Check out our list of free IoT Analytics Software. Products featured on this list are the ones that offer a free trial version. As with most free versions, there are limitations, typically time or features.
If you'd like to see more products and to evaluate additional feature options, compare all IoT Analytics Software to ensure you get the right product.
Datadog is the monitoring, security and analytics platform for developers, IT operations teams, security engineers and business users in the cloud age. The SaaS platform integrates and automates infrastructure monitoring, application performance monitoring and log management to provide unified, real-time observability of our customers' entire technology stack. Datadog is used by organizations of all sizes and across a wide range of industries to enable digital transformation and cloud migration,
Agile analytics and reporting tool, which enables business users to make informed decisions from real-time business data
Smarter. Faster. Integrable There’s only one IoT platform that can take any digitalized system and develop a smarter, faster and integrable application for all your IoT needs. Axonize has developed a disruptive, multi-app architecture, purpose-built for service providers, and end customers, that enables the deployment of fully customized smart solutions across all applications, verticals and device types, while cutting down application development and smart project launch time from 6-18 month
BUILD YOUR INTERNET OF THINGS SOLUTION WITH IoT AT SOFTWARE AG’S VISIONARY INTEGRATION PLATFORM The Internet of Things (IoT) is next-generation integration of every “thing” with the power to transform any business—and it’s easy to see why. By connecting assets and acting on the data they generate, IoT solutions can unlock new revenue streams, improve efficiency and increase customer engagement. So why chance IoT? Go with Cumulocity IoT, the IoT platform that is consistently positioned as a l
Powerful, out-of-the-box IoT analytics, no matter your connected product or platform. Get analytics then dashboards on your IoT data in minutes meaning faster deployments, happy customers and quicker returns from your IoT projects.
IT-OT Integration, Manufacturing Data Engineering, Edge Analytics, Edge Intelligence, and Fog Computing Platform Foghub is an IT-OT Integration, Manufacturing Data Engineering, Edge Analytics, Edge Intelligence, Operational Intelligence and Fog Computing Platform for manufacturing organizations and original equipment manufacturers. Foghub acts as a perfect machine connectivity, data integration and data engineering platform by automating collection & analysis of machine data, offering real
IoT Analytics is the process of analyzing the data generated by industrial equipments to understand effectiveness. Typically, building IoT analytics requires massive data storage and high performance data processing engines with investment of several months period. Fogwing IoT Analytics Studio is simplifying it with the power of Fogwing IIoT Platform and inbuilt data streaming processes. Business users does not require any IoT or IT skills to explore IoT data. The Fogwing IoT Analytics Studio is
IXON Cloud: the only Industrial IoT platform for machine builders, system integrators and building automation. Improve your service. Innovate faster. Maximize your sales. IXON Cloud is the only no-code Remote Access & IIoT solution for machine builders and its customers. Enrich your machines with a cloud platform, fully customizable, for ultimate service and insights. Using IXON's fully integrated cloud gateway (IXrouter) you can access your industrial devices in minutes. Plug & Play
Knowi is a modern business intelligence platform purpose-built for today's modern data enabling enterprises of all sizes to dramatically shorten the distance from raw data to foresight to action. With native integration to virtually any data source, including NoSQL, SQL, RDBMS, file-based and API’s, Knowi eliminates the need for ETL, ODBC drivers, or data transformation processes that alternate solutions require. Data engineers can join structured and unstructured data sources to create blen
RoboMQ is a leading-edge, end-to-end SaaS and Internet of Things (IoT) middleware platform that can connect any device to any system, application or cloud through its suite of connectors and adapters, over any standard protocol. It is a middleware with no protocol of its own but supports all protocols and cross conversion among them. It has an IoT Gateway that allows devices and device mesh networks to connect to the cloud over cellular or wired connectivity. SaaS and Enterprise Application Inte
Data analytics is at the core of the Internet of Things (IoT), alongside process automation and asset management. Equipping machinery, office space, transport vehicles, and other things with smart sensors allows organizations to gather more data and glean more insight than ever before. Additionally, it shows how various assets perform and interact with one another. IoT analytics tools help business leaders unlock actionable insights so they can learn about their operations and how to effectively optimize them from every angle.
For a connected business, there might be hundreds or thousands of advanced data sets to ingest each day. This data might include information about how devices are used, working conditions, and prescriptive analytics. The platforms in this fast-growing category offer methods for visualizing, analyzing, organizing, and exploring real-time and historical data generated by disparate IoT devices. Most businesses collect overwhelming amounts of enterprise IoT data. These solutions help determine the most relevant and actionable insights based on connected applications and their corresponding strategies and goals.
Key Benefits of IoT Analytics Software
Digital transformation is increasingly focused on connected devices, artificial intelligence, and other solutions to make workplaces automated and efficient. Smart devices—from factory machinery to office appliances—can assist with task automation as well as reveal insights about a company, building, employees, and customers. Connected assets help track details related to output, performance, and engagement with software platforms, employees, customers, and other applications connected to a network. IoT analytics solutions empower business users to sort and make sense of these findings. They can learn more about their business operations from the perspective of everyday “things" used across the company, regardless of their cost, size, or function.
Many platforms in this category offer tools to view and convert raw data into shareable formats. In some cases this entails integrations with data visualization software, business intelligence platforms, or other tools used for data analytics. In addition to gathering and preparing valuable data from smart devices, some platforms provide tools for real-time monitoring and reporting, helping users make instant decisions based on momentary events. The real-time streaming of powerful insights helps decision makers adjust and improve processes when these devices are involved, without waiting for lengthy reports.
