channel interactions, social media, syndicated data through sources like loyalty cards, and other customer-related information have increased the ability of organizations to create a complete picture of customers’ preferences and demands – a goal of marketing, sales and customer service for decades. Through this deeper understanding, organizations of all types are finding new ways to engage with existing and potential customers. This principle clearly applies in retail, but equally as well in telecommunications, healthcare, government, banking and finance, and consumer products where end-consumers and citizens are involved, and in business-to-business interactions among partners and suppliers.
  • The promise of achieving significant, measurable business value from big data can only be realized if organizations put into place an information foundation that supports the rapidly growing volume, variety and velocity of data. We asked respondents to identify the current state of their big data infrastructures. Almost two-thirds report having started their big data journeys with an information foundation that is integrated, scalable, extensible and secure. Four information management components were cited most often as part of respondents’ big data initiatives. Integrated information is a core component of any analytics effort, and it is even more important with big data. As noted in our 2011 study on advanced analytics, an organization’s data has to be readily available and accessible to the people and systems that need it. The next two most prevalent information management foundation components in big data initiatives are a scalable storage infrastructure and high-capacity warehouse. Each supports the rapid growth of current and future data coming into the organization. For many organizations, improving the capability to manage growing volumes is the first big data priority, followed closely by addressing the expanding variety of data. Strong security and governance processes are in place at 58 percent of the organizations who report having active big data efforts underway. While security and governance have long been an inherent part of business intelligence, the added legal, ethical and regulatory considerations of big data introduce new risks and expand the potential for very public missteps, as we have already seen in some companies that have lost control of data or use it in questionable ways.
  • Most early big data efforts are targeted at sourcing and analyzing internal data. According to our survey, more than half of the respondents reported internal data as the primary source of big data within their organizations. This suggests that companies are taking a pragmatic approach to adopting big data and also that there is tremendous untapped value still locked away in these internal systemsAs expected, internal data is the most mature, well-understood data available to organizations. It has been collected, integrated, structured and standardized through years of enterprise resource planning, master data management, business intelligence and other related work. By applying analytics, internal data extracted from customer transactions, interactions, events and emails can provide valuable insights. However, in many organizations, the size and scope of this internal data, such as detailed transactions and operational log data, have become too large or varied to manage within traditional systems. Almost three out of four respondents with active big data efforts are analyzing log data. This is “machine/sensor generated” data produced to record the details of automated functions performed within business or information systems – data that has outgrown the ability to be stored and analyzed by many traditional systems. As a result, much of this data is collected, but not analyzed. Executive interviews confirmed that many CIOs who are guiding their companies’ big data initiatives are beginning with these untapped sources of internal information, using the additional processing power provided by a more scalable infrastructure.
  • Big data does not create value, however, until it is put to use to solve important business challenges. This requires access to more and different kinds of data, as well as strong analytics capabilities that include both software tools and the requisite skills to use them.When we look at those organizations engaged in big data activities, we find that they start with a strong core of analytics capabilities designed to address structured data. Next, they add capabilities to take advantage of the wealth of data coming into the organization that is both semi-structured (data that can be converted to standard data forms) and unstructured (data in non-standard forms).More than 75 percent of respondents with active big data efforts reported using core analytics capabilities, such as query and reporting, and data mining to analyze big data, while more than 67 percent report using predictive modeling. Beginning with these foundational analytics capabilities is a pragmatic way to start interpreting and analyzing big data, especially when it is being stored in a relational database. Having the capabilities to analyze unstructured (for example, geospatial location data, voice and video) or streaming data continues to be a challenge for most organizations. While the hardware and software in these areas are maturing, the skills are in short supply. Fewer than 25 percent of respondents with active big data efforts reported having the required capabilities to analyze extremely unstructured data like voice and video. Acquiring or developing these more advanced technical and analytic capabilities required for big data advancement is becoming a top challenge among many organizations with active big data efforts. Among these organizations, the lack of advanced analytical skills is a major inhibitor to getting the most value from big data.
