The Internet of Things (IoT) is growing at an astonishing rate and is only throttled by the shortage of chips. According to a recent report by market research firm IoT Analytics, there were 12.2 billion active IoT endpoints by 2021. And it is estimated there will be over 29 billion IoT devices connected to the internet by 2030.
This means that businesses will need to prepare for a world where almost everything is connected and be equipped to deal with the deluge of data these devices collect.
One of the most important aspects of this new era is IoT analytics. IoT analytics allows businesses to make sense of all the data being generated by these devices.
What is IoT Analytics Software?
IoT analytics software enables companies to generate insight from the data generated by connected devices. It allows you to track the performance of your machinery, understand unique datasets, and provide predictive maintenance recommendations.
IoT analytics tools also help you prepare, filter, and transform data. This can be helpful in drilling down into specific datasets and understanding the relationships between different data points. By understanding the data generated by IoT devices, you can gain valuable insights into the performance of your business and the health of your machines.
Also see: Best IoT Platforms for Device Management
How to Select IoT Analytics Tools
IoT analytics tools are designed to help organizations make sense of the vast amounts of data being generated by their IoT devices. But with so many options on the market, it can be difficult to know which tool is right for your organization. To help you make a decision, we’ve put together a list of the four must-have features of IoT analytics tools.
Data ingestion
A good IoT analytics tool must be able to consume data directly from internet-connected devices, sensors, and time series databases. This data is typically stored in JSON or CSV format. The tool should also support batch and real-time data ingestion, so you can start analyzing your data as soon as it’s collected.
Data modeling and discovery
The tool should support data modeling, so you can create models of data to understand better what it contains. It should also support data discovery, which allows you to explore data without having to write any code. This is especially important if you’re not a data scientist or engineer.
Interactive visualization
A good IoT analytics tool will allow you to drill down into the data with interactive visualization. This way, you can quickly identify patterns and trends in your data.
Reporting and visualization
The tool should allow you to create reports and visualizations based on the data. These reports and visualizations should be customizable, so you can tailor them to your specific needs.
Also see: IoT in Healthcare
Benefits of IoT Analytics
Real-time data collection
IoT analytics provides the ability to collect data in real time from a variety of devices and sensors. This data can be used to gain insights into how these devices are being used as well as identify patterns and trends.
Improved decision making
IoT analytics can help to improve decision-making by providing insights that would otherwise not be available. One example is data from IoT devices that can be used to predict demand, optimize production processes, or improve customer service.
Reduced costs
IoT analytics can enable cost reduction by reducing the need for manual data collection and analysis. Additionally, IoT analytics can help organizations avoid potential problems before they occur, which can save both time and money.
Increased revenue
IoT analytics can increase revenue by providing insights that can be used to create new products or services or improve existing ones. For example, data from IoT devices can be used to develop new marketing campaigns or target new customer segments.
Improved customer satisfaction
IoT analytics can improve customer satisfaction by providing insights that can be used to improve the customer experience. A good example is data from IoT devices used to identify and resolve issues more quickly or provide personalized recommendations.
Enhanced security
IoT analytics can enhance security by providing insights that can be used to identify potential security threats or vulnerabilities. For example, data from IoT devices used to monitor unusual activity or detect malicious behavior.
Increased competitiveness
IoT analytics can increase competitiveness by providing insights that give organizations a competitive advantage. For instance, data from IoT devices can be used to develop new business models or strategies or enter new markets.
Also see: Guide to IoT Analytics: 7 Key Tips
Top IoT Analytics Providers
We have reviewed five of the top IoT analytics providers, which we believe offer a strong combination of features and benefits for enterprises. For each tool, we provide a brief description, a list of features, pros and cons, and pricing information where available.
AWS IoT Analytics
AWS IoT Analytics is a fully-managed service from Amazon Web Services that enables analysis of massive, sophisticated IoT data without the typical high cost and complexity normally required to build an IoT analytics platform.
Key Features
- Ingest data from any source: AWS IoT Analytics allows you to collect data from any source, including AWS IoT Core, as well as third-party devices and sensors.
- Collect only the data you want to store and analyze: With AWS IoT Analytics, you can filter and select only the data that is relevant for analysis, reducing storage costs and improving efficiency.
- Cleanse, filter, transform, enrich, and reprocess: The service allows for data cleansing, filtering, transformation, enrichment, and reprocessing to ensure data is properly formatted for analysis.
- Time-series data store: AWS IoT Analytics also has a time-series data store, making it easy to analyze temporal patterns in IoT data.
- Run ad hoc or scheduled SQL queries: The service allows ad hoc or scheduled SQL queries to analyze IoT data.
- Hosted notebooks: AWS IoT Analytics also offers hosted notebooks for sophisticated analytics and machine learning.
- QuickSight integration: The service integrates with Amazon QuickSight for visual data analysis and insights.
