Vectors to the Rescue: Uncovering New Streams of Business Insight and Action in IoT Data

IoT devices keep generating new data. A vector database helps companies capture, analyze, and act on that data in new ways.

Today's methods of managing IoT data are often too slow, imprecise, and incomplete. The conventional approach is to collect data from sensors with an IoT gateway, transport them wirelessly, store them in a data lake or NoSQL database, clean and normalize the data, and use a dashboard to visualize the results.

But merely looking at snapshots of what’s apparent on the surface of IoT data is only the beginning. Insights are hidden inside these sensor data streams and like salmon swimming in a stream, the direction, speed, and pace of change matter. New insights can be uncovered by applying streaming data science and generative AI.

That’s why vector databases, IoT sensors, and generative AI are a match made in database heaven. Here’s how to leverage them together.

Uncovering Hidden Insights in IoT Data

According to Gartner, 70 percent of successful organizations will make decisions with IoT data by 2026. In The Rise Of Vector Databases, Forbes writer Adrian Bridgewater explains how generative AI powers vector databases to perform advanced analytics such as similarity search and anomaly detection to reveal whether current conditions are similar to previous scenarios when failures occurred.

A vector numerically represents high-dimensional data such as text, images, audio, video, or sensor readings. For example, imagine we wanted to classify the temperature and pressure readings values that tend to lead to failure for industrial equipment on a grid. These encodings succinctly capture the relationships between entities in space, time, or other characteristics as vectors. 

By comparing the similarity of current conditions to the space above based on streaming, real-time data from sensors, vector databases can predict that conditions are trending toward the red zone and suggest actions, like whether to perform predictive maintenance on a device.

For example, the latest wind turbines are complex machines full of sensors. They collect data on wind speed, temperature, vibration, and power output, which can be analyzed to optimize performance and reduce maintenance costs. Some have 1,500 sensors in the rotor blades alone. According to GE Renewable Energy, a typical modern wind turbine can generate up to 500GB of data daily. That’s a lot of data blowing in the wind.

Like salmon swimming in a stream, vectors help you understand the direction, timing, and intensity of signals concealed in sensor data streams. This knowledge helps improve output and device efficiency, predict maintenance requirements before system breakdowns occur, and even forecast real-time trends that may result in catastrophic failure.

It does the bear no good to see a snapshot of where the salmon were yesterday – she must anticipate where they’ll be next. So, direction, speed, and pace of change matter just as much, if not more, than the current state of the world.

Vector databases help you capture, summarize and organize this type of streaming data. Once encoded as vectors, it’s effortless and fast to ask and answer questions like “How have sensor readings varied in the last 24 hours, and are there any patterns in those events that point to a failure we should be concerned about?” or “How is the power generation of this wind turbine doing compared to its performance during similar weather conditions in the last 3 years?” 

In our increasingly connected world, generative AI needs to respond efficiently and rapidly to questions raised by a stream of IoT data. Vector-based insights help to anticipate what is happening next in this stream, enabling quicker and more accurate responses.

And, when generative AI is used to compute a prediction based on that streaming data, it, too, can be stored along with the raw device data. By storing predictions alongside the data used to anticipate them, analysts can better determine imminent failure and “rewind the tape” to see which conditions led to that prediction. 

Many vector databases make integrating time-series data, AI predictions, and vectors easy with conventional data stores like data warehouses or relational stores. When all sources of insight are organized in the same place, time, space, and sequence becomes easier to analyze and understand.

Beyond Static Data Streams: A World in Motion

Wind turbines adjust to changes in the weather, but the weather moves far more slowly than many business scenarios that rely on true real-time inputs.  Sensors in driverless vehicles, automated trading, or cybersecurity applications demand vector computation and analysis closer to real-time. Since time is fleeting and the window to extract insights can be small, some vector databases have implemented techniques like micro batching to consume data at blazing speed with low latency.

Real-time vector databases filter, aggregate, reduce, and store data in “chunks” or batches to handle large throughput levels. For example, an industrial equipment monitoring application might configure the vector database to identify, capture, and organize error codes in batches that match its use into “buckets” of vectors. 

Not only does this batching of data increase throughput, but it organizes data for super-fast recall by optimizing its storage. Answers can now be answered at the right time, in the right context, with the best insight.

Real-Time Vector Database Use Cases

KX CEO Ashok Reddy wrote in Forbes that real-time IoT observability is a rapidly emerging use case. For example, vector databases help track where, when, and why assets move to help companies track the location and status of assets such as inventory, equipment, and vehicles using real-time sensors and GPS, satellite, or drone location data. This helps optimize asset utilization, reduce theft and loss, and improve supply chain efficiency. 

Intelligent devices like AirTags, wearable devices like fitness trackers, health monitors, bikes, scooters, and other connected products emit data that can be used to understand usage patterns, usability flaws, safety, and sentiment. Vectorized context can help optimize the customer experience, improve product design, and drive new revenue opportunities. 

Finally, modern vehicles are loaded with embedded sensors that track engine performance, fuel efficiency, braking patterns, and driver behavior from cars to trains to planes. This data can be used to improve vehicle performance, diagnose issues, and enhance safety features.

As more of the connected world gets and stays in motion, vector databases will continue to rise in primacy as the way you understand and act in the context of time, space, and momentum. 

Vector Databases Help You Be the Bear!

More “salmon” are coming. The number of Internet of Things (IoT) devices worldwide is forecast to triple from 9.7 billion in 2020 to almost 30 billion IoT devices by 2030. Applications that combine IoT data, AI, and vector data management technology will help companies gain critical real-time insights from massive volumes of time series data generated by connected devices. 

These insights will go a long way toward optimizing asset management, automating industrial processes, improving environmental conditions, and enhancing the connected customer experience. 

Will you be the salmon or the bear? In an increasingly connected world, streaming data, generative AI, and vector databases help you rise to the top of your industry’s food chain.