  • To better understand the big data landscape, we asked respondents to describe the level of big data activities in their organizations today. The results suggest four main stages of big data adoption and progression along a continuum that we have labeled Educate, Explore, Engage and Execute:Educate: Building a base of knowledge (24 percent of respondents)In the Educate stage, the primary focus is on awareness and knowledge development. Almost 25 percent of respondents indicated they are not yet using big data within their organizations. While some remain relatively unaware of the topic of big data, our interviews suggest that most organizations in this stage are studying the potential benefits of big data technologies and analytics, and trying to better understand how big data can help address important business opportunities in their own industries or markets. Within these organizations, it is mainly individuals doing the knowledge gathering as opposed to formal work groups, and their learnings are not yet being used by the organization. As a result, the potential for big data has not yet been fully understood and embraced by the business executives. Explore: Defining the business case and roadmap (47 percent)The focus of the Explore stage is to develop an organization’s roadmap for big data development. Almost half of respondents reported formal, ongoing discussions within their organizations about how to use big data to solve important business challenges. Key objectives of these organizations include developing a quantifiable business case and creating a big data blueprint. This strategy and roadmap takes into consideration existing data, technology and skills, and then outlines where to start and how to develop a plan aligned with the organization’s business strategy. Engage: Embracing big data (22 percent)In the Engage stage, organizations begin to prove the business value of big data, as well as perform an assessment of their technologies and skills. More than one in five respondent organizations is currently developing POCs to validate the requirements associated with implementing big data initiatives, as well as to articulate the expected returns. Organizations in this group are working – within a defined, limited scope – to understand and test the technologies and skills required to capitalize on new sources of data. Execute: Implementing big data at scale (6 percent)In the Execute stage, big data and analytics capabilities are more widely operationalized and implemented within the organization. However, only 6 percent of respondents reported that their organizations have implemented two or more big data solutions at scale – the threshold for advancing to this stage. The small number of organizations in the Execute stage is consistent with the implementations we see in the marketplace. Importantly, these leading organizations are leveraging big data to transform their businesses and thus are deriving the greatest value from their information assets. With the rate of enterprise big data adoption accelerating rapidly – as evidenced by 22 percent of respondents in the Engage stage, with either POCs or active pilots underway – we expect the percentage of organizations at this stage to more than double over the next year.
  • Thomas Inman

    1. 1. Findings from the research collaboration ofIBM Institute for Business Value andSaïd Business School, University of OxfordAnalytics: The real-world use of big dataHow innovative enterprises extract value from uncertain dataTom Inman, Vice President, IBM Software ©2012 IBM Corporation
    2. 2. Agenda 1 Introduction to Big Data 2 Macro Findings 3 Key Findings 4 Recommendations 5 Call to Action2 | ©2012 IBM Corporation
    3. 3. Introduction to big data| ©2012 IBM Corporation
    4. 4. Introduction to big dataBig data is a business priority – inspiring new models andprocesses for organizations, and even entire industriesAnalytics is expanding from enterprise data to big data Volume Velocity Variety12of Tweets create daily terabytes 5 million trade events per second 100’s from surveillance cameras video feeds Analyze product sentiment Identify potential fraud Monitor events of interest 350 meter readings per annum billion 1.5 billion call detail records per hour 80% data growth are images, video, documents… Predict power consumption Prevent customer churn Improve customer satisfaction | ©2012 IBM Corporation
    5. 5. Introduction to big dataBig data is a business priority – inspiring new models andprocesses for organizations, and even entire industries 5 | ©2012 IBM Corporation
    6. 6. Introduction to big data Applications for Big Data AnalyticsSmarter Healthcare Multi-channel Finance Log Analysis salesHomeland Security Search Quality Traffic Control Telecom Manufacturing Trading Analytics Fraud and Risk Retail: Churn, NBO | ©2012 IBM Corporation
    7. 7. Introduction to big dataBig data embodies new data characteristics created bytoday’s digitized marketplace Characteristics of big data 7 | ©2012 IBM Corporation
    8. 8. Macro findings| ©2012 IBM Corporation