Pros
- Fully managed service
- Easy to deploy and configure
- Works well off the shelf with straightforward configuration
- User-friendly environment
Cons
- Complex pricing structure
Pricing
AWS IoT Analytics is priced based on the volume of data processing, data storage, query execution, and custom analysis execution.
SAS Analytics for IoT
SAS Analytics for IoT is a powerful tool for businesses to make use of the high volumes of data generated by IoT devices. The platform uses a secure, flexible, and scalable artificial intelligence (AI)-embedded IoT Analytics platform that is easy to use and provides users with the ability to organize, visualize, and act on data quickly and efficiently.
This makes it an ideal solution for businesses of all sizes that want to make the most of their current IoT investments. SAS Analytics for IoT is also compelling for a variety of users, including line of business, engineering, IT, and data science professionals, due to its wide range of capabilities.
By extending the use of analytics and collaboration to the enterprise, SAS Analytics for IoT optimizes current IoT investments and provides businesses with a valuable tool for driving growth.
Key Features
- Streamlined, extensible ETL: The tool includes sensor attributes, device attributes, hierarchies, measures, and events, so you can easily integrate your sensor data with production quality data. In addition, it also includes comprehensive ETL (extract, transform, load) capabilities to enable the rapid integration of additional field data.
- Flexible, sensor-focused data model: SAS Analytics for IoT is a flexible, sensor-focused data model that provides a standardized, extensible sensor-based data model. It integrates real-time and historical data, hierarchies, and other relationships right out of the box.
- Unified, intuitive user interface: The tool features a unified, intuitive user interface for selecting data based on the business context, with no need for SQL or advanced programming skills. It allows for easy drill down and roll up of sensor values to analyze both individual sensors and aggregated data.
- Data profiles & explorations: SAS Analytics for IoT includes data profiles and explorations, which enables users to understand their data at a glance with automatic visualizations quickly. This helps users identify patterns and anomalies, making it easier to uncover insights from their data.
- Launchers: The platform also includes launchers or preconfigured workflows for tasks such as calculating key performance indicators (KPIs), building models, and analyzing sensor data streams. These launchers enable users to analyze their IoT data without advanced programming skills quickly.
- Advanced analytics and machine learning: In addition to basic statistical analysis, SAS Analytics for IoT includes advanced analytics capabilities such as forecasting and predictive modeling. It offers built-in machine learning algorithms for creating models, allowing users to uncover insights from their IoT data quickly.
- Streaming model execution: The tool allows for streaming model execution, enabling users to make real-time decisions based on their IoT data. This helps improve operational efficiency and drive growth in various industries, such as manufacturing and healthcare.
- Public APIs: SAS Analytics for IoT includes public APIs, making it easy to integrate with other applications and extend capabilities.
Pros
- User-friendly with an easy-to-use graphical user interface (GUI)
- Easy debugging
- Flexible and scalable
- Advanced analytics and machine learning capabilities
- Robust data security
- Good customer support
Cons
- Can be expensive for companies on a tight budget
Pricing
The vendor does not publish pricing information. Pricing is available on request, and customers can sign up for a free demo.
Oracle Internet of Things Cloud Service
Oracle IoT Cloud Service helps you connect your devices to the cloud and manage them with ease. With this managed platform as a service (PaaS), Oracle takes care of all of the underlying infrastructure, so you can focus on your data and applications.
Oracle IoT Cloud Service provides real-time data analysis from your IoT devices, allowing you to make better-informed decisions. You can also easily integrate Oracle IoT data with enterprise applications, web services, and other Oracle Cloud services. Oracle’s IoT solution is reliable, scalable, and secure, making it a good choice for businesses of all sizes.
Key Features
- Numerous device connection options: Oracle IoT Cloud Service provides numerous device connection options to make it easy to connect almost any type of device. Devices can be connected with JavaScript, Java, Android, C POSIX, and iOS as well as REST APIs.
- Real-time data processing: The platform allows for real-time data processing, enabling businesses to make immediate decisions based on their IoT data.
- Predictive analytics: Oracle IoT Cloud Service includes predictive analytics, allowing users to predict future events and outcomes based on their IoT data.
- Forecasting: The platform also offers forecasting capabilities, giving users a look into potential future trends based on their IoT data.
- Visualization: Oracle’s IoT solution includes visualization capabilities, enabling users to understand and analyze their data easily.
- Oracle IoT Digital Twin: The platform includes Oracle IoT Digital Twin, which allows for the modeling and simulation of physical assets in a virtual environment. This helps businesses to improve design, maintenance, and operations.
- Integration with external services: The platform enables easy integration with several external services, such as JD Edwards EnterpriseOne IoT Orchestrator and other Oracle enterprise applications, making it easy to connect with other applications and extend capabilities.
- Applications for various solutions: Oracle’s IoT Cloud Service includes applications for various solutions such as asset management, Production Monitoring, Fleet Monitoring, and Connected Worker. This helps companies in various industries improve their operations, increase productivity, and drive growth.
