Best IoT Analytics Tools

Cloud IoT Core is a fully managed service that allows you to easily and securely connect, manage, and ingest data from millions of globally dispersed devices.

ThingSpeak is an IoT analytics platform service that allows you to aggregate, visualize and analyze live data streams in the cloud. ThingSpeak provides instant visualizations of data posted by your devices to ThingSpeak. With the ability to execute MATLAB® code in ThingSpeak you can perform online analysis and processing of the data as it comes in. ThingSpeak is often used for prototyping and proof of concept IoT systems that require analytics.

AWS IoT Analytics is a fully-managed service that makes it easy to run and operationalize sophisticated analytics on massive volumes of IoT data without having to worry about the cost and complexity typically required to build an IoT analytics platform. It is the easiest way to run analytics on IoT data and get insights to make better and more accurate decisions for IoT applications and machine learning use cases. IoT data is highly unstructured which makes it difficult to analyze with traditional analytics and business intelligence tools that are designed to process structured data. IoT data comes from devices that often record fairly noisy processes (such as temperature, motion, or sound). The data from these devices can frequently have significant gaps, corrupted messages, and false readings that must be cleaned up before analysis can occur. Also, IoT data is often only meaningful in the context of additional, third party data inputs. For example, to help farmers determine when to water their crops, vineyard irrigation systems often enrich moisture sensor data with rainfall data from the vineyard, allowing for more efficient water usage while maximizing harvest yield. AWS IoT Analytics automates each of the difficult steps that are required to analyze data from IoT devices. AWS IoT Analytics filters, transforms, and enriches IoT data before storing it in a time-series data store for analysis. You can setup the service to collect only the data you need from your devices, apply mathematical transforms to process the data, and enrich the data with device-specific metadata such as device type and location before storing the processed data. Then, you can analyze your data by running ad hoc or scheduled queries using the built-in SQL query engine, or perform more complex analytics and machine learning inference. AWS IoT Analytics makes it easy to get started with machine learning by including pre-built models for common IoT use cases. You can also use your own custom analysis, packaged in a container, to execute on AWS IoT Analytics. AWS IoT Analytics automates the execution of your custom analyses created in Jupyter Notebook or your own tools (such as Matlab, Octave, etc.) to be executed on your schedule. AWS IoT Analytics is a fully managed service that operationalizes analyses and scales automatically to support up to petabytes of IoT data. With AWS IoT Analytics, you can analyze data from millions of devices and build fast, responsive IoT applications without managing hardware or infrastructure.

Cumulocity IoT DataHub allows you to connect existing analytics tools and applications to Cumulocity IoT, such as: Business Intelligence and machine learning tools, as well as arbitrary custom applications, and much more. Enable your users to understand why certain events occur, whether these are normal, whether they are likely to happen again, and what was the end result previously, whether positive or negative. Cumulocity IoT DataHub offers you SQL-based access via ODBC/JDBC and REST to your IoT data. Store the large volume of valuable information from sensors and devices for much longer than the usual 14 day period in a much more cost-effective way than previously possible in your data lake. Access all time-series data from your preferred tools and applications. Enable your teams to compare historical data from the same device types across multiple locations, explore, analyze, and act on this data. Use your data efficiently. Understand the past to predict the future and action sophisticated data-driven outcomes. Learn more about Cumulocity DataHub at Data Lake for IoT Analytics | Cumulocity IoT DataHub (softwareag.com) and more on our IoT Platform at IoT Platform | Cumulocity IoT | Software AG

TrendMiner software is based on a high-performance analytics engine for data captured in time series. With TrendMiner, process engineers and operators can easily search for trends and question their process data directly – without help from a data scientist. TrendMiner delivers all you need to analyze, monitor and predict the performance of batch, grade and continuous plants.

SAS Analytics for IoT enables all types of users to transform data into intelligence with an extensible, sensor-based data model and an intuitive, visual interface built on SAS Viya that streamlines ETL tasks.

Altair SmartWorks™ was built for optimal flexibility, from its extensive compatibility with third party hardware, communication technologies, and applications to its innovative licensing and global partner ecosystem.Altair transforms design and decision making by applying simulation, machine learning and optimization throughout product lifecycles. Our broad portfolio of simulation technology and patented units-based software licensing model enable Simulation-Driven Innovation™ for our customers. With more than 2,000 employees, Altair is headquartered in Troy, Michigan, USA and operates 82 offices throughout 25 countries. Altair serves more than 5,000 customers across broad industry segments. 

The IoT Analytics Platform module focuses on delivering measurable business value, using intelligent big data processing and real-time data analytics for M2M/IoT business purposes. It provides thorough information about the way in which customers are using your IoT products. Additionally, it handles some issues related to quality of service (QoS) within operations, by helping discover which device (or type of device) generates issues, revealing data patterns and trends, and providing reports and analyses of anomalies. Device/IMSI (International Mobile Subscriber Identity) or customer profile dashboards enable deep data drill down. You can also use the supporting functions such as alarms and notifications internally, to improve your business and operational results and enhance customer experience using this solution, which enables you to truly unlock the potential of IoT (Internet of Things) analytics data for telecom organizations