There is immense value to be unlocked in using data to analyze and optimize massive industries such as construction, supply chain and logistics, healthcare, manufacturing, agriculture, and energy production. However, you must first collect rich, comprehensive, and ideally real-time data in order to conduct analyses or make predictions.
A common phrase in data analysis is “garbage in, garbage out.” The quality of the “data in” is a major factor in the success of any AI/ML project. Even if you build a great model or algorithm, the value of the results depends on the quality and validity of the data fed into it. Hence, any successful AI/ML project must begin with a sound data collection foundation.
It stands to reason that many compelling AI/ML applications are within ecosystems that are already rich with data—for instance, MarTech, FinTech, Commerce, fraud detection, and cybersecurity to name a few. On the other hand, many legacy industries and workflows centered in the physical world lag behind the tech sector in their data infrastructure. Most of this is due to the inherent lack of digital infrastructure. In addition to a deluge of unmanageable data, the digital infrastructure of the physical world also suffers from limited interoperability across data siloes.
The Internet of Things (IoT) unlocks real world data
IoT devices can automate real time data collection from physical environments, assets, and instruments. Applied AI/ML solutions focused on these physical-world industries have the potential to unlock major value for very large markets. These solutions start by using IoT—the Internet of Things or, simply put, connected devices—to source rich data sets from conventionally offline and manually monitored physical assets.
IoT devices can digitize and automate the tracking of a logistics fleet and its managed goods to enable the building of models that optimize its operations. They can sit at the end of each step in a production line to collect data related to quality to automate the QA process. They can monitor environmental factors like soil quality, atmospheric variables, and weather conditions to feed models that help farmers understand how to optimize their crop yield. And as we’ve previously discussed, wearables can collect data on a person’s vitals and warn when signs of a health issue occur.
Leveraging the connected device / IoT layer for data collection paves the way for transformative tech and AI/ML solutions to be built across offline industries. IoT devices are constantly improving to allow more advanced use cases. There is IoT tech focused on vision, audio, environmental readings, asset tracking, and motion detection. Each new IoT advancement allows the applied AI/ML layer above it to create next-gen solutions.
Moreover, certain AI/ML solutions built on IoT can greatly alleviate human workload as they learn to analyze data and take the appropriate next action. Also, as the system learns what qualifies as significant and important data, it will be more efficient in processing and storing data as it ignores unnecessary or insignificant data points. Finally, there’s increased tailwinds for all of this being done at the IoT device level as 5G empowers new use cases.
Omega is Investing in IoT-empowered AI/ML Solutions
This intersection of IoT and applied AI/ML is an area we are monitoring closely at Omega Venture Partners. We have brought this thesis to life in our portfolio via our recent investment in Elemental Machines.
Elemental Machines was founded by a serial entrepreneur, whose previous wearables company Misfit was acquired by Fossil Group, and who has considerable expertise in building scalable technology solutions that blend hardware with software. Elemental Machines is a pioneer in digitizing the operations of high-value physical spaces, starting with life sciences labs. The company’s plug and play IoT devices connect with a wide array of lab equipment, disparate life sciences software applications, facilities sensors, and they collect data on the environment of the lab itself. This all feeds their insights dashboard which creates a single, actionable view of an entire lab’s worth of data.
Simply put, Elemental Machines’ Sensory Network gathers and synthesizes environmental data into actionable insights. This includes the ability to track contextual variables such as temperature, humidity, air pressure, and light levels as well as monitoring critical equipment performance such as that of freezers, refrigerators, ovens, and incubators.
All this enables clients to have a data-driven, real-time understanding of complex processes, which helps them refine and accelerate their work across all phases of product innovation and manufacturing. Clients are able to automate a lot of the QA/QC process and take prophylactic measures based on automated alerts. Doing so also allows lab operators to make data-informed decisions around asset management and investment as they begin to quantify the actual utility of each asset.
The application of IoT-enabled AI solutions for Lifesciences, and the digitization of high-value laboratory operations to generate actionable insights represents a valuable and substantial market. Omega is pleased to back Elemental Machines as they modernize and optimize lab operations across the large and growing life sciences industry.
Why does this matter?
IoT powered AI/ML solutions have the ability to unlock a new wave of digital innovations that merge offline data with online analytics. The industries most poised to benefit are often also the ones that most directly effect the general population’s everyday life.
Simply put, the convergence of IoT data with AI solutions empowers businesses with valuable insights. It increases the value of data and algorithms and improves the quality and strength of AI outputs. Connected IoT endpoints coupled with AI solutions also reduce application complexity costs by facilitating cross-platform and cross-industry data portability. Improving data management efficiency and increasing the amount of usable data available to AI systems unlocks significant value.
Data-driven innovation across physical-world industries is good for both the businesses that adopt these solutions as well as the downstream consumers who rely on their services. Through these initiatives, we believe that the costs of these industries will be lowered, and the efficiency, accessibility, and quality of their services will be improved.
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