    9. 9. Study overviewIBM Institute for Business Value and the Saïd BusinessSchool partnered to benchmark global big data activities IBM Institute for Business Value IBM Global Business Services, through the IBM Institute for Business Value, develops fact-based strategies and insights for senior executives around critical public and private sector issues. Saïd Business School University of Oxford The Saïd Business School is one of the leading business schools in the UK. The School is establishing a new model for business education by being deeply embedded in the University of Oxford, a world-class university, and tackling some of the challenges the world is encountering. 9 | ©2012 IBM Corporation
    10. 10. Macro findingsNearly two out of three respondents reports realizing acompetitive advantage from information and analytics Realizing a competitive advantageCompetitive 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 America, industry or market.” Respondent percentages shown are for those who rated the India/SE Asia and ANZ reported extent a [4 ] or [5 Significant extent]. The same question has been asked each year. realizing a competitive advantage 2010 and 2011 datasets © Massachusetts Institute of Technology Total respondents n = 114410 | ©2012 IBM Corporation
    11. 11. Introduction to big dataRespondents 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%.11 | ©2012 IBM Corporation
    12. 12. Macro findingsThree out of four organizations have big data activitiesunderway; 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 installed, Respondents were asked to describe the state use spreads quickly across the organization of big data activities within their organization. Total respondents n = 1061 Totals do not equal 100% due to rounding12 | ©2012 IBM Corporation
    13. 13. Macro findingsOrganizations are gaining value from working with IBM Grow, retain and 60% Improvement in billed satisfy customers revenue retention rate Increase operational 50% Increase in efficiency inventory turns Transform financial 50% Reduction in processes planning cycle times Manage risk, fraud & 70% regulatory compliance Trading decisions improved with 70% of counterparties | ©2012 IBM Corporation
    14. 14. Key findings| ©2012 IBM Corporation
    15. 15. Key findingsFive key findings highlight how organizations are movingforward 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 value15 | ©2012 IBM Corporation
    16. 16. Key Finding 1: Customer analytics are driving big data initiativesImproving the customer experience by better understandingbehaviors drives almost half of all active big data efforts Big data objectives Customer-centric outcomes  Digital connections have enabled customers to be more vocal about expectations and outcomes  Integrating data increases the ability to create a complete picture of today‟s „empowered consumer‟  Understanding behavior patterns and preferences provides organizations with new ways to engage customers Customer-centric outcomes New business model Other functional objectives Operational optimization Employee collaboration  The ability to connect data and Risk / financial management expand insights for internally Top functional objectives identified by organizations with active big data pilots focused efforts was significantly or implementations. Responses have been weighted and aggregated. less prevalent in current activities Total respondents n = 106116 | ©2012 IBM Corporation
    17. 17. Key Finding 1: Customer analytics are driving big data initiativesCustomer-centric analytics is the primary functionalobjective across macro industry groups, as well Healthcare / Consumer Goods Financial Services Life Sciences 5% 2% 4% 7% 16% 10% 10% 50% 16% Customer- 51% centric 21% 20% 59% outcomes 11% 19% Operational optimization Risk / financial Manufacturing Public Sector management Telecommunications 1% New business 6% 6% 6% model 13% 18% 32% Employee 42% collaboration 13% 27% 11% 62% 8% 26% 30%17 | ©2012 IBM Corporation
    18. 18. Case studySantam Insurance: Predictive analytics improve frauddetection and speed up claims processingSouth Africa‟s largest short-term insurance company uses predictive analytics to uncover a major insurance fraudsyndicate, save millions on fraudulent claims and resolve legitimate claims 70 times faster than before. Solution  Gained the ability to spot fraud early with an advanced analytics solution that:  captures data from incoming claims, assesses each claim against identified risk factors and segments claims to five risk categories, separating higher-risk Business Opportunity cases from low-risk claims  Plans to use propensity modeling to enhance and  Like most insurers around the world, Santam was refine segmentation process as more data becomes losing millions of dollars paying out fraudulent claims every year  Expenses were being passed on to the customer Results in the form of higher premiums and longer waits to settle legitimate claims  Identified a major fraud ring less than 30 days after  To improve its bottom line and enhance customer implementation satisfaction, the company needed to detect and  Saved more than $2.5M in payouts to fraudulent stop insurance fraud early in the claims process customers, and nearly $5M in total repudiations  Reduced claims processing time on low-risk claims by  It also needed to find a way to isolate risky, nearly 90% fraudulent claims so that claims managers could  Cut operating costs by reducing the number of mobile more quickly process lower-risk claims claims investigations18 | ©2012 IBM Corporation
