You have invested hundreds-of-thousands of dollars on analytics software, and you believed the ROI would be right around the corner, you were promised “simplifying business analytics for complex data” or “Answer questions as fast as you can think of them,” but in reality, insights we’re non-related or minor at best.
The Data Analytics market is overwhelmed with forecasting solutions that perform predictions based on uncorrelated data. The problem with those solutions is that the questions they answer are hardly related to the business challenges. Challenges the organization is facing, making their ROI very hard to define or measure.
Some of Razor Labs clients suffered from this issue, where current analytics tools have no clear ROI for their end-users. For context let’s look at an example.
Suppose you are a coal mining company, the question that current solutions answer can be “What will be the coal throughput tomorrow?”, when the business question for your company is “How can I optimize coal throughput?”, so I can maximize profits.
We brought to life a different product, one that aligns with your objectives that draws a clear line between your business question and the insights generated.
The new product is called DataMind and is gaining significant success in different use-cases. Its uniqueness is using Artificial Intelligence (here on – AI) to recommend specific actions that easily solve those problems, keeping the whole system continuously optimized. We use a different “type” of AI that is special in its resemblance to the human biology of neurons in the brain.
Deep Learning is a subset of AI. Deep Learning algorithms are inspired by the human neurons in our brain, allowing for pattern recognition and learning of complex data and systems that were impossible until recent years, but DataMind uses an even more innovative technology called Deep Reinforcement Learning which is a type of Deep-Learning.
This kind of AI, Deep Reinforcement Learning (here on – RL), is based on a simple concept – reward and punishment. The technology agent has one goal in “life,” to maximize its “score” on a task. When a behavior that increases the score occurs, the agent learns that this is a positive “pattern” and vice-versa.
You must be saying to yourself – “this sounds much like a child playing a game.” Moreover, you are entirely correct. This analogy is helpful.
Imagine DataMind as a Super-Mario game where the business process is the game “world.” Allowed operations in the game, as moving right/left and jumping follows operations permitted in the business process, such as changing input parameters in a manufacturing process. Also, the physics of the game mimics the physics of the process – the speed of movement, the height of jumping and so on.
If you were back at your 10-year-old body, playing Super-Mario for the first time, you would start to experiment around, “discovering” your new virtual environment. With time you would learn that some behaviors are better and result in a higher score. In reality, the only limitation to become “best” is time, what we achieve with DataMind is virtually removing that limitation, let us see how.
What if you could model your specific business process with high accuracy and low bias, to the digital world, effectively removing the intuitive concept of experience as a function of time?
DataMind does precisely that. DataMind constructs a digital model by consuming all data created by the existing business process, using data as a single source of truth reduces human bias to zero, allowing for a much more accurate model.
Then the exciting part happens, you let DataMind “play” on your model for millions of iterations, getting better and better every time, learning the hidden subtleties of your use-case. You gain the insight of hundreds of human years in a matter of days. That insight comes in the form of a simple, readable and interpretable output – “Increase input Y by 20%”, “Reduce temperature by 3 degrees.”.
Deep Learning technologies are the core of DataMind and are embedded all along the process. These algorithms have been proven very useful in the industry. For example, for the Oil & Gas industry similar techniques have resulted in 50%-75% more accurate anomaly detection and 15% reduction in full stream OPEX expenditure.
For the Oil & Gas industry similar techniques have resulted in 50%-75% more accurate anomaly detection and 15% reduction in full stream OPEX expenditure.
As DataMind’s product owner I have seen first hand how companies have gained up to 40% increase in throughput, this is an unprecedented improvement, based on data utilization entirely. I am excited by the new use cases that are still waiting.
If you have a business or manufacturing process that omits data, you should see how DataMind can prove valuable