Gartner: IoT Analytics ‘Cool Vendors’ Address Things-Specific NeedsGartner: IoT Analytics ‘Cool Vendors’ Address Things-Specific Needs
The four vendors on the Gartner IoT analytics ‘cool vendors’ list offer IoT-specific analytics capabilities, which are useful in handling two important tasks unique to IoT environments.
June 23, 2017
To better realize value from IoT projects, organizations should begin to target specific domain use cases with IoT analytics–like an increase of equipment life span, asset optimization, predictive maintenance of devices, anomaly detection in the manufacturing processes or finding new product usage patterns–according to a recent Gartner IoT-related report profiling “cool” vendors in the space.
Gartner named four “cool vendors in Internet of Things analytics” that focus on some of the hottest areas of IoT–such as visibility into the manufacturing process; enabling new analytics users; and device diagnostics, repairs and information–that the analyst firm says can help organizations derive more value from IoT projects.
“The possibilities for analytics are limitless, and ever-growing data sources present more opportunities to innovate through holding novel insights,” researchers wrote in the report. “But currently, data and analytics leaders should narrow their focus to deriving additional value from the ongoing IoT implementations.”
There’s no preferred method of analyzing IoT data yet, with respondents to a Gartner IoT survey indicating equal preferences for leveraging non-IoT analytics tools already in place, analytical capabilities within an IoT platform, building proprietary capabilities and leveraging IoT-specific analytics solutions.
While researchers said general-purpose, modern analytics tools can be used for IoT in most instances, two differences specific to IoT analytics–adding new analytics users like those with deep domain expertise and engineers who want to embed analytics into projects, and consuming massive amounts of sensor data that must often be enriched with other data–are best addressed by IoT-specific analytics, according to the report.
As such, it names four “cool vendors” that can help with those scenarios. Gartner defines a cool vendor as a small company offering a technology or service that is innovative and enables users to do things they couldn’t before, has a business impact and is intriguing.
Vendors this year include:
Arcadia Data. To meet the needs of new analytics users and especially engineers, Arcadia offers options for adaptive visualization and embedded analytics. The software, which enables everything from technical analysis of IoT data for device maintenance and monitoring, to marketing tasks, like product pricing, also offers unique per-node pricing for IoT teams whose members use analytics to different extents, according to the report.
Optimal+. With heavy presence in the semiconductor industry, Optimal + creates a multi-industry and value-chain digital thread for end-to-end product visibility, by gathering data from IT and OT systems; creating a “quality index”; and binding data on wafer geography, parametric outliers, multivariate analysis and yield, according to the report.
Predii. A repair and predictive maintenance platform, Predii leverages domain-specific knowledge to empower technicians with artificial intelligence, helping diagnose problems and also providing repair instructions to fix them. Organizations in automotive and manufacturing industries could use the platform for pre-emptive maintenance, guided repairs, selecting which parts to take on service calls and automated claim approval, according to the report.
Predikto. The Predikto Max predictive maintenance platform accounts for contextual data such as weather, topology, operations data, personnel, inventory, asset usage and maintenance—in addition to equipment telemetry data—to dynamically create reliable predictive models using machine learning, according to the report, helping asset-intensive industries do everything from reducing predictive maintenance costs to addressing operational challenges.
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