Pros
- Intuitive user interface
- Easy to adopt and learn
- Good documentation
- Vibrant user community
- Great technical support
- Easy to deploy the app
Cons
- Fairly new, so not as large a user base
Pricing
Oracle IoT Cloud Service is available through a pay-as-you-go subscription model. Actual pricing information is not published, but the vendor offers a cost estimator to help customers determine their potential costs. There is also a free trial available for those interested in trying out the platform before purchasing.
TrendMiner
TrendMiner is a sophisticated IoT analytics solution that helps organizations make better use of their big industrial data. The platform is web-based and intuitive, making it easy for users to get up and running quickly. TrendMiner offers a self-service option, allowing users to access the platform without having to go through IT. The platform is available as a SaaS, on-premises, or private cloud solution.
TrendMiner’s plug-and-play feature adds value immediately, and its cross-site collaboration capabilities help organizations improve their overall performance. It is especially suited to process manufacturing companies in chemical, oil and gas, food and beverage, water and wastewater, and other industries where process data is important.
Key Features
- Live monitoring: With TrendMiner, you can access historical data quickly to “live-monitor” current process behavior. Live monitoring does not require the user to keep checking the screen every few minutes; it can be done as a background process.
- HTML5 web interface: TrendMiner’s web interface is built using HTML5, making it easily accessible from any device with a web browser.
- Advanced analytics: The platform includes advanced analytics capabilities, enabling users to easily analyze and visualize their data for better decision-making. Reporting options include embedding a view in a report, printing a relevant chart, creating reports of notifications captured during production, triggering a shift handover report, or downloading periodic loss accounting reports.
- Business intelligence (BI) dashboards: Business intelligence dashboards can improve your business by displaying data such as energy consumption, production waste, or product quality. The dashboard can be specifically tailored to a production line, site, or even your entire business unit. Another useful feature of the BI dashboard is the heat map function, which uses early warnings to prevent issues before they arise.
- Self-service option: The self-service option allows users to access TrendMiner without having to go through IT, improving efficiency and allowing for quicker insights and decision making.
- Plug-and-play feature: The plug-and-play feature helps create immediate value from the platform, as it automatically detects process data sources and connects them to the platform.
- Asset framework support: TrendMiner supports various asset frameworks including OSIsoft PI, Yokogawa FAST/TOOLS, Honeywell PHD, and more. This makes it easy for customers already using these frameworks to integrate with TrendMiner and benefit from its advanced analytics capabilities.
Pros
- Easy to use and adopt
- Ideal for data analytics beginners
- Good customer support
- Advanced analytics capabilities
- Plug-and-play tool
- Self service
Cons
- Limited integrations with other software
Pricing
Finding the price for TrendMiner can be difficult, as the company doesn’t publicly list its pricing. However, you can request pricing from TrendMiner, or even request a demo of the product before making a decision.
ThingSpeak
The name is a portmanteau of “things” and “speak,” an acknowledgment that there are insights in the “Internet of Things” data, if we only listen with the right tools. ThingSpeak is the perfect way to monitor, visualize, and analyze live data streams. You can get data from your devices sent straight to ThingSpeak as well as send alerts.
It also includes MATLAB analytics and visualizations.
Key Features
- Collect data in private channels: ThingSpeak allows you to collect data in private channels, meaning only authorized people have access to the data.
- Share data with public channels: You can also choose to share data with public channels, allowing for collaboration and sharing of ideas and insights.
- RESTful and MQTT APIs: ThingSpeak offers both RESTful and MQTT APIs for easy integration with other software and devices.
- MATLAB analytics and visualizations: ThingSpeak includes MATLAB analytics and visualizations, giving you more options for analyzing data.
- Event scheduling: The event scheduling feature allows you to schedule events such as changing device configuration or triggering actuators.
- Alerts: With ThingSpeak, you can set up alerts for when a certain condition is met, allowing for proactive decision-making.
- App integrations: ThingSpeak offers integrations with various apps and devices such as MATLAB & Simulink, Arduino, Particle devices, ESP8266 and ESP32 Modules, Raspberry Pi, LoRaWAN, Things Network, Senet, Libelium, and Beckhoff.
Pros
- MATLAB analytics and visualizations
- Instant visualizations of data uploaded by devices or equipment
- Easy integration with other software and devices through APIs
- Event scheduling and alerts
- Build an IoT systems without servers setups or web software
Cons
- Limited support for asset framework
Pricing
ThingSpeak is a flexible, extensible platform that allows you to store and analyze data from a variety of sensors and sources. The pricing for ThingSpeak is based on the number of channels required and on a count of messages to be processed and stored in a one-year period.
ThingSpeak capacity is bought in units, where one unit allows 33 million messages to be processed and stored in a one-year period (~90,000 messages/day). One unit, which is the standard license type, costs $675.00 USD per year.
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