Splunk enters the IoT market with Industrial Asset Intelligence

© Splunk
© Splunk

The machine data specialists are entering the highly-competitive predictive analytics market for IoT

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The machine data specialist Splunk has formally entered the IoT space with a new product aimed at providing companies with real-time monitoring and predictive analytics for their industrial assets.

The product, called Industrial Asset Intelligence (IAI) is the vendor's first foray into the crowded internet of things (IoT) market. Where it hopes to stand out is its heritage of being able to process large data sets (traditionally from IT logs) and layering machine learning on top to give predictive analytics.

The solution is built on top of Splunk Enterprise to capture and correlate data from Industrial Control Systems (ICS), sensors, SCADA systems and applications.

"Real-time analytics is an absolute must for manufacturers today, but organisations are struggling to bridge the gap between legacy systems, industrial assets and sensor data," said Ammar Maraqa, senior vice president, business operations and strategy and general manager of IoT markets at Splunk said in a statement. 

"Splunk IAI provides a single solution that ensures industrial systems are running at full capacity, enabling organisations to significantly save resources and money on unplanned downtime."

It is naturally being aimed at the manufacturing, oil and gas, transportation, energy and utilities sectors to monitor and analyse data from industrial assets, also knows as Industrial IoT (IIoT). The holy grail here is predictive analytics to help cut downtime of key assets and drive cost savings.

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The vendor is entering a crowded market here though, with SAP already offering predictive analytics for IIoT customers through its Leonardo platform and other major vendors like AWS, Dell, Hitachi, Microsoft, Salesforce and IBM offering cloud IoT platforms.

Seema Haji, director of product marketing for IoT at Splunk told Computerworld UK: "The big advantage Splunk has over vendors that creep into the IoT space is the platform approach. So the number one thing that stands out for me at Splunk is the entire platform approach to start in IT department or security and expand use cases to IoT, so looking at similar data, that machine data, but with different lenses.

"There are a lot of solutions that are very specifically aimed at certain pain points, but we take a more holistic platform approach, so more than just the analytics piece with dashboards, it is looking at operational data at the machine data level to get real time insights," Haji, who has been at Splunk nine months now, added.

Splunk IAI will be introduced in April at Hannover Messe for a limited number of customers, with plans to make it generally available in the Autumn.

"This is why we do the limited availability release," Haji explained, "because [IAI] came from repeatable patterns within our existing customer base and seeing how they use us for real-time maintenance, so let's offer a packaged set of capabilities to be able to create dashboards and customisable reports."

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Early customers include Brazilian shipping company Sapura Navegacao Maritima and carpet manufacturers Shaw Industries. 

"For years, Splunk has provided their customers with valuable answers from their IT and security machine data. Now, the massive growth of IoT data has the industrial world demanding a solution that can help them proactively take action on their data as well," said Dr. Ulrich Bock, director, rail and industry consulting, ESE GmbH.

"Working with Splunk Industrial Asset Intelligence has helped us significantly reduce the time spent on troubleshooting equipment issues," said Ricardo Bicudo, vessel performance manager at Sapura Navegacao Maritima.

"The packaged set of capabilities provided by Splunk Industrial Asset Intelligence easily integrates with our existing Splunk platform," said Erika Swartz, senior process engineer, fibres division at Shaw Industries. "Access to real-time and predictive insights is critical for identifying and diagnosing issues, as well as improving performance of our manufacturing processes."

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