What are IoT Analytics Platforms?

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 platforms 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 Platforms Synthesize data generated by a network of smart devices and IoT sensors Maximize ROI for diverse connected platforms across an organization Shape operational strategies using prescriptive and predictive analytics Identify the most relevant insights for data analytics and translate them into visualizations for easy consumption and analysis Make noteworthy data points from IoT networks searchable and accessible for future reference

Why Use IoT Analytics Platforms?

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.

Who Uses IoT Analytics Platforms?

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 platforms. 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 platform 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 platforms 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.

Page 2 | Best IoT Analytics Platforms: User Reviews from September 2026

Oracle Internet of Things Cloud

Oracle offers the broadest choice of cloud solutions to simplify IT and power innovation.

Average Rating: 3.9/5.0

Total Reviews: 18

How Do G2 Users Rate Oracle Internet of Things Cloud?

Ease of Use: 7.8/10 (Category avg: 8.9/10)
Quality of Support: 7.1/10 (Category avg: 8.9/10)

Who Is the Company Behind Oracle Internet of Things Cloud?

Seller: Oracle
Year Founded: 1977
HQ Location: Austin, TX
Twitter: @Oracle
827,997 Twitter followers
LinkedIn® Page: www.linkedin.com
207,576 employees on LinkedIn®
Ownership: NYSE:ORCL

Who Uses This Product?

Top Industries: Information Technology and Services
Company Size: 39% Large, 39% Small

What Are Recent G2 Reviews of Oracle Internet of Things Cloud?

"Creating POC to share run data analytics on data collected from motion sensors deployed at homes."

Rating: 4.5/5.0 stars

— Ehtisham M.

Best IoT Analytics Platforms - Page 2

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Category Stats (Sep 2026)

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