What is IoT Analytics

Businesses use internet of things (IoT) analytics to make decisions based on data from their IoT devices. Companies in industries like transportation, industrial automation, government, healthcare, and consumer tech can operate thousands or even millions of connected devices. Analytics help them collect, store, and process all the data. Without analytics, these companies can’t make informed decisions.

Why use IoT analytics?

The IoT industry is slated to reach $1.29 trillion by 2020 and there are already more IoT devices than there are smartphones. As the sector grows, companies struggle to make sense of the data. The datasets are more complex and are generated in greater volumes than ever before. This new environment demands tools and skill sets that most companies don’t yet have and they often have difficulty adapting without IoT analytics. For example, to take advantage of IoT technology, a transportation and logistics company that traditionally ran on paper forms would need to deploy a data ecosystem to capture large quantities of data. That could include telemetry and operational data, such as the location, fuel usage, and acceleration from thousands of connected vehicles, and unstructured data, from digitized drivers logs. Once the trucking company warehoused all that data, it would have to make it easy to access. That might mean deploying a technology stack full of tools for searching the data, rules for sorting data, and a user interface with dashboards to make it accessible to lay users. But that’s not all. The trucking company would also need to employ people who write code and understand data science well enough to develop hypotheses to answer questions about how the fleet is performing. To simplify things, many companies use IoT analytics. The software performs most of those functions and offers APIs to link all the data sources together. Some platforms include a machine learning algorithm to make suggestions to human users. With IoT analytics, the trucking company’s team could answer questions like:

  • What are the most fuel-efficient routes?
  • Which routes are profitable?
  • How will the weather impact operations?
  • Will increasing the size of our fleet improve productivity?

Because most IoT analytics software offers a user-friendly interface, anyone within the trucking company could log in and get answers. They wouldn’t be reliant on just a few data scientist power users. And because the software is offered on demand, the company could scale it up or down as needed.

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What are the top applications for IoT analytics?

IoT analytics are useful to any company that manages connected devices. These businesses often fall into one of six of use cases:

Industrial automation

Industrial manufacturers use IoT analytics to streamline their factories, which are increasingly automated. The consulting firm ABI estimates that sales of industrial automation robots will triple by 2025 and consulting firm BGC estimates that as much as 40 percent of factory jobs will soon be held by robots. These robots are often networked together and manufacturers are able to use the data to make better decisions. They can rearrange shop floors for greater productivity, reduce anomalies and errors, and decrease the time needed to estimate the cost of new projects—known as costing—to respond more quickly to requests for proposals.  

Transportation

Transportation companies can use IoT analytics to increase profit margins. When tracking vehicles, managers can digitally model fleet operations and test hypothesis. This helps them make recommendations to improve fuel efficiency, avoid traffic, and monitor driver safety. Similarly, public transportation systems can use IoT analytics to better understand ridership patterns to reduce congestion, improve schedules, and, if devices are embedded in infrastructure like roads and overpasses, improve public safety.

Healthcare

Research firm eMarketer predicts that IoT for healthcare–sometimes known as the internet of medical things (IoMT)—will be a $163 billion industry by 2020. Three in four healthcare executives believe the technology will disrupt current healthcare companies, and IoMT data is already available in everything from patient’s personal fitness trackers to pharmaceutical inventory and life support equipment. With IoT analytics, healthcare and insurance providers can connect all these devices to plan with greater certainty, modernize their facilities, cut costs, and improve patient care.

Consumer tech

In the consumer internet of things (CIoT), companies use IoT analytics to mine data with the permission of their users. This includes data from mobile apps, fitness trackers, mobile devices, vehicles, and appliances. The same way that companies can track user behavior online, IoT analytics offers insight into how consumers use products in the physical world. Consumer companies can provide increasingly personalized services and users get a better experience.

Weather

Today, weather companies like The Weather Channel, owned by IBM, consider themselves data companies. They operate millions of sensors around the world to track, analyze, and report on changes in weather patterns in as close to real-time as possible. With IoT analytics, weather companies can analyze data more quickly and provide increasingly accurate predictions.

Government

Governments use IoT devices to increase access to and improve the effectiveness of government services. They monitor buildings and facilities to reduce maintenance costs, track the safety of roads and bridges, and measure utility usage via smart meters. With IoT analytics, governments can use IoT data in combination with machine learning to better predict and serve citizen’s needs.

How to deploy IoT analytics

When evaluating vendors, companies should consider the devices and data sources they’ll need to track to ensure that any potential vendor has APIs that can incorporate them all. An ideal IoT analytics vendor will have a simple, user-friendly interface, already have successful clients in the IoT space, and will provide machine learning features that reduce the burden of analysis so teams can get accurate answers quickly.