15 data and analytics trends that will dominate 2017



Along with social, mobile and cloud, analytics and associated data technologies have earned a place as one of the core disruptors of the digital age. 2016 saw big data technologies increasingly leveraged to power business intelligence. Here's what 2017 holds in store for the data and analytics space.

John Schroeder, executive chairman and founder of MapR Technologies, predicts the following six trends will dominate data and analytics in 2017:

  • Artificial intelligence (AI) is back in vogue.
  • In the 1960s, Ray Solomonoff laid the foundations of a mathematical theory of AI, introducing universal Bayesian methods for inductive inference and prediction. In 1980 the First National Conference of the American Association for Artificial Intelligence (AAAI) was held at Stanford and marked the application of theories in software. AI is now back in mainstream discussions and the umbrella buzzword for machine intelligence, machine learning, neural networks and cognitive computing, Schroeder says. Why is AI a rejuvenated trend? Schroeder points to the three Vs often used to define big data: Velocity, Variety and Volume.
    Platforms that can process the three Vs with modern and traditional processing models that scale horizontally provide 10-20X cost efficiency over traditional platforms, he says. Google has documented how simple algorithms executed frequently against large datasets yield better results than other approaches using smaller sets. Schroeder says we'll see the highest value from applying AI to high volume repetitive tasks where consistency is more effective than gaining human intuitive oversight at the expense of human error and cost.
  • Big data for governance or competitive advantage. In 2017, the governance vs. data value tug of war will be front and center, Schroeder says. Enterprises have a wealth of information about their customers and partners. Leading organizations will manage their data between regulated and non-regulated use cases. Regulated use cases data require governance; data quality and lineage so a regulatory body can report and track data through all transformations to originating source. Schroeder says this is mandatory and necessary but limiting for non-regulatory use cases like customer 360 or offer serving where higher cardinality, real-time and a mix of structured and unstructured yields more effective results.
  • Companies focus on business- driven applications to avoid data lakes from becoming swamps. In 2017 organizations will shift from the "build it and they will come" data lake approach to a business-driven data approach, Schroeder says. 

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