6 Big Data Predictions for 2017

6 Big Data Predictions for 2017

6 Big Data Predictions for 2017

The market has evolved from technologists looking to learn and understand new Big Data technologies to customers who want to learn about new projects, new companies and most importantly, how organizations are actually benefitting from the technology.  According to John Schroeder, executive chairman and founder of MapR Technologies, Inc., the acceleration in Big Data deployments has shifted the focus to the value of the data. 

John has crystallized his view of market trends into these six major predictions for 2017: 

Artificial Intelligence is Back in VogueIn 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. Why is AI a rejuvenated trend? The three V’s come to mind: Velocity, Variety and Volume. Platforms that can process the three V’s with modern and traditional processing models that scale horizontally providing 10-20X cost efficiency over traditional platforms. Google has documented how simple algorithms executed frequently against large datasets yield better results than other approaches using smaller sets. 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.

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Big Data for Governance or Competitive AdvantageIn 2017, the governance vs. data value tug of war will be front and center. 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. 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 SwampsIn 2017 organizations will shift from the “build it and they will come” data lake approach to a business-driven data approach. Today’s world requires analytics and operational capabilities to address customers, process claims and interface to devices in real time at an individual level. For example any ecommerce site must provide individualized recommendations and price checks in real time. Healthcare organizations must process valid claims and block fraudulent claims by combining analytics with operational systems.

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Data Science Congress 2017

5
Jun
2017
Data Science Congress 2017

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AI Paris

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Jun
2017
AI Paris

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Chief Data Officer Summit San Francisco

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2017
Chief Data Officer Summit San Francisco

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Customer Analytics Innovation Summit Chicago

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Customer Analytics Innovation Summit Chicago

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HR & Workforce Analytics Innovation Summit 2017 London

12
Jun
2017
HR & Workforce Analytics Innovation Summit 2017 London

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