Technology 4 min read

As Big Data Management Becomes a Priority, IIoT Becomes a Must

Sdecoret |

Sdecoret |

We’ve spent generations gathering data, and now we’re finally beginning to process it with the help of AI. Big Data management enabled by cloud computing and AI are crucial to organizing and storing these massive databases.

The IIoT Revolution

Take just a moment to consider how many of the devices you interact with on a daily basis are connected to the internet (your phone, the gas station pump, ATMs, computers, jet engines…the list goes on).

In fact, the analyst firm Gartner estimates that by 2020, there will be over 26 billion connected devices. With all that data, efficient big data management will have to be developed.

The Internet of Things (IoT) allows consumers to access a seemingly unending number of devices. But, in reality, IoT objects cannot truly be separated from the industry that produces them.

In 2017, this Industrial Internet of Things (IIoT) seems to have finally captured the attention of industry and market leaders.

A recent article by Inside Big Data suggests that “IIoT incorporates machine learning and big data technology to capture and communicate data in real-time in order to identify inefficiencies and avoid costly breakdowns.”

Moreover, at the March 2017 Gartner Data & Analytics Summit analyst Frank Buytendijk claimed that in 2016 alone spending on IoT hardware amounted to a whopping $2.5 million USD every minute.

Cloud Computing for Big Data Management

Traditionally, “the adoption of cloud computing in the healthcare industry has been a slow process due to concerns around security, regulatory and compliance,” says Inside Big Data.

However, in the healthcare field especially, cloud-based machines seems to be offering innovative solutions to current clinical workflow and data collection concerns.

By 2020, over 26 billion devices will be connected to the Internet of Things.Click To Tweet

Cerebrum offers a workflow model for real-time patient monitoring and expedition of the care process through electronic healthcare tools and modules.

CMD sees approximately 200 patients a day across its three clinics. The utilization of the Cerebrum™ has greatly reduced our operational cost and reduced our reporting cycle time, resulting in higher efficiencies and greater revenue retention. Our cardiologists have come to accept and rely on the Cerebrum™ as an industry standard for the office and patient management solution. Indeed our business has benefited from higher efficiency through use of this model and our physicians and patients have benefited from being able to provide prompt and accurate patient care.

Dr. David Neumann, CardioMatters Diagnostics Inc., Toronto, ON

Similarly, Digisight offers a cloud-based workflow model for those institutions who are early adopters of teleophthalmology.

Big Data Analytics

Because cloud computing and IoT rely on massive quantities of data–the ability to process, mine, and visualize these datasets is quickly becoming crucial for industry giants.

Chinese company MeritData recently turned to Intel in the search for new ways to optimize these processes.  By using Intel® Data Analytics Acceleration Library (Intel® DAAL) and Intel® Math Kernel Library (Intel® MKL) Intel and MeritData’s algorithm engineers were able to create significant performance improvements (ranging from 3x all the way to 14x).

Jin Qiang, data mining algorithm architect at MeritData, reports that “The performance―and customer’s experience―is improved significantly [with these processes].”

Master Big Data Management

Inside Big Data suggests that because the technologies we currently rely on are becoming more and more affiliated with data collection, storage, and exchange, both personal devices and industrial machines will soon be vitally connected to the IoT world.

Forrester additionally predicts, IoT will spread across edge and cloud systems while being supported by developing AI and other machine containers.

This means, in essence, both more widespread Wi-Fi availability and new machine learning programs will continue to secure IoT’s growth in the years to come.

How do you think the data we generate now will be used in the future? Do you think the products of a strong IIoT can solve the current halting problem associated with AI?

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