Thanks to modern edge computing technology, the data gathered on these platforms can be processed and stored on the edge of company networks rather than centralized data warehouses. This helps deliver the right data at a faster rate, without eating into the bandwidth of critical systems. To establish an edge computing scenario for IoT devices and the data they collect, a business must configure edge devices (e.g., routers, integrated access devices or IADs) that control the flow of data. As a company builds out their IoT, analytics platforms help unlock the full potential of these devices without compromising the performance of their assets or IT infrastructures. A number of IoT platforms include analytics solutions or certain reporting features, but dedicated analytics platforms such as those in this category offer deeper insights related to IoT devices, networks, and more related functions in an organization.
The extensive insights from IoT analytics platforms are valuable to everyone in an organization; these insights influence strategic decisions and help the company improve business outcomes. However, only select individuals are typically trained to use these platforms, understand the data, and communicate the findings. The following teams or individuals are the most likely users of IoT analytics tools.
IoT specialists — As the popularity of IoT grows, so does the need for dedicated experts within an organization. New positions such as IoT architect and IoT engineer are prioritized in thousands of tech-forward businesses. Individuals taking focused courses or training on smart technology and its applications may be recruited by an organization to fill an emerging role. In many cases, managers train existing employees so they can take on new responsibilities related to IoT strategy, such as tracking and comprehension of IoT analytics. The exact job titles of these individuals may vary based on the company’s unique approach to the focus area. Internal IoT specialists likely use platforms in this category. These platforms are essential for maximizing the value of IoT investments and making strategic decisions based on smart object activity. If a company designates one or more employees as IoT specialists, the right analytics tool can make a significant impact and convert IoT activity into actionable insights.
Data scientists and analysts — Depending on the size and scope of a company’s IoT infrastructure, they may not designate team members to be purely focused on IoT. In these cases, they may distribute related tasks and responsibilities to different teams or employees. Analytics experts, such as data analysts and data scientists, might be tasked with observing IoT data and determining appropriate responses to these findings. In addition to their existing analytics software and other business tools they use, data experts utilize IoT analytics platforms to observe, sort, and share unique insights generated by smart devices and any asset configured with an IoT sensor. In some cases, these findings are exported to other platforms for further studying, storing, or sharing. An IoT analytics tool can be essential for consuming the continuous stream of data connected devices produce, such as time-series data and streaming data from critical equipment on a factory line. Additionally, these tools assist with modeling and blending unique data sets for optimal analysis.
IoT development firms — Internet of things developers, or IoT developers, are agencies that specialize in designing and deploying smart applications to use in an organization. These experts offer custom manufacturing of IoT objects and help configure new IoT networks. When working with one of these agencies, a business may need additional assistance with testing, troubleshooting, and tracking devices and IoT activity. IoT development teams might leverage a data analytics platform to visualize the findings of connected objects at any point in the customer experience, so customers can yield desired results with their IoT strategies.
The diverse platforms in this category offer a unique set of tools to assist with IoT data analytics. The following are primary features common in this category of software.
Data models and customization — IoT analytics solutions often come with data models for organizing and standardizing information generated by connected devices. Data modeling is useful for revealing relationships between large sets of unorganized data so users can draw conclusions. With some platforms, users can customize data models or configure entirely new models to fit their particular needs. Models may be useful for observing logical relationships within data sets, and determining how data is retrieved, stored, and formatted.
Ingestion and filtering — IoT sensors enable objects to generate limitless data; this increases based on the portfolio of devices in the network. IoT analytics software usually include filtering and ingestion tools, allowing users to collect the most relevant data points. When determining how data is ingested from IoT devices, users can decide whether specific types of data will be used immediately or filed away for later use. In some cases, users can create dashboards for real-time data streaming including location and what settings are most beneficial at the time.
Event scheduling and alerts — Along with determining which data should be collected, users of IoT analytics tools can determine when to generate reports. Scheduling analytics readings could revolve around a specific time schedule, or particular events. Users might track IoT data in response to alerts such as environmental changes or equipment issues. In other cases, they may simply want to schedule data ingestion for a particular time to make basic observations about patterns and performance. Companies can elect to pull data in a variety of ways and adjust their analytics strategy over the course of their IoT campaign. The platforms in this category offer a number of configurations for reporting to suit these needs.
Data gaps — In addition to taking systems offline at unexpected and inconvenient times, random lapses in connectivity cause inconsistencies in time-series data. For example, you may notice several hours between two data points where there would normally be a steady, uninterrupted time line of data. These random gaps can be a source of frustration when it comes to studying and drawing conclusions. To prevent this, IT experts should monitor edge networks and routers and proactively address any issues.
False or corrupted readings — The more end points a company adds to its IoT stack, the greater the potential for transmission issues from an individual sensor. This is an unfortunate risk of any new technology, when it comes to IoT, these possibilities are multiplied by the number of devices they enable. A false reading can happen for a number of reasons, inaccurate data point can corrupt the integrity of data sets. It’s important to perform audits on data and run as many tests as possible to quickly identify issues, before problematic devices contribute additional false readings. Regular software updates are critical to keep distributed smart devices updated, reducing the chances of incomplete or inaccurate data.