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INFOGRAPHIC: Making #BigData Work

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INFOGRAPHIC: Making #BigData Work

There is little arguing the benefits and disruptive potential of Big Data. However, many organizations have not fully embedded Big Data in their operations. In fact, our research shows that only 13% have achieved full-scale production for their Big Data implementations. The most troubling development is that most organizations are failing to benefit from their investments. Only 27% of respondents described their Big Data initiatives as “successful” and only 8% described them as “very successful”.

There is little arguing the benefits and disruptive potential of Big Data. However, many organizations have not fully embedded Big Data in their operations. In fact, our research shows that only 13% have achieved full-scale production for their Big Data implementations. The most troubling development is that most organizations are failing to benefit from their investments. Only 27% of respondents described their Big Data initiatives as “successful” and only 8% described them as “very successful”.

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INFOGRAPHIC: Making #BigData Work

  1. 1. Making Big Data Work: It pays to be Systematic Nearly 60% of executives agree that Big Data will disrupt their industry in the next three years However, only 13%of organizations have their initiatives in full-scale production of initiatives are “successful”, 27% Only And 8% are “very successful” Most initiatives are yet to take off, despite the buzz… Most organizations are not doing it right… No planned approach do not have a well-defined roadmap, with milestones and timelines, in place 55% either have scattered pockets of resources or follow a decentralized model for analytics initiatives 47% No well-defined criteria do not have well-defined criteria to identify, qualify and select use-cases do not have well-defined success criteria (KPIs) to measure the success of their Big Data initiatives 74% 67% $ Those that are, are tasting success… Well-defined roadmap 51% 22% Yes No Criteria for use-case selection 53% 29% Yes No Well-defined success criteria 51% 28% Yes No 53% Scattered Pockets Ad-hoc, isolated analytics teams 43% 27% 20% Decentralized Separate analytics teams for separate departments Centralized Central team acting as a competence center for Big Data, and coordinating initiatives for all business units Business Unit Analytics team as a distinct profit-making division Comparison of Big Data initiative success rates Lack of a defined organizational structure > > Source : Capgemini Consulting, “Big Data Survey”, November 2014

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