Accelerating the Industrial Internet of Things
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DataRPM

Automating Predictive Maintenance.
United States
2012
Private
< $10m
11 - 50
DataRPM is an award-winning predictive analytics company focused on delivering the next generation predictive maintenance solutions for the Industrial IoT. DataRPM platform automates data science leveraging the next frontier in machine learning known as meta-learning, which is machine learning on machine learning. The platform increases prediction quality and accuracy by over 300% in 1/30th the time and resources delivering 30% in cost savings or revenue growth for business problems around predicting asset failures, reducing maintenance costs, optimizing inventory and resources, predicting quality issues, forecasting warranty and insurance claims and managing risks better.

DataRPM's advisory board consists of industry luminaries from companies such as Google, Hortonworks, Oracle, Cloudera, EMC, Facebook and Booz Allen Hamilton. Headquartered in Redwood City, California, the company is privately held. Customers of DataRPM include companies like GE, Cisco, Jaguar, Orange and similar Fortune 500 companies.
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DataRPM deliver industry’s only Cognitive Predictive Maintenance (CPdM) Platform for Industrial IoT. DataRPM uses patent pending Meta-Learning technology, integral component of Artificial Intelligence to automate predictions of asset failures.

CPdM is a full stack platform available on cloud or on premises that connects to the data lake and then automatically runs multiple Machine Learning experiments, finds patterns & anomalies in data, identifies influencing factors & predictors, and builds an ensemble of predictive models to automate Predictive Maintenance.

Not only does CPdM delivers descriptive, predictive, prescriptive analytics through Natural Language Discovery module but also provides a closed loop system by integrating this actionable insights through APIs to ERP, CRM, and CMS systems. The Meta Learning environment runs multiple live automated ML experiments on datasets, extract data from every experiment, train an ensemble of models on this meta data repository, applies models to predict the best algorithms and finally build machine -generated and human verified Machine Learning models for Predictive Maintenance. The entire workflow uses various recipes like feature engineering, segmentation, influencing factors and prediction recipes to give prescriptive recommendations.
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GE, Cisco, Jaguar, Orange
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Hardware
Software
Horizontal applications are standardized (e.g., asset tracking). Vertical applications are tailored to specific needs (e.g., delivery fleet management).
APIs are the market enabler for IoT. They allow users to manage devices, enable data transfer between software, and provide access capabilities.
Middleware integrates the diverse components of an IoT application by structuring communication, workflows, and business rules.
IoT analytics includes real-time or edge computing and batch analysis. Analytics can be behavioral, descriptive, predictive, or prescriptive.
Data management solutions capture, index and store data in traditional database, cloud platforms, and fog systems for future use.
Services
System integrators link IoT component subsystems, customize solutions, and ensure that IoT systems communicate with existing operational systems.
IoT data management consultancies help to make sense of big data, decide which data to maintain and for how long, and troubleshoot IT issues.
IoT software consultancies support the development of data analytics, visualization solutions, and platforms, as well as integration into embedded systems.
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