Monnit Data Architect Encourages Organizations to Capitalize on IoT Data in Electronics Update Magazine

“The current and unimagined future value of IoT data lies at the intersection of must-have data management strategies and how they counter common data management problems.”

—Brandon Young, Monnit Vice President of Information Systems

Brandon Young shared his insights about “How to Create an Agile Data Management System” in the June 2021 issue of Electronics Update Magazine. He answers important questions about how businesses can develop “a clear vision of making IoT data work for them to proactively pivot with our evolving connected world.”

  • How can you create value from the large volume of IoT data?
  • What open or compatible data resources do businesses need?
  • What are some best practices to overcoming IoT data management challenges?
  • What to look for in an ideal IoT data management system?
  • How can organizations build a data-driven future?

Here’s an excerpt from the article:

Data’s Abundance Naturally Creates DataOps Challenges

There’s a double-edged sword to the volume of data. On one side, too much data can be unwieldy. It may be challenging to gain real-time visibility into what’s going on in your organization. You can’t see the forest for the trees. But on the other side, if you have the open or compatible resources to compute through your data, you can pull out actionable insights that offer real value to your business.

You can overcome the potentially overwhelming weight from large amounts of data through best practices that handle data’s gravity. In this way, volume produces value. Meaning as data volume grows, it adds or attracts applications to create value from the data. The applications then add more data volume leading to greater insight.

Ultimately, the greater the data volume, the more value it has. This is why you need a secure, scalable, and stable IoT infrastructure to handle the data influx and its management. Not having an agile IoT data infrastructure—processing, management, and storage systems—is a significant challenge to achieving the highest data value.

Build a Data-Driven Future

It’s been a minute since we just used file storage. We moved from relying solely on relational and adding in NoSQL (not only SQL) databases. NoSQL is an answer to Big Data because we can store more and access data faster with these databases. They’re also lighter on the resources with large data sets.

But today, we see more and more edge computing to prefilter data before it goes to the cloud. We’re expanding capabilities to run a hybrid IoT data management model depending upon the industry application for the foreseeable future. We’ll continue to handle, store, and analyze the raw data in the cloud and preprocessed edge data.

It’s harder to do at the edge, but cloud data set cross-pollination also powers current data management practices. If you run an open, compatible system, you can quickly access, integrate, and analyze disparate data sources in one data management solution. For example, you can combine your smart building’s security, energy, occupancy, traffic, IT, maintenance, staffing, and more system data sets for a comprehensive view of your operations.

Moving forward, we’ll want to continually cross-pollinate, simplify data system integrations, and mix in machine learning (ML) and artificial intelligence (AI) centrally and at the edge—all to provide preemptive maintenance of things and streamline the spectrum of processes. The benefits of this actionable intelligence are exponential in greater efficiency and cost-effectiveness.

Read the full article here (page 35; page 20 of the pdf) and see how to “Take Advantage of the Future of IoT Cloud Data Management Today.”