Clearly Understanding Real-Time Processing of Data for IoT Applications and It’s Benefits

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The internet of items (IoT) is driving value across nearly every sector. Sectors span from manufacturing and logistics to retail and resource management, and the IoT is capturing data from a network of linked”items,” including drones, delivery trucks, medical instruments, safety cameras, and building equipment.

Even though IoT sensors and apparatus accumulate a great deal of valuable insights, they also generate massive, high-speed data streams that are difficult to process, analyze, store, and secure. 

IoT data is also highly perishable, and without the right tools, organizations miss chances to act on time-sensitive insights having the most potential.

Here, we will discuss how real-time data analytics and IoT applications come together to create new opportunities across a wide range of businesses.

What Does Real-Time Data Processing Mean for IoT Applications?

As IoT adoption continues to rise, organizations from every business struggle to maintain these massive datasets expanding at exponential rates. As a point of reference, IoT apparatus and sensors can capture gigabytes of data within a few hours–and that’s before you consider the data coming from the CRM, social media channels, financial reports, and so forth.

At the same time, big data analytics, and AI & machine learning are evolving at a break-neck pace. By applying AI to IoT data management and analytics, organizations can quickly pull valuable information from these massive, heterogeneous data sets and respond to real-time conditions. Together, these technologies are driving game-changing innovations.

For example, big data’s inherent characteristics (aka the 4Vs) are perfect for”training” AI and ML applications fast.

Those intelligent applications can then be utilized to automate processes, predict equipment failures, detect security threats–in real-time. In the case of fully-autonomous solutions, AI takes the wheel, relying on a connected network of IoT devices to guide the way.

With significant gains in autonomous driving at all levels, real-time analytics can support drivers with safety features like automatic braking, parking, and collision avoidance by transmitting data.

When there are endless examples of what AI, advanced analytics, and the IoT can accomplish, they can’t deliver on these promises without the right tools.

Real-Time Insights Depend on Powerful Computing

The majority of the IoT platforms in use today were designed to connect the various devices within a system and merge and process data streams from several heterogeneous sources.

These platforms often address many of the challenges IoT gifts like storage, security, and interoperability and can integrate with data analytics solutions to provide valuable business insights. However, because most data analytics solutions use a cloud computing architecture called Platform as a Service (PaaS), real-time data processing isn’t possible.

According to a current Dell report, using cloud-based systems to procedure IoT data has several limitations, including security risks, latency, and missed opportunities to act on powerful, real-time data.

Even though IoT data streams themselves capture what’s happening in-the-moment, processing these data streams means sending them into the cloud for off-line analysis and processing, that can then be reviewed at a later moment.

You’re also working inside a system where you are sending information to a distant location at a volume that may exceed network bandwidth and waste storage space and computing power on unusable insights.

The report found that while just 29% of participating firms have incorporated edge computing in their analytics strategies, 69% of respondents agreed that prioritizing edge for processing IoT data would assist them achieve their primary business goals.

However, it is worth noting that advantage computing alone won’t unlock the door to real-time data analytics.

Technologies such as 5G and WiFi6, IoT platforms like Kaa and AWS, event-driven architectures, and analytics programs such as Kafka, Kinesis, Spark, Storm, Cassandra, and BigTable, designed for processing continuous streams are converging to enable real-time large data analytics.

The Convergence of IoT and Big Data Analytics

The convergence of IoT, big data, and AI-driven analytics introduces a number of new opportunities for companies to create more competitive business models.

According to Forrester’s 2020 Predictions, enterprise strategy is getting to be a critical initiative for driving digital transformation. 

While the report cites that interest in large data has waned over the past few years, innovations in AI and machine learning are driving renewed fascination with big data–as they introduce new opportunities to process data and put it to good use.

At the same time, we’re seeing more affordable hardware, software, and sensors, as well as emerging standards and best practices driving IoT adoption. As such, there’s a rapidly rising number of connected”objects” capturing constant data streams (including audio, video, and images) and metrics that measure machine functions, environmental conditions, and much more.

The Essential Use of Big Data Analytics in IoT

While the Web of Things and Big Data are two distinct concepts, they’re becoming increasingly interconnected.

In the IoT, you have got a massive network of sensors that collect an unprecedented amount of data from a variety of sources feeding into the broader big data landscape. Here is an example that will help you get a clearer idea of just how much data, none of these devices could accumulate.

The Oura Ring is a device that is worn on a user’s finger and tracks the user’s sleep, temperature, and physical activity. The system captures data at a rate of 250 times per minute.

This data can contain matters such as customer usage insights, opinion analysis, sales metrics, and behavioral patterns–among hundreds of other data sources. Together, Big Data and IoT create contextual insights that can be applied to enhance products, services, and procedures –and in turn, generate more revenue.

Big data analytics platforms hold the key to unlocking this information from taking unstructured IoT data–about say, foot traffic at a theme park, weather patterns, or patient health--and analyzing that information alongside other data sources to supply a holistic view of the situation. From there, platforms organize that information into digestible insights that companies can utilize to optimize their processes.

This means that environmental data from sensors, surveillance footage, log files, and geo-location data can combine forces with social media and consumer behavior insights, to create a better understanding of your audience–bringing them to life in a way that marketing metrics can’t provide on their own.

Deriving Value from IoT Data

IoT and Big Data analytics are not any longer stand-ins representing the promising use cases of tomorrow; they’re rapidly emerging as essential resources for staying aggressive right now.

Big IoT data analytics gives organizations the ability to extract value from IoT sensors and methods by analyzing IoT data with existing business tools and third-party data collections to bring more contextual information to the fold.

Then, the information can be applied to create improved products, services, and adventures. However, organizations will need to make certain that they have the infrastructure in place to encourage real-time data processing at scale to get the full value from their investments.

Hardware and Software Design Leader in 4i Platform.
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