Navigating IoT Data Scale Challenges

Jeff Tao
Jeff Tao
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This article was originally published in Forbes Technology Council.

According to Gartner, IT services for the Internet of Things (IoT) market will represent a $58 billion opportunity in 2025. The explosion of IoT has transformed how we collect and analyze data, and the interconnectivity brought by IoT has made everything smarter. Our homes and lives are powered by the likes of smart thermostats, connected cars and wearable devices, while in the world of industry, IoT systems are enabling predictive maintenance of machinery, reducing downtime and increasing productivity for businesses.

And we are only beginning to realize the benefits of our increasingly connected world. With millions of IoT devices generating billions of data points, businesses can access unprecedented insights to drive growth and innovation. But there’s a catch.

We’ve started to see IoT data outgrowing enterprises exponentially, and keeping up is causing strain. For many early adopters of IoT in industries like energy, city management, agriculture and healthcare, scaling has become a significant challenge.

But the good news is that our knowledge is scaling as fast as IoT data. In this article, we’ll explore the challenges of managing IoT data and scale and share tips on how to build an effective data management strategy.

The Challenges

The three key challenges of scaling for IoT are volume, velocity and variety. First, IoT datasets are massive: In 2019, General Electric’s digital business processed over 1.5 billion data elements daily from more than 1.2 million connected assets worldwide. And by 2025, the total data volume of connected IoT devices worldwide is forecast to reach 79.4 zettabytes. The data processing solutions of the past cannot keep up with data at this scale, and modern, purpose-built products are necessary.

Real-time streaming data, as we see in IoT, brings in the challenge of velocity. Real-time insights into trends, operations, performance and customer engagement are critical use cases for IoT data, and if you are hit with data processing delays, there can be opportunities that could be missed.

For example, users of a navigation app like Waze, which uses real-time traffic data, could experience a lag in directions and traffic updates if new data cannot be processed in time. In some cases, this can even be a matter of life or death (e.g., medical devices or critical infrastructure).

Finally, to fully realize the benefits of advanced data analytics, it’s necessary to process data from disparate sources using different protocols or formats. This diversity of data requires specialized tools and technologies to manage and analyze it effectively.

Modern Systems for Modern Data

The fact is that traditional data historians and relational databases just aren’t equipped to handle modern IoT or operational datasets. The data collected by smart devices and industrial equipment today differs from 20 years ago, and it’s unreasonable to expect legacy systems to keep up. Instead, to ensure that your data infrastructure can grow with your business, you need to invest in purpose-built systems that are designed for IoT.

The following are some of the most important factors to consider before deploying new products in your data infrastructure.

  • Performance: With smaller datasets, many products can offer high performance, but it’s essential to test with a dataset similar in size to what you have in production to ensure that performance does not deteriorate and that historical data can be accessed without excessive latency.
  • Scalability: As your business expands, your datasets will only increase in size. Your data systems must be able to grow with you, so make sure that you choose products that can be scaled horizontally to meet future needs.
  • Ecosystem: Closed systems are more challenging to adapt to future technologies and more susceptible to vendor lock-in. For the best results, select open-source systems that integrate easily with the other components of your data pipeline.
  • Ease of Use: Many enterprises need IT staff to develop and support complex systems. Your data systems must be easy to use and offer rich feature sets that do not require a huge stack just to provide basic features.

One Step at a Time

Justifying the cost of scaling for IoT can be a significant concern, especially if your enterprise already has legacy data management systems. A complete migration away from these systems can incur significant costs and downtime and more risks. Remember that digital transformation does not necessarily need to occur simultaneously. Instead, consider systems that can integrate and interact with your legacy historians or other platforms. This way, you can benefit from modern, purpose-built data infrastructure while retaining existing investments and ensuring continuity. Scaling is simpler when you don’t throw out the whole system but find a solution that seamlessly integrates with legacy systems.

Additionally, don’t be afraid of open source. A great benefit of scaling with open-source systems is the advantage of flexibility and innovation, enabling businesses to adopt new tools and technologies to meet their unique needs. The open nature allows a community of developers to contribute to the IoT scaling evolution, resulting in more robust and cutting-edge solutions for managing IoT data. This flexibility and innovation are critical for enterprises looking to stay ahead of the curve in a rapidly evolving landscape.

A Journey, Not a Destination

It’s essential to recognize that scaling IoT data management is ongoing. Enterprises must remain agile and adaptable, continuously evaluating their data management strategies to ensure they remain effective as IoT data’s volume, velocity and variety grow. Regularly reviewing and refining your data management processes will ensure your enterprise can keep pace with the demands of the IoT era.

Scaling for IoT data is not a one-and-done deal; it’s an ongoing process. Businesses must continuously evaluate and optimize their approaches to succeed in this landscape. With a strategic and holistic plan that embraces open-source systems and specialized tools, they can stay ahead of the curve and unlock unprecedented insights, because standing still is not an option in the IoT world.

  • Jeff Tao
    Jeff Tao

    With over three decades of hands-on experience in software development, Jeff has had the privilege of spearheading numerous ventures and initiatives in the tech realm. His passion for open source, technology, and innovation has been the driving force behind his journey.

    As one of the core developers of TDengine, he is deeply committed to pushing the boundaries of time series data platforms. His mission is crystal clear: to architect a high performance, scalable solution in this space and make it accessible, valuable and affordable for everyone, from individual developers and startups to industry giants.