November 2014

Multiple sponsors

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The second annual EMA / 9sight Big Data online survey addressed 259 business intelligence and data management professionals, including almost 600 projects, and was designed to identify the key trends surrounding the adoption, expectations and challenges connected to Big Data. This report summarizes the findings. 

Abstract

The 2013 EMA/9sight Big Data research makes a clear case for the maturation of Big Data as a critical approach for innovative companies. This year’s survey went beyond simple questions of strategy, adoption and use to explore why and how companies are utilizing Big Data. This year’s findings show an increased level of Big Data sophistication between 2012 and 2013 respondents. An improved understanding of the “domains of data” drives this increased sophistication and maturity. Highly developed use of Process-mediated, Machine-generated and Human-sourced information is prevalent throughout this year’s study.

The 2013 study dives deep into the Big Data project initiatives of EMA/9sight respondents focusing on multiple characteristics within each. These 259 respondents, averaging between two and three projects in their Big Data programs, provided information on nearly 600 ongoing Big Data efforts. Over 50% of these projects have an implementation stage of In Operation – In Production or Implemented as a Pilot. Respondents indicated that the top three business challenges were associated with Risk Management activities, Ad-Hoc Operational queries, and Asset Optimization operations. These projects provide groundbreaking detail information into not just the strategy of Big Data implementations, but also the details on implementation choices: on-premises vs. cloud; project sponsors throughout the organization specifically outside the office of the CIO; and actual implementation stages.

Speed of Processing Response has replaced Online Archiving as the top Big Data use case in the 2013 study. This shows that organizational strategies are moving from discovering “the things we don’t know we don’t know” into managing Big Data initiatives toward achievable business objectives and “the things we know we don’t know.” That being said, many of the individual projects being implemented are still using an Online Archiving use case. Speed of Processing Response and Online Archiving are the two most popular uses cases in projects classified as In Operation indicating that these use cases are critical to early Big Data adopters.

Respondents in the 2013 survey indicated that the information consumers (users) of these Big Data projects are coming from the less technical ranks of their companies. Approximately 50% of users were from business backgrounds with Line of Business Executives and Business Analysts representing the top two responses. This shows that Big Data projects are moving beyond Data Scientist as the primary user of these projects. When examining the sponsors of Big Data projects, business is not only using the information results from these systems, but also “putting their money where their users are.” Nearly 50% of all Big Data projects are sponsored by business organizations such as finance, marketing and sales. Just over two of ten Big Data projects were sponsored directly by the CIO.

Integrating Big Data initiatives into the fabric of everyday business operations is growing in importance. The types of projects being implemented overwhelmingly favor Operational Analytics. Operational Analytics workloads are the integration of advanced analytics such as customer segmentation, predictive analytics and graph analysis into operational workflows to provide real-time enhancements to business processes. An excellent example of Operational Analytics can be found as organizations move toward the real-time provisioning of goods and services. It is critical to provide visibility into AND action regarding illicit activities among customers. In addition, risk assessments become more important as businesses use value-based decisions to determine courses of action to pursue new customers and/or to retain existing ones.

In summary, the world of Big Data is maturing at a dramatic pace and supporting many of the project activities, information users and financial sponsors that were once the domain of traditional structured data management projects. It is possible that within the next three to five years, Big Data will have fully absorbed those traditional approaches into a new world driven by a more open and dynamic set of data best practices.