    19. 19. Key Finding 2: Big data is dependent upon a scalable and extensible information foundationBig data efforts are based on a solid, flexible informationmanagement foundation Big data infrastructure Solid information foundation  Integrated, secure and governed data is a foundational requirement for big data  Most organizations that have not started big data efforts lack integrated information stores, security and governance Scalable and extensible  Scalable storage infrastructures enable larger workloads; adoption levels indicate volume is the first big data priority  High-capacity warehouses support Respondents with the variety of data, a close second active big data efforts priority were asked which platform components  A significant percentage of were either currently organizations are currently piloting in pilot or installed Hadoop and NoSQL within their organization. engines, supporting the notion of exponential growth ahead19 | ©2012 IBM Corporation
    20. 20. Key Finding 2: Big data is dependent upon a scalable and extensible information foundationBig data efforts are based on a solid, flexible informationmanagement foundationA holistic and integrated approach to analytics and big data Solutions Enterprise Enterprise Healthcare Healthcare Next Best Next Best Social Media Social Media Fraud Fraud Analytics Analytics Action Action Analytics AnalyticsAn approach that enable organizations to: Analytics and Decision Management • Discover and integrate relevant information Predictive Predictive Content Content Decision Decision Visualization Visualization • Analyze patterns and predict outcomes Analytics Analytics Analytics Analytics Management Management & Discovery & Discovery • Visualize and explore for answers Big Data Platform • Take action and automate processes Content Content Hadoop Hadoop Data Data Stream Stream Management Management System System Warehouse Warehouse Computing • Optimize analytical performance and IT costs Computing • Manage, Govern and Secure Information Information Integration and Governance Big Data Infrastructure: Systems, Storage and Cloud | ©2012 IBM Corporation
    21. 21. Key Finding 3: Initial big data efforts are focused on gaining insights from existing and new sources of internal dataInternal sources of data enable organizations to quicklyramp up big data efforts Big data sources Untapped stores of internal data  Size and scope of some internal data, such as detailed transactions and operational log data, have become too large and varied to manage within traditional systems  New infrastructure components make them accessible for analysis  Some data has been collected, but not analyzed, for years Focus on customer insights  Customers – influenced by digital experiences – often expect information provided to an organization will then be “known” during future interactions Respondents were asked which data  Combining disparate internal sources with sources are currently advanced analytics creates insights into being collected and analyzed as part of customer behavior and preferences active big data efforts  Transactions within their organization.  Emails  Call center interaction records21 | ©2012 IBM Corporation
    22. 22. Case studyVestas: Better data analysis capabilities lower costsand improve effectiveness Vestas Wind Systems A/S optimizes capital investments based on 2.5 petabytes of information and big data technologies Solution  Vestas can now help its customers optimize turbine placement and, as a result, turbine performance.  Uses a big data solution on a supercomputer -- one of the world‟s largest to date -- and a modeling solution to harvest insights from an expanded set of factors including both structured and unstructured data Results Business Opportunity  Insights lead to improved decisions for wind turbine placement and operations, as well as more accurate  Wind turbines are a multimillion dollar power production forecasts investment with a typical lifespan of 20-30 years  Greater business case certainty, quicker results, and  Placement depends upon a large number of increased predictability and reliability location-dependent factors  Vestas has been unable to support data analysis  Decreased cost to customers per kilowatt hour of the very large data sets the company deemed  Reduction by approximately 97 percent – from weeks to necessary for precision turbine placement and hours – of response time for business user requests power forecasting due to inadequate  Greatly improves the effectiveness of turbine placement infrastructure and reliance on external models22 | ©2012 IBM Corporation
    23. 23. Key Finding 4: Big data requires strong analytics capabilitiesStrong analytics capabilities – skills and software – arerequired to create insights and action from big data Analytics capabilities Strong skills and software foundation  Organizations start with a strong core of analytics capabilities, such as query and reporting and data mining, designed to address structured data  Big data efforts require advanced data visualization capabilities as datasets are often too large or complex to analyze and interpret with only traditional tools  Optimization models enable organizations to find the right balance of integration, efficiency and effectiveness in processes Skills gap spans big data  Acquiring and/or developing advanced Respondents were technical and analytic skills required for asked which analytics big data is a challenge for most capabilities were organizations with active efforts underway currently available within their organization to  Both hardware and software skills are analyze big data. needed for big data technologies; it‟s not just a „data scientist‟ gap23 | ©2012 IBM Corporation
