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Making sense of IoT-kind data from machines and sensors data comes often with its unique challenges, such as the need for time-series databases in storage, and for relatively deep domain expertise in analysis. These kinds of factors create a certain mismatch with many leading technologies that have been designed for more traditional, “digital-first” analytic environments. This, in turn, is attracting a flurry of startup-level activity aimed at filling the gaps.
According to practice director Dan Shey, “What is remarkable about this market is how much of the innovation actually comes from startups. Take, for instance, ParStream’s geo-distributed architecture, CyberLightning’s 3D visualization technology or Peaxy’s work on software-defined data access. All three address some of the problems that usually come up in discussions with end-users. Meanwhile, of the more incumbent vendors likes of Datawatch, Informatica, Software AG and Splunk seem well-positioned to seize the IoT opportunity.”
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