Top IoT Data Analytics Platforms

Much of the data generated by the more than 20 billion things Gartner predicts will be connected to the internet by next year will be funneled through IoT data analytics platforms. To derive value from the insight that can be generated from connected devices, many enterprises turn specifically to cloud-based solutions.

Cloud computing, analysts said, is best suited for storage, scale and speed when it comes to the large workloads and gigabytes of IoT data in play. And given the hundreds of available IoT platforms, it’s best for an organization to deal with a vendor or service provider with experience in its own industry, said Christian Renaud, research vice president of IoT at 451 Research.

“Customers are buying outcomes; they want rapid time to value,” he said. “You need someone who understands your market and verticals.

“And you need to identify what analytics you need and what insight you want.”

If 90% of businesses expect data-driven insight to become a key differentiator by the end of this year, according to a Forrester study, they need cloud-scale help.

Practical considerations around providing connectivity to remote locations, plus a general suspicion about the security, capability and trustworthiness of public cloud providers, are largely gone, according to Forrester. And with fewer businesses in the IoT space investing in building their own networks of data centers, the public cloud is the place to be.

While companies engaging in IoT projects in the early days simply needed monitoring capabilities, there’s now a demand for analytics, machine learning and AI.

“Vendors must bake analytics, insight and action deeply into their platform offerings to support predictive maintenance, machine-learning-powered workload optimization and scheduling, and more,” according to Forrester research.

Before you buy, industry analysts advise sampling the IoT data analytics platforms to see how well they handle your use cases, how easily they can be configured to various IoT and business apps and how access is controlled.

At a minimum, the analytics functions should handle:

• Descriptive analytics for what’s happening at any given time.
• Predictive analytics to prevent unwanted and costly downtime.
• Prescriptive analytics to help answer questions about ROI, new business models and ways to maximize output and efficiency.

A critical aspect of any IoT platform in general is its ability to manage the volumes of data generated and provide users with the ability to integrate actionable results, according to 451 Research.

“That includes not only working with the data generated by the devices in the IoT network, but also being able to integrate data streams from other sources to create context and meaning for richer results,” according to 451’s IoT platform selection guide. “Too often, IoT data is considered in isolation. While it has intrinsic value, it is far more powerful for an orga­nization when it is blended with data from the rest of the enterprise.”

The IoT data analytics platform should ingest structured, unstructured and time-series data automatically; process it; make intelligent decisions in real time; and then automate the decisions, industry analysts said. Some platforms offer a mixture of prebuilt tools to allow their customers to create their own business-specific analytics, and also support off-the-shelf solutions.

Pricing differs by vendor, with many shifting from fixed prices to metered or pay-per-use outcome-based models. Gartner said it sees new delivery models, moving from system integration to insights as a service. Gartner research also estimates that over half of data and analytics services will be performed by machines instead of humans by 2022.

To help readers make a choice among IoT data analytics platforms, we compiled a list of the top products in this space. Our list focuses on vendor offerings related to cloud-based IoT data analytics rather than IoT data analytics vendors in general. To make the list, the vendors had dedicated IoT tools and at least some support for industrial IoT analytics applications, which represent a significant volume of overall IoT applications. Those that had overall strong analytics capabilities but less of a focus on the industrial market did not make the list. We also took into consideration high rankings from analysts who specialize in IoT verticals.

The top IoT data analytics tools listed below possess most if not all of the following key traits:

• The ability to handle massive data volume sets generated by IoT devices and SCADA systems.
• The ability to not only handle the volume but the variety of data and at higher velocity.
• Edge and on-premises processing capabilities.
• Security protocols that verify where data flows from, especially if it’s edge-enabled.
• AI and machine learning capabilities through APIs.
• Device management.

Here’s the list of the top 11 (we intended to do 10, but there was a tie) cloud-based data analytics platforms, which is presented alphabetically (requires registration):