Industrial Analytics, AI, Big Data and Industry 4.0 are all buzz words, somewhat surrounded in mystery and glamour. Companies spend big dollars jumping on the band wagon of AI, to be seen as innovative and forward thinking. However, with few companies scaling up their pilot programs, applying the technology enterprise wide and broadly reaping the benefits, we thought it timely to put together an article to assist in the selection of industrial analytics solution.

There are now dozens of companies promising to provide artificial intelligence to industrial businesses. With this market now some-what saturated why are we still waiting to see results widely publicised? Perhaps it is because not all solutions are created equal, and there is a disparity between offerings. At VROC we’ve put together this table below so show how broad the AI technology market is and how one solution does not fit all.


Some of the tech providers in this space provide AI to a broad range of industries; from retail, banking, healthcare, telecommunications, aerospace, manufacturing and transportation. Artificial Intelligence can be applied to any type of big data and therefore a broad range of industries can benefit greatly. However at VROC, we feel that customers benefit when there is an in-depth knowledge of the industry and a clear understanding of the type of problems they are trying to solve. The problem statements being addressed in a retail or healthcare market are vastly different to those in oil and gas or utilities for example. Whilst there are benefits to diversification, there are also risks both to the technical provider and the customer of a diluted solution that is somewhat generic.  For industrial companies, we highly recommend you seek out AI providers that specialise in industrial data and industrial problem statements. 


There are a number of AI providers whose methodology revolves around a lengthy and costly consultative period. Spruced as a highly customised solution, these often take many months to develop and require human level intervention to create new AI models and maintain existing ones. Unfortunately some customers now shy away from Digital Transformation and AI altogether, as they engaged consultants previously with some never getting to a proof of concept stage or seeing any tangible results from the investment.

Whilst these providers offer domain knowledge, its is important to consider speed of implementation and cost when choosing an AI partner.


There are a few providers, including VROC who have developed a ‘do it yourself’ solution, which can be deployed rapidly and feature automated machine learning. This functionality does not rely on costly and lengthy consultations meaning that results can be generated from data in next to no time at all. The ‘self service’ component puts the power in the hands of the subject matter experts, who know what problem they are trying to solve and no-code platforms, like OPUS help them create their own artificial intelligence data models.

This method releases data scientists to focus on the complex 20% of all problems, with 80% of problems being resolved by subject matter experts and engineers, saving time and money immediately.

The benefit of automated machine learning is that the models are automatically refreshed, learning from new data constantly, updating insights. If an incident takes place that has never occurred before, the machine learning technology will learn all the triggers in order to predict any future occurrences that are similar. This process is known as MLOps.


What makes industrial data so unique? It’s the shear volume of it from hundreds or thousands of different sensors and controls, some of which produce time-based data every millisecond. This volume alone is mind-boggling. The industrial analytics solution chosen needs to be able to easily process this volume of data rapidly.  The problem statements are also vastly different from other industries, that customers benefit from partnering with an AI provider that understands the ins-and-outs of the sector. Industrial assets are expensive pieces of equipment and for many industrial businesses, it is essential that they operate continuously. Slight improvements to production can mean a significant boost to the bottom line, and asset performance needs to be carefully modelled to predict deviations so interventions can be planned and faults and failures avoided.


At VROC we are focused only on industrial big data and generating no-code AI predictive insights for improved efficiencies and reliability.


So, where to from here? It’s important as you research your Industrial Analytics partner that you ask the following questions;

  1. Is their solution generic or specific to your industry?
  2. How quickly will you be able to use the technology to see results
  3. Will you be able to create your own models as are ready to address new problem statements?
  4. Can the solution be deployed remotely, or does the provider need to come on-site? (this is a critical question during the COVID-19 pandemic)
  5. Can the platform be scaled to your entire enterprise?
  6. Do you retain ownership of your data?
  7. Are they equipment agnostic? Will the solution integrate with your other existing systems?
  8. Can you easily customise data visualisations to show to senior management?


Learn more about VROC’s industrial analytics solution OPUS, and see examples of the solution at work here for our customers. 

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