    24. 24. Case studyAutomercados Plaza’s: Greater revenue throughgreater insight Automercados Plaza‟s uses data analysis and optimization to gain deeper insights into its customers and generate spectacular gains in sales and the bottom line Solution  Automercados Plaza‟s managers now quickly review daily inventory levels, store sales and cost of goods to see which products are selling and are most profitable, and which promotions are most successful  Enables chain limit losses by scheduling price reductions to move perishable items prior to spoilage  The solution aids in compliance with government price controls on grocery staples  Assists with store location selection Business Opportunity Results  $20M in inventory and more than six terabytes of product and customer data spread across  Increased annual revenues by 30% multiple systems and databases  Increased annual profits by $7M  Unable to easily assess operations at  Decreased time to compile sales tax data by 98% individual stores using manual processes  Lowered losses on perishable goods, which comprise  Needed a comprehensive and timely view of approximately 35% of the chains products operations that would support and improve  Helped executives pinpoint optimal locations for four decisions about business operations new grocery stores24 | ©2012 IBM Corporation
    25. 25. Key Finding 5: The emerging pattern of big data adoption is focused upon delivering measureable business valuePatterns of organizational behavior are consistentacross four stages of big data adoption Big data adoption When segmented into four groups based on current levels of big data activity, respondents showed significant consistency in organizational behaviors Total respondents n = 1061 Totals do not equal 100% due to rounding25 | ©2012 IBM Corporation
    26. 26. Additional FindingsBig data leadership shifts from IT to business asorganizations move through the adoption stages CIOs lead early efforts Leadership shifts  Early stages are driven by CIOs once leadership takes hold to drive exploration  CIOs drive the development of the vision, strategy and approach to big data within most organizations  Groups of business executives usually guide the transition from strategy to proofs of concept or pilots Business executives drive action  Pilot and implementation stages are driven by business executives – either a function-specific executive such as CMO or CFO, or by the CEO Respondents were asked which executive is most closely aligned with  Later stages are more often centered the mandate to use big data within their organization. Box placement reflects the degree to which each executive is dominant in a given stage. on a single executive rather than a group; a single driving force who can Total respondents n = 1028 make things happen is critical26 | ©2012 IBM Corporation
    27. 27. Additional FindingsExecutive desire for quick and precise decisions to keepup with the pace of business drives real-time data needs Speed to insight Reduce the lag  Executives are focused on reducing the time between data intake and its availability within business processes  This lower latency supports the ability to target customer-centric outcomes, but requires a more resilient infrastructure Acceleration anticipated  40% of executives in the Execute stage expect real-time data to be available within processes  The move toward real-time availability will continue to Respondents were asked how quickly business users require data to be increase as the use of machine- available for analysis or within processes. Box placement reflects the to-machine processing and prevalence of that requirements within each a stage. embedded analytics expands Total respondents n = 97327 | ©2012 IBM Corporation
    28. 28. Additional FindingsChallenges evolve as organizations move through thestages, but the business case is a constant hurdle Obstacles to big data State the case  Findings suggest big data activities are being scrutinized for return on investment  A solid business case connects big data technologies to business metrics Getting started  The biggest hurdle for those in the early stages is first understanding how to use big data effectively, and then getting management‟s attention and support  Skills become a constraint once organizations start pilots, suggesting the need to focus on skills during planning  Data quality and veracity only surface Respondents were asked to identify the top obstacles to big data efforts as an obstacle once roll-out within their organization. Responses were weighted and aggregated. Box placement reflects the degree to which each obstacle is dominant in a begins, again suggesting the need for given stage. earlier attention Total respondents n = 97328 | ©2012 IBM Corporation
    29. 29. Recommendations| ©2012 IBM Corporation
    30. 30. RecommendationsAn overarching set of recommendations apply to allorganizations focused on creating value from big data1 2 3 Commit initial Develop Start with existing efforts to drive enterprise-wide data to achieve business value big data blueprint near-term results 4 5 Build analytical Create a business capabilities based on case based on business priorities measurable outcomes30 | ©2012 IBM Corporation
