Analytics: The Real-world Use of Big Data


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UPDATE: Register now to participate in the 2013 survey: IBM’s Institute for Business Value (IBV) and the University of Oxford released their information-rich and insightful report “Analytics: The real-world use of big data.” Based on a survey of over 1000 professionals from 100 countries across 25+ industries, the report provides insights into organizations’ top business objectives, where they are in their big data journey, and how they are advancing their big data efforts. It also provides a pragmatic set of recommendations to organizations as they proceed down the path of big data. For additional information, including links to a podcast with one of the lead researchers and a link to download the full report, visit

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Analytics: The Real-world Use of Big Data

  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 data ©2012 IBM Corporation
  2. Agenda 1 Macro Findings 2 Key Findings 3 Recommendations2 | ©2012 IBM Corporation
  3. Macro findings| ©2012 IBM Corporation
  4. 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. 4 | ©2012 IBM Corporation
  5. Introduction to big dataBig data embodies new data characteristics created bytoday’s digitized marketplace Characteristics of big data 5 | ©2012 IBM Corporation
  6. 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 = 1144 6 | ©2012 IBM Corporation
  7. 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%. 7 | ©2012 IBM Corporation
  8. 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 rounding 8 | ©2012 IBM Corporation
  9. Key findings| ©2012 IBM Corporation
  10. 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 value10 | ©2012 IBM Corporation
  11. 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 activities11 | ©2012 IBM Corporation
  12. 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%12 | ©2012 IBM Corporation
  13. Key Finding 2: Big data is dependent upon a scalable and extensible information foundationBig data efforts are based on a solid, flexible informationmanagement foundation Solid information foundation Big data infrastructure 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 engines, within their organization. supporting the notion of exponential growth ahead13 | ©2012 IBM Corporation
  14. 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 records14 | ©2012 IBM Corporation
  15. 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 technical and analytic skills required for Respondents were big data is a challenge for most asked which analytics organizations with active efforts underway capabilities were currently available within Both hardware and software skills are their organization to needed for big data technologies; it’s not analyze big data. just a ‘data scientist’ gap15 | ©2012 IBM Corporation
  16. 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 = 1061 Totals do not equal 100% due to rounding16 | ©2012 IBM Corporation
  17. 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 = 1028 make things happen is critical17 | ©2012 IBM Corporation
  18. 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 begins, within their organization. Responses were weighted and aggregated. Box placement reflects the degree to which each obstacle is dominant in a again suggesting the need for earlier given stage. attention Total respondents = 97318 | ©2012 IBM Corporation
  19. Recommendations| ©2012 IBM Corporation
  20. 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 outcomes20 | ©2012 IBM Corporation
  21. 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 value21 | ©2012 IBM Corporation
  22. Download the study and access additional resources Listen to a podcast on this study| ©2012 IBM Corporation