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Unlocking the potential of IoT with the intelligent edge Posted on : Jul 09 - 2018

Big Data: A back-burner discussion?

One of the things we encounter most when speaking to IoT developers is, while they are often focused on getting their product to market, they aren’t thinking long-term about Big Data strategy.

This is for two primary reasons. Often the lead technical resource at new IoT companies has a background in hardware, electrical engineering, robotics, etc. This makes a lot of sense as getting an IoT product, like a robot that picks strawberries or a sensor that monitors vaccine temperature, up and running requires a strong knowledge of hardware and electrical engineering. That said, while often able to code, these folks don’t generally have a background in Big Data architectures and aren’t knowledgeable enough to ensure they have a future-proof, intelligent edge strategy.

Secondly, time to market is very important for startups. They are racing the clock on funding, want to capture their market before their competitors, and have limited resources. As a result, when discussing Big Data architectures with new IoT companies or companies tackling new IoT initiatives, it often becomes a back-burner discussion. “We will tackle that later after we get to market.” In fact, I have even had conversations with CTO’s at IoT companies who are well aware their data infrastructure simply will not support their scale. They are experts in hardware and highly focused on getting their product to market, however, it’s apparent to most outside observers that the data they collect may be more valuable than the function their IoT product enables.

Often folks working on IoT projects, whether at new startups or at more mature companies, do not feel empowered to tackle Big Data architectures from day one. This isn’t surprising as the accepted status quo for Big Data architectures is incredibly daunting and unnecessarily complex.

Eventually, these products mature, gain user adoption, and are deployed widely. At the edge, often a caching layer or a “lite” version of a database product is deployed. This results in a less than “intelligent” edge and an industry-wide problem where 99 percent of IoT data goes unused.

The true promise of IoT

Currently, the true promise of IoT, allowing for real-time actionability of IoT sensor feedback is not in place. This is due to a lack of feedback loop between IoT sensor data and machine learning and predictive analytics. Imagine a facility that monitors quality control of baked goods using image recognition. They analyze these images historically and look for quality issues. View More