    31. 31. Key Recommendation 1: Commit initial efforts at customer-centric outcomesCustomer analytics creates a high-impact start to big data Customer analytics imperative Customer analytics imperative “I think big data will  Focus initial big data initiatives on areas that significantly impact the can provide the most value to the business business delivery and  Customer analytics enable better service to customers as a result of being able to truly consumer landscape by understand customer needs and anticipate helping service providers future behaviors and retailers better predict Dynamic customer expectations consumer needs and  To effectively cultivate meaningful relationships reduce overall costs with customers, organizations must connect with them in ways their customers perceive as through better supply-chain valuable management, increased  The value may come through more speed in delivery and timely, informed or relevant interactions  Value may also come as organizations improve higher sales” the underlying operations in ways that enhance the overall experience of those interactions – Entertainment /Media executive United States31 | ©2012 IBM Corporation
    32. 32. Key Recommendation 2: Develop enterprise-wide big data blueprintAn enterprise-wide blueprint defines what organizationswant to achieve with big data Components of a blueprint Aligns organization around big data “Currently organizations  Encompasses the vision, strategy and requirements have a lot of data but for big data within an organization  Establishes alignment between the needs of they do not know how to business users and the IT project plan make use of it.  Creates a common understanding of how the Knowledge is right there enterprise intends to use big data to improve its business objectives in the data, but we did not have the tools to Defines the scope of big data explore them until now.  Identifies the key business challenges to which big data will be applied Big data will help convert  Outlines business process requirements that define data into knowledge” how big data will be used  Documents the future architecture – Information Technology  Serves as basis for developing an IT roadmap to implement big data solutions in ways that create executive sustainable business value ISA IOT32 | ©2012 IBM Corporation
    33. 33. Key Recommendation 3: Start with existing data to achieve near-term resultsExisting data offers opportunity to quickly startlearning new tools and technologies with familiar data Focus on internal data Pragmatic approach “Big data will allow  The most logical and cost-effective place to organizations to analyze start looking for new insights is within the enterprise and correlate data from  Looking internally first allows organizations to: their internal processes • Leverage existing data and their business • Use software and skills already in place • Deliver near-term business value environments in a way that • Gain important experience before is not possible today, even adding new data, skills and tools with a lot of business Speed to value intelligence and analytical  Enables organizations to take advantage of tools that already exist” the information stored in existing repositories while infrastructure implementations are underway – Consumer Products executive  As new technologies become available, big Brazil data initiatives can be expanded to include greater volumes and variety of data33 | ©2012 IBM Corporation
    34. 34. Key Recommendation 4: Build analytical capabilities based on business prioritiesAnalytics capabilities include both skills and tools Key capabilities required Addressing the skills gap “I think big data is going to  Organizations face a growing variety of analytics tools while also facing a critical shortage of play a very important role analytical skills in the near future since the  Big data effectiveness hinges on addressing this significant gap dynamics of IT are  In short, organizations will have to invest in changing very quickly. acquiring both tools and skills In coming years, the Proactive development company with the superior  As part of this process, it is expected that new skills will lead to the path roles and career models will emerge for of success and making the individuals with the requisite balance of analytical, functional and IT skills world a better place”  Attention to the professional development and career progression of in-house analysts – who – IT executive are already familiar with the organization‟s United Kingdom unique business processes and challenges – should be a top priority for business executives Harvard Business Review: “Data Scientist” is the sexy new career34 | ©2012 IBM Corporation
    35. 35. Key Recommendation 5: Create a business case based on measurable outcomesBusiness cases must include explicit forecasts of howtechnology investments will impact the bottom line Business case details “I believe big data will force Articulating the case  Many organizations are basing their business companies to re-think their cases on the following benefits that can be structures and business derived from big data: divisions to focus more on  Smarter decisions – Leverage new sources of data to improve the quality of decision making those areas that are most  Faster decisions – Enable more real-time data capture and analysis to support decision making at relevant to the accomplishment the “point of impact” of the strategy and corporate  Decisions that make a difference – Focus big data efforts toward areas that provide true differentiation goals, and not just Secure executive support financial, but also in terms of  An important principle underlies each of these recommendations: business and IT professionals customer satisfaction, product must work together throughout the big data development, research, etc.” journey  Active involvement and sponsorship from one or – Insurance industry executive more business executives throughout this process is needed to advocate for investments Mexico35 | ©2012 IBM Corporation
