Analytics: The Real-world Use of Big Data

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UPDATE: Register now to participate in the 2013 survey: http://ibm.com/2013bigdatasurvey 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 http://ibm.co/RB14V0

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Analytics: The Real-world Use of Big Data

  1. 1. ©2012 IBM Corporation Analytics: The real-world use of big data How innovative enterprises extract value from uncertain data Findings from the research collaboration of IBM Institute for Business Value and Saïd Business School, University of Oxford
  2. 2. ©2012 IBM Corporation| Agenda 2 Key Findings2 Macro Findings1 Recommendations3
  3. 3. ©2012 IBM Corporation| Macro findings
  4. 4. ©2012 IBM Corporation| IBM Institute for Business Value and the Saïd Business School partnered to benchmark global big data activities 4 Study overview 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 IBM Institute for Business Value 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. www.ibm.com/2012bigdatastudy
  5. 5. ©2012 IBM Corporation| Big data embodies new data characteristics created by today’s digitized marketplace Introduction to big data Characteristics of big data 5
  6. 6. ©2012 IBM Corporation| Nearly two out of three respondents reports realizing a competitive advantage from information and analytics Macro findings Total respondents n = 11442010 and 2011 datasets © Massachusetts Institute of Technology Realizing a competitive advantage Respondents were asked “To what extent does the use of information (including big data) and analytics create a competitive advantage for your organization in your industry or market.” Respondent percentages shown are for those who rated the extent a [4 ] or [5 Significant extent]. The same question has been asked each year. Competitive advantage enabler A majority of respondents reported analytics and information (including big data) creates a competitive advantage within their market or industry Represents a 70% increase since 2010 Organizations already active in big data activities were 15% more likely to report a competitive advantage A higher-than-average percentage of respondents in Latin America, India/SE Asia and ANZ reported realizing a competitive advantage 63% 58% 37% 2012 2011 2010 70% increase 6
  7. 7. ©2012 IBM Corporation| Respondents define big data by the opportunities it creates Introduction to 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 Defining big data 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
  8. 8. ©2012 IBM Corporation| Three out of four organizations have big data activities underway; and one in four are either in pilot or production Macro findings Total respondents n = 1061 Totals do not equal 100% due to rounding Big data activities Respondents were asked to describe the state of big data activities within their organization. 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, use spreads quickly across the organization 8
  9. 9. ©2012 IBM Corporation| Key findings
  10. 10. ©2012 IBM Corporation| Five key findings highlight how organizations are moving forward with big data Key findings Big data is dependent upon a scalable and extensible information foundation2 The emerging pattern of big data adoption is focused upon delivering measureable business value5 Customer analytics are driving big data initiatives1 Big data requires strong analytics capabilities4 Initial big data efforts are focused on gaining insights from existing and new sources of internal data3 10
  11. 11. ©2012 IBM Corporation| Improving the customer experience by better understanding behaviors drives almost half of all active big data efforts Key Finding 1: Customer analytics are driving big data initiatives 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 Other functional objectives The ability to connect data and expand insights for internally focused efforts was significantly less prevalent in current activities Big data objectives Top functional objectives identified by organizations with active big data pilots or implementations. Responses have been weighted and aggregated. Customer-centric outcomes Operational optimization Risk / financial management New business model Employee collaboration 11
  12. 12. ©2012 IBM Corporation| Customer-centric analytics is the primary functional objective across macro industry groups, as well 50% 11% 21% 16% 2% 42% 26% 13% 13% 6% 59%20% 10% 7% 5% 51% 19% 16% 10% 4% 62% 8% 11% 18% 1% 32% 30% 27% 6% 6% Consumer Goods Financial Services Healthcare / Life Sciences Manufacturing Public Sector Telecommunications Key Finding 1: Customer analytics are driving big data initiatives Customer- centric outcomes Operational optimization Risk / financial management New business model Employee collaboration 12
  13. 13. ©2012 IBM Corporation| Big data efforts are based on a solid, flexible information management foundation Key Finding 2: Big data is dependent upon a scalable and extensible information foundation 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 the variety of data, a close second priority A significant percentage of organizations are currently piloting Hadoop and NoSQL engines, supporting the notion of exponential growth ahead Big data infrastructure 13 Respondents with active big data efforts were asked which platform components were either currently in pilot or installed within their organization.
  14. 14. ©2012 IBM Corporation| Internal sources of data enable organizations to quickly ramp up big data efforts Key Finding 3: Initial big data efforts are focused on gaining insights from existing and new sources of internal data 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 Combining disparate internal sources with advanced analytics creates insights into customer behavior and preferences Transactions Emails Call center interaction records Big data sources Respondents were asked which data sources are currently being collected and analyzed as part of active big data efforts within their organization. 14
  15. 15. ©2012 IBM Corporation| Strong analytics capabilities – skills and software – are required to create insights and action from big data Key Finding 4: Big data requires strong 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 big data is a challenge for most organizations with active efforts underway Both hardware and software skills are needed for big data technologies; it’s not just a ‘data scientist’ gap Analytics capabilities Respondents were asked which analytics capabilities were currently available within their organization to analyze big data. 15
  16. 16. ©2012 IBM Corporation| Patterns of organizational behavior are consistent across four stages of big data adoption Key Finding 5: The emerging pattern of big data adoption is focused upon delivering measureable business value 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 rounding 16
  17. 17. ©2012 IBM Corporation| Big data leadership shifts from IT to business as organizations move through the adoption stages Additional Findings CIOs lead early efforts 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 Later stages are more often centered on a single executive rather than a group; a single driving force who can make things happen is critical Leadership shifts Respondents were asked which executive is most closely aligned with the mandate to use big data within their organization. Box placement reflects the degree to which each executive is dominant in a given stage. Total respondents = 1028 17
  18. 18. ©2012 IBM Corporation| Challenges evolve as organizations move through the stages, but the business case is a constant hurdle Additional Findings 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 as an obstacle once roll-out begins, again suggesting the need for earlier attention Obstacles to big data Respondents were asked to identify the top obstacles to big data efforts within their organization. Responses were weighted and aggregated. Box placement reflects the degree to which each obstacle is dominant in a given stage. Total respondents = 973 18
  19. 19. ©2012 IBM Corporation| Recommendations
  20. 20. ©2012 IBM Corporation| An overarching set of recommendations apply to all organizations focused on creating value from big data Commit initial efforts to drive business value Build analytical capabilities based on business priorities 1 Develop enterprise-wide big data blueprint Create a business case based on measurable outcomes Start with existing data to achieve near-term results 4 2 3 5 Recommendations 20
  21. 21. ©2012 IBM Corporation| Big data creates the opportunity for real-world organizations 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 Getting started 21 Big data: Tapping into new sources of value
  22. 22. ©2012 IBM Corporation| www.ibm.com/2012bigdatastudy Download the study and access additional resources Listen to a podcast on this study
  23. 23. ©2012 IBM Corporation| Take the 2013 survey!

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