MapR's John Schroeder on Big Data Trends for 2016
What lies ahead on the Big Data front? MapR CEO and Cofounder, John Schroeder is out with a set of very interesting predictions on that topic. According to John, we are in the midst of the biggest change in enterprise computing in decades. How data is stored, analyzed and processed is transforming businesses, he says. Businesses are being revolutionized and new ones are being formed based on converged data and analytics across industries, from insurance carriers basing premiums on actual behavior and specific risk profiles, to healthcare providers improving personalized treatments.
Schroeder sees an acceleration in big data deployments, and has five major predictions for 2016, found in this post, in Shroeder's own words:
Converged Approaches Become Mainstream
For the last few decades, the accepted best practice has been to keep operational and analytic systems separate, in order to prevent analytic workloads from disrupting operational processing. HTAP (Hybrid Transaction / Analytical Processing) was coined in early 2014 by Gartner to describe a new generation of data platforms that can perform both online transaction processing (OLTP) and online analytical processing (OLAP) without requiring data duplication. In 2016, we will see converged approaches become mainstream as leading companies reap the benefits of combining production workloads with analytics to adjust quickly to changing customer preferences, competitive pressures, and business conditions. This convergence speeds the “data to action” cycle for organizations and removes the time lag between analytics and business impact.
The Pendulum Swings from Centralized to Distributed
Tech cycles have swung back and forth from centralized to distributed workloads. Big data solutions initially focused on centralized data lakes that reduced data duplication, simplified management and supported a variety of applications including customer 360 analysis. However, in 2016, large organizations will increasingly move to distributed processing for big data to address the challenges of managing multiple devices, multiple data centers, multiple global use cases and changing overseas data security rules (safe harbor). The continued growth of Internet of Things (IoT), cheap IoT sensors, fast networks, and edge processing will further dictate the deployment of distributed processing frameworks.
Storage (Particularly Flash) Becomes an Extremely Abundant Resource
Next-generation, software-based storage technology is enabling multi-temperature (fast and dense) solutions. Flash memory is a key technology that will enable new design for products in the consumer, computer and enterprise markets. Consumer demand for flash will continue to drive down its cost, and flash deployments in big data will begin to deploy. The optimal solution will combine flash and disk to support both fast and dense configurations. In 2016, this new generation of software-based storage that enables multi-temperature solutions will proliferate so organizations will not have to choose between fast and dense—they will be able to get both.
“Shiny Object Syndrome” Gives Way to Increased Focus on Fundamental Value
In 2016, the market will focus much less on the latest and greatest “shiny object” software downloads, and more on proven technologies that provide fundamental business value. New community innovations will continue to garner attention, but in 2016, companies will increasingly recognize the attraction of software that results in business impact, rather than focusing on raw big data technologies.
Markets Experience a Flight to Quality
In terms of big data technology companies, investors and organizations will turn away from volatile companies that have frequently pivoted in their business models. Instead, they will turn to focus on more secure options – those companies that have both a proven business model and technology innovations that enable improved business outcomes and operational efficiencies.