Scaling Data Science for the Industrial IoT

Conventional techniques for extracting and testing algorithms must get smarter to keep pace with the phenomena they’re tracking. The challenges are mainly in the following areas: - Volume, velocity, and variety of data - Unbounded amount of factors that may affect the output - Fast changing environment calls for constantly evolving models

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  • VENDOR
  • CUSTOMER
  • Agnostic, works for all customers who generate and analyze large amounts of data

  • SOLUTION
  • ThingWorx Analytics runs a vast toolset of algorithms and techniques to create more than just predictive models: causal analysis, relationship discovery, deep pattern recognition, and simulation. All the user needs to do is tell the system what the objective is—for instance, that a zero flow rate indicates a failure—and what data is available to the system. The analytics do the rest. For instance, analytics can generate a slgnal, the characteristics of a device that show that it is high-performing or low-performing. Various features you want to track—the model and age of the machine, for instance—form a profile. Pizonka describes profile generation as “finding a needle in a haystack.” Internally, ThingWorx Analytics performs the standard machine learning process of injecting training data into model building and validating the results with test data, and later in production with data from the field. Model building is extremely broad, using a plethora of modern techniques like neural networks to search for the most accurate prediction model. ThingWorx Analytics also runs continuously and learns from new input. It compares its prediction models to incoming data and adjusts the models as indicated. Pizonka says that the automated system can generate in minutes to hours what a human team would take months to accomplish. It’s good for everyday problems, letting data scientists focus on more meaty tasks.

  • DATA COLLECTED
  • OPERATIONAL IMPACT
  • Impact #1

    Generate profiles for each device that focuses effort on the features that are most important to track

    Impact #2

    Generates solutions for everyday problems, which frees up time for human data scientists to focus on high level work

    Impact #3

    Able to capture data from the sensors directly, which streamlines the analytics process and captures data from a wider range of devices

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