Big data and analytics trends in 2017: James Kobielus’s predictions

Big data and analytics trends in 2017: James Kobielus’s predictions

Big data and analytics trends in 2017: James Kobielus’s predictions

Every December, I publish my predictions of data industry trends for the upcoming year. Here are my forecasts for trends in Big Data analytics, data science, predictive business and cognitive computing in 2017. I’ve also included a few looks back at the year now ending and some long-range projections into the coming decades.

I didn’t see Hadoop declining as rapidly as it did from the Big Data platforms landscape. Hadoop is still a mainstay of unstructured data acquisition, transformation, cleansing and queryable archiving, but its core components—MapReduce for massively parallel data processing and HDFS for data storage—are conspicuous by their absence from newer platforms. This trend is most visible in IBM’s newly launched Watson Data Platform, which uses Apache Spark instead of MapReduce and distributed object storage in lieu of HDFS. Although we’ll continue to see WDP and other next-generation cloud data platforms source data from Hadoop clusters, it’s clear that Spark is the centerpiece of the new cloud data services platform that’s geared to accelerating the productivity of development teams working on data science projects.

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We’re going to see mass deployment of a new generation of optimized neural chipsets, GPUs and other high-performance cognitive computing architectures. These increasingly miniaturized components will be the foundation for most new cognitive mobile, cognitive IoT and cognitive cloud applications that come to market this year and beyond. As low-cost, low-power cognitive hardware platforms appear, more consumer products will incorporate them at attractive price points for mass adoption. Over-the-air or remote distribution of machine learning and other algorithmic artifacts, as well as security patches and updates, will become the standard approach. Cognitive algorithms will be compressed to run on hardware platforms that are resource-constrained, small-footprint, low-power and intermittently connected.

People who can design AI-powered products that combine robotics, embodied cognition, IoT fog computing, deep learning, predictive analytics, emotion analytics, geospatial contextualization, conversational engagement and wearable form factors will be in hot demand. We are at the start of an amazing period of mind-blowing innovation in the consumer and industrial economy. Every human artifact is being retrofitted with AI capabilities or being designed from the ground up either to function as intelligent assistants or to handle many chores autonomously that their owners can’t or prefer not to do themselves. 

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Purely premises-based data platforms are vanishing as more organizations begin to rely completely on public cloud services such as Watson. Beyond that, it’s hard to identify an older approach to standing up data management and analytics that’s in danger of disappearing in 2017.

 



Data Science Congress 2017

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