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21 data and analytics trends that will dominate 2016

Thor Olavsrud | Jan. 19, 2016
Five industry insiders predict the trends that will shape the big data and analytics market in 2016.

Dan Graham, general manager of enterprise systems at data warehousing and big data analytics specialist Teradata, predicts that in 2016:

  • Organizations will hit reset on Hadoop. Graham believes 2016 will see enterprises use lessons learned from past deployments to rearchitect their approaches. "As Hadoop and related open source technologies move beyond knowledge gathering and the hype abates, enterprises will hit the reset button on (not abandon) their Hadoop deployments to address lessons learned — particularly around governance, data integration, security and reliability," he says.
  • Algorithms will enter the boardroom. "Algorithms heat up in the data ingest and preparation processes for house holding and profiling," he says. "As a result, CEOs and Investors will start talking deep analytics as core business goals."
  • Data lakes will finally discover a few killer apps. Data lakes will be the most common repository for staging raw IoT data, driven by volume and costs, Graham says. "The size of IoT M2M data will over run in-memory capacity by orders of magnitude, driving implementers to data lake technologies for low cost storage," he says.
  • IoT data captured at the data center will erode in value faster than transaction data. "Lacking monetary data fields, most sensor data will become low value in hours, days or weeks as it is replaced by fresh collections of the same sensor data," Graham says. "Architectures and systems will be forced to compensate for this rapid decline to cope with retention and processing costs."


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