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February 2014

Big data deployments: Maximizing their value, minimizing mistakes

Big data projects are becoming more common as companies seeking a competitive edge look to take advantage of an increasing variety of information from both internal and external sources. But deploying, implementing and using big data systems and big data analytics tools can be a complex undertaking.

This e-book will offer practical advice on managing big data deployments to IT, business intelligence and analytics teams and business executives. Individual chapters will focus on evaluating and selecting Hadoop, NoSQL databases and other big data technologies; incorporating sensor data and log files into big data applications; and creating effective analytical models as part of big data programs.


  • Big data buying decisions call for open eyes and minds

    Big data management and analytics programs can provide competitive advantages and help drive increased revenue, but getting started on them -- and not getting caught up in all the big data hype without a solid plan of action -- can be a challenge for unprepared organizations. This e-book chapter will provide IT, business intelligence and analytics managers as well as other readers with information on the current state of big data adoption and advice on how to prepare for and launch projects, including a look at key issues to consider in the big data technology evaluation and selection process.

  • Machine data equals big value for analytics programs

    Sensors capture large volumes of data about the operations of industrial equipment; similarly, log files gather huge amounts of information about computers and IT networks. Behind those exceedingly large volumes of data is real business value. That is, if analytics program directors are willing -- and able -- to look for it.

    This e-book chapter takes a ground-level look at two analytics programs that, while still in their infancy, have taken sensors and log files and effectively joined them together with big data analytics, putting them on the fast track toward greater efficiency and an improved bottom line. The strategies highlighted by those at the helm of such operations arm IT, business intelligence and analytics managers with the insight and advice to effectively incorporate machine data into big data management and analytics programs.

  • Predictive analytics capabilities allow for top-notch big data modeling

    Building effective analytical models is a key facet of big data analytics applications -- though doing so is easier said than done.

    This e-book chapter provides guidance on how to successfully manage the modeling process in big data environments through advice from leaders of analytics teams. The chapter drills down on the iterative nature of developing analytical models and the benefits of data sampling -- using representative portions of data sets to speed model development. And it takes a close look at the ongoing struggle to find analytics professionals with the optimal mix of technical skills and business knowledge to staff modeling efforts and help organizations make the most of their big data investments.


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