analytics: the real-world use of big data
DESCRIPTION
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/RB14V0TRANSCRIPT
©2012 IBM Corporation
Analytics: The real-world use of big dataHow innovative enterprises extract value from uncertain data
Findings from the research collaboration of IBM Institute for Business Value andSaïd Business School, University of Oxford
©2012 IBM Corporation|
Agenda
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Key Findings2
Macro Findings1
Recommendations3
©2012 IBM Corporation|
Macro findings
©2012 IBM Corporation|
IBM Institute for Business Value and the Saïd Business School partnered to benchmark global big data activities
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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
©2012 IBM Corporation|
Big data embodies new data characteristics created by today’s digitized marketplace
Introduction to big data
Characteristics of big data
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©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 enablerA 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
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©2012 IBM Corporation|
Respondents define big data by the opportunities it creates
Introduction to big data
Greater scope of informationIntegration creates cross-enterprise view External data adds depth to internal data
New kinds of data and analysisNew sources of information generated by pervasive devicesComplex analysis simplified through availability of maturing tools
Real-time information streamingDigital feeds from sensors, social and syndicated dataInstant 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%.
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©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 = 1061Totals 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 eraAlmost half of all organizations surveyed report active discussions about big data plansBig data has moved out of IT and into business discussions
Getting underwayMore than a quarter of organizations have active big data pilots or implementationsTapping into big data is becoming real
Acceleration aheadThe number of active pilots underway suggests big data implementations will rise exponentially in the next few yearsOnce foundational technologies are installed, use spreads quickly across the organization
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©2012 IBM Corporation|
Key findings
©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
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©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 objectivesThe 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
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©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
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©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 foundationIntegrated, secure and governed data is a foundational requirement for big dataMost 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 priorityHigh-capacity warehouses support the variety of data, a close second priorityA significant percentage of organizations are currently piloting Hadoop and NoSQL engines, supporting the notion of exponential growth ahead
Big data infrastructure
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Respondents with active big data efforts
were asked which platform components were either currently
in pilot or installed within their
organization.
©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 dataSize and scope of some internal data, such as detailed transactions and operational log data, have become too large and varied to manage within traditional systemsNew infrastructure components make them accessible for analysisSome data has been collected, but not analyzed, for years
Focus on customer insightsCustomers – 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
TransactionsEmailsCall 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.
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©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 foundationOrganizations start with a strong core of analytics capabilities, such as query and reporting and data mining, designed to address structured dataBig data efforts require advanced data visualization capabilities as datasets are often too large or complex to analyze and interpret with only traditional toolsOptimization models enable organizations to find the right balance of integration, efficiency and effectiveness in processes
Skills gap spans big dataAcquiring and/or developing advanced technical and analytic skills required for big data is a challenge for most organizations with active efforts underwayBoth 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.
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©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
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©2012 IBM Corporation|
Big data leadership shifts from IT to business as organizations move through the adoption stages
Additional Findings
CIOs lead early effortsEarly stages are driven by CIOs once leadership takes hold to drive explorationCIOs drive the development of the vision, strategy and approach to big data within most organizationsGroups of business executives usually guide the transition from strategy to proofs of concept or pilots
Business executives drive actionPilot and implementation stages are driven by business executives – either a function-specific executive such as CMO or CFO, or by the CEOLater 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
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©2012 IBM Corporation|
Challenges evolve as organizations move through the stages, but the business case is a constant hurdle
Additional Findings
State the caseFindings suggest big data activities are being scrutinized for return on investmentA solid business case connects big data technologies to business metrics
Getting startedThe biggest hurdle for those in the early stages is first understanding how to use big data effectively, and then getting management’s attention and supportSkills become a constraint once organizations start pilots, suggesting the need to focus on skills during planningData 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
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©2012 IBM Corporation|
Recommendations
©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
1Develop
enterprise-wide big data blueprint
Create a business case based on
measurable outcomes
Start with existing data to achieve
near-term results
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2 3
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Recommendations
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©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
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Big data: Tapping into new sources of value
©2012 IBM Corporation|
www.ibm.com/2012bigdatastudy
Download the study and access additional resources
Listen to a podcast on this study
©2012 IBM Corporation|
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