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From smart buildings and city infrastructure to laundry machines that tell you when you’re low on detergent, IoT-connected devices represent the next wave of digital transformation. Given that Gartner has estimated that as many as 3.1 billion IoT devices may already be in use by businesses, it’s safe to say that IoT is here to stay.

And therein lies the problem: it is this quantity of data that poses the largest problem for those looking to leverage IoT to the fullest. Capturing and integrating data is the easy part— storing it and turning it into actionable insights is the challenge. This is evidenced in the Forbes Insights survey, which found that over a third of respondents were collecting large amounts of data, but were unable to leverage it.

This article will discuss SDS and IaaS as data infrastructure solutions to IoT data storage, and will introduce machine learning as a solution to analyzing IoT data.

Software Defied Storage and Infrastructure-as-a-Service for IoT

Two strategies that companies are adopting to meet the data demands of IoT are Software Defined Storage (SDS) and Infrastructures-as-a-Service (IaaS) public cloud options.

The most apparent benefit of SDS and IaaS is scale. Given the tremendous amount of data IoT systems are designed to capture, scalability is key to IoT success. This is where SDS comes in. SDS is hardware-agnostic, making it unrestricted by arbitrary capacity limits. Additionally, the SDS technologies usually offer file and object storage, making them ideal for managing the typically unstructured data produced by IoT devices. Similarly, IaaS by definition provides the ability to scale data storage at whatever rate desired.

Closely linked to scalability is hardware flexibility. While SDS architectures provide consistent data access, underlying hardware is continuously changing. The hardware-agnostic nature of SDS means that you can leverage new hardware capabilities without waiting for a full system refresh. In a technology landscape where digital transformation has become a race, access to the fastest memory and processing available is crucial to remaining competitive. The ability to swap in new hardware easily also enables greater agility and speed in deploying new data capacity.

Scalability, of course, comes with a cost. This is part of what makes IaaS so attractive. Using a pay-as-you-go model, IaaS lets you pay for only what you consume, reducing infrastructure costs. Most SDS technology uses a similar model. Another benefit of pay-as-you-go is the ability to deal with unpredictable demand, letting you consume infrastructure quicker while optimizing for cost.

It’s important to note that pay-as-you-go does not by default equate to cheaper infrastructure. All costs associated with IaaS and SDS, such as for data egress, must be considered when architecting any data solution. Nevertheless, SDS and IaaS should be among the first options considered when looking to modernize your data storage infrastructure for IoT.

Gathering Actionable Insights with Machine Learning

Traditional statistical models, while they have their place, often fall short when applied to IoT data. This is because IoT data is remarkably dynamic, with variables that change over time or shift to accommodate a desired outcome such as improving efficiency. Additionally, much of IoT data is left unstructured, which can make it challenging to determine which factors and variables to begin measuring.

For these reasons, companies are turning to machine learning to turn IoT data into actionable insights. Machine learning can be used to spot patterns and identify the key variables between them, automatically creating and refining statistical models. These models can then be used for simulations or to make business decisions. As new data comes in, the machine learning algorithm improves itself over time—it “learns”. This makes it possible to perform analysis on data that would be otherwise impossible due to manpower restrictions.

How does it work? Machine learning leverages an approach called neural networking. A Neural network is a network of nodes that takes data from a variety of inputs with which it is trained to produce a desired outcome. Neural networking encompasses deep learning, a form of neural network with hidden layers between inputs and outputs. An example of machine learning in action can be seen in IBM’s Watson Visual Recognition Service.

Have questions now? Get in touch with Hanu to discuss how your IoT initiatives can begin driving marketing initiatives today.