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In Lighting the Path to Innovation Blog
authored by What’s in Store for Big Data in 2016

At Sensuron, our fiber optic sensing technologies collect and analyze material and structural data based on changes in the way light moves down a fiber optic cable. Sounds simple enough, however our software has to analyze massive data sets in real-time to extract environmental parameters such as strain, loads, temperature, as well as deflection and 3D shape sensing. Over the past few years, big data has become the buzzword du jour, with countless discussions around how to store it, manage it and perhaps most importantly, make sense of it quickly and efficiently. It’s no question big data has the potential to make an immense impact on our world today. As we look ahead to what the new year holds, here are a few predictions for how we see the big data space evolving:

IoT is becoming a reality

While we’ve been hearing about the Internet of Things for a while, it is rapidly becoming a reality. Andrew Brust, a contributor for ZDNet recently wrote a feature on upcoming big data predictions and noted, “Snehal Antani, CTO at Splunk, predicts that “Industrial IoT will fundamentally disrupt the asset intelligence industry.” Additionally, Suresh Vasudevan, the CEO of Nimble Storage proclaims “in 2016 the IoT invades the datacenter.” That may be, but IoT technologies are far from standardized – with competitors using Bluetooth, Wi-fi and Zigbee for their applications – and that’s a barrier to entry for the datacenter. Maybe that’s why the folks at DataArt say “the IoT industry will [see] a year of competition, as platforms strive for supremacy.”

We agree and with that in mind, industrial IoT is where we see perhaps the most potential. For example, as more systems become increasingly intelligent and interconnected, the energy field in particular will see lots of transformation and new ways of tackling existing problems.

Machine Learning

We anticipate many companies will start to embed machine learning into everyday operations, if they haven’t already done so. However without the right talent and expertise, achieving success with machine learning will be difficult. While data scientists are the new must-have employee in today’s big data economy, we expect more database technologies to pop up that allow organizations to efficiently embrace machine learning without requiring investment in new and expensive talent.

Big Data Matures

For the past few years, big data as a term has been clouded with hype – organizations constantly talk about it however we’ve seen limited headway so far. This year we anticipate the market to mature and we aren’t the only ones. Oracle recently stated, “Perhaps the most interesting and risky set of predictions we’re making are in the realm of the analog. We predict that big data shifts from being merely a technology showcase to a technology embedded deeply into the fabric of our business, social, and political lives. This could be a bumpy transition into maturity; think of these as the awkward teen years.”

Across Sensuron’s primary industries alone – aerospace, medical and energy – the potential of big data is immense. By harnessing data in the medical field we can learn to understand hidden patterns in information, discovering new medicines faster and thereby curing diseases quicker. By examining data patterns in aerospace we can detect plane problems, track them more efficiently and influence future plane designs. And in energy, we can obtain insights such as which rock formations are most prone to damaging drill heads. Regardless of industry, by examining and gathering data on failures and quickly taking into account all relevant factors including time, place, characteristics, etc., we can find patterns over time and fix problems that may have had catastrophic impact further down the line.

One thing is clear – as we figure out to more effectively and efficiently harness big data technologies, it will provide countless opportunities for organizations across a variety of fields. Now that early adopters have tested the waters, we expect to see big data adoption take hold in 2016.

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