Frost & Sullivan Applauds the Disruptive Value of Mtell's Leading-edge Machine Learning Analytics Platform in the Oil & Gas Industry

The platform's interoperability with in-house maintenance systems aids seamless changes and modifications of existing maintenance plans

May 19, 2015, 09:00 ET from Frost & Sullivan

MOUNTAIN VIEW, Calif., May 19, 2015 /PRNewswire/ -- Based on its recent analysis of the big data and analytics market in the oil & gas (O&G) industry, Frost & Sullivan recognizes Mtell with the 2015 American Frost & Sullivan Award for Customer Value Leadership. Mtell is poised to revolutionize the asset health domain with its advanced machine learning platform that facilitates the precise, timely, and prognostic assessment of a machine's operational condition. The platform boasts leading-edge prescriptive analysis capabilities that guarantee early detection of degradation.

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Unlike competing solutions that provide only anomaly detection, Mtell's best-in-class, scalable, machine learning platform uses real-time sensor data and combines anomaly-detection and precise pattern recognition techniques to deliver accurate alerts on the impending failure of a machine. By focusing on degradation detection rather than failure detection, Mtell's machine learning platform addresses customers' specific pain points, such as environmental damage, personnel safety, loss of productivity due to machine downtime, or any other related compliance issues.

"Mtell has further improved its technology to enable individual machines to learn from their surroundings and transfer knowledge to other machines. The key differentiator here is its ability to detect small faults and minor degradations before they become major catastrophes," said Frost & Sullivan Senior Research Analyst Rahul Vijayaraghavan. "This not only significantly boosts the operational efficiency levels of customers' assets, but also saves cost and time by increasing equipment uptime."

The smart machine learning-based solution incorporates artificial software agents that are easier to implement and produce the best results among similar products. The customer can create multiple agents on a single asset to extract precise data patterns. The agents detect minute-by-minute changes in any machine and correlate everything to detect a failure in advance, thus providing customers with ample time (often more than 30 days) to plan their maintenance efforts.

In addition, the solution's seamless integration with other enterprise asset management (EAM) systems to automatically issue work orders is a huge advantage to customers that need a cost-effective, centralized asset health decision-support platform. Other attractive features of Mtell's advanced machine learning platform include its rapid deployment, ease of use, and limited human involvement post implementation. As the platform is self-sufficient, self-learning, and self-improving, there is limited requirement for expensive maintenance methodologists or data scientists.

"While the platform can analyze and cleanse data as well as provide training for agents through wizards, users can step in and obtain metrics on several perspectives of an asset's condition and even tweak certain values to get a larger percentage of a result, based on their specific requirements," observed Rahul. "Moreover, Mtell's centralized control over asset performance allows customers to easily scale up production."

Mtell's multi-sided platform can be sold to a wide variety of customers, including owner-operators in asset-intensive businesses, companies that provide remote monitoring and maintenance services, and original equipment manufacturers that supply software and services along with their equipment. These strategic collaborations and partnerships with key value chain participants ensure that end users do not have to rely heavily on Mtell for implementation services, adding value to the overall customer experience.

Each year, Frost & Sullivan presents this award to the company that has demonstrated excellence in implementing strategies that proactively create value for its customers with a focus on improving the return on the investment that customers make in its services or products. The award recognizes the company's inordinate focus on enhancing the value that its customers receive, beyond simply good customer service, leading to improved customer retention and, ultimately, customer base expansion.

"The key to the long-term success of any company is its ability to continuously nurture and enhance the value of its products and/or services to its customers. As such, Frost & Sullivan is excited to recognize Mtell for its industry-leading focus on improving customer experience and customer ROI with its machine learning analytics platform for the oil & gas industry," said Frost & Sullivan Global President & Managing Partner Krishna Srinivasan.

Frost & Sullivan's Best Practices Awards recognize companies in a variety of regional and global markets for outstanding achievement in areas such as leadership, technological innovation, customer service, and product development. Industry analysts compare market participants and measure performance through in-depth interviews, analysis, and extensive secondary research.

About Mtell

Founded in 2006, San Diego-based Mtell is a privately held company providing software solutions for managing the health of industrial equipment. Making machines smart, Mtell plays an important role in developing the Internet of Things. In addition to reducing risk to people safety and the environment, Mtell is a significant contributor to equipment performance and profitability. Solutions are deployed globally in the oil and gas, mining, pharmaceutical, transportation, and wastewater industries. For more information, visit

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SOURCE Frost & Sullivan