    36. 36. Recommendations by stage: Educate to ExploreMoving from Educate to Explore is the first critical stepdown the path towards achieving value through big data Educate to Explore: Create a foundation for action  Continue to expand your knowledge by focusing on use cases where big data is providing competitive advantage to organizations, both inside and outside of your Industry.  Work with different business units and functions to identify your most critical business opportunities and challenges that can be addressed with better and more timely information access.  Focus on strengthening your information management environment and infrastructure, including the development of a big data blueprint.  Blueprints are often based on industry standards, reference architectures and other available technical frameworks and resources.36 | ©2012 IBM Corporation
    37. 37. Recommendations by stage: Explore to EngageMoving from Explore to Engage takes organizations fromstrategizing about big data to beginning to realize value Explore to Engage: Put plans into action  Confirm active business leader sponsorship as you develop your big data strategy and roadmap.  Develop the business case for one or two key business opportunities or challenges that you plan to address through POCs or pilot project(s).  While beginning to plan for longer-term requirements, regularly confirm that your information management foundation and IT infrastructure are able to support the big data technologies and capabilities required for the POC or pilot.  Assess your current information governance processes and their readiness to address the new aspects of big data.  Analyze existing skill sets of internal resources, and begin gap analysis of where you need to grow and/or hire additional skills.37 | ©2012 IBM Corporation
    38. 38. Recommendations by stage: Engage to ExecuteMoving from Engage to Execute is a key step to maximizingbusiness value and competitive advantage from big data Engage to Execute: Understand the opportunities and challenges ahead  Actively promote pilot project successes to sustain momentum while beginning to engage other parts of the business.  Finalize the business case with the validation and quantification of projected returns on investment and benefits, including defined success criteria and metrics.  Identify the business process modifications and improvements expected from having access to better and more timely information (for example, marketing, sales, customer service and social media sites).  Develop a competency plan to confirm the availability of adequate technical and quantitative skills that are required to achieve short-term and longer- term objectives.  Document the detailed project plan for migrating pilot(s) into production. This plan should include confirmation of expected business value, costs, resources and project timelines.38 | ©2012 IBM Corporation
    39. 39. Recommendations by stage: ExecuteOrganizations in the Execute stage must continue to expandtheir capabilities to stay ahead of the competition Execute: Embrace the innovation of big data  Document quantifiable outcomes of early successes to bolster future efforts.  Initiate formal big data communications across the organization to continue building support and momentum.  Focus on extending technologies and skills required to address new big data challenges across business units, functions and geographies.  Remain vigilant about information governance (including information lifecycle management), privacy and security.  Continue to evaluate rapidly-evolving big data tools and technologies. Balance existing infrastructure with newer technologies that increase scalability, optimization and resiliency.39 | ©2012 IBM Corporation
    40. 40. Create a business case based on measurable outcomes Key Business Benefits Dealer incentives, Pricing, District based approachBusiness Areas Targeted marketing and increased cross-sell / up-sell1. Sales & Marketing Effectiveness Reduction in subscriber churn through advanced subscriber analytics2. Advanced DataExpansion Filling capacity , Sachet approach on pricing3. Network Optimization Freemium approach,4. Finance Improve network utilization Optimize network capital investments Optimize network operating investments Benchmarking, Spend smart, Financial analysis , post mortem Product Profitability Analysis | ©2012 IBM Corporation COMPANY CONFIDENTIAL
    41. 41. Getting started j| ©2012 IBM Corporation
    42. 42. Getting startedBig data creates the opportunity for real-worldorganizations to extract value from untapped digital assets Focus on measurable business outcomes Take a pragmatic approach, beginning with existing data, tools/technologies, and skills Expand your big data capabilities and efforts across the enterprise Big data: Tapping into new sources of value | ©2012 IBM Corporation
    43. 43. | ©2012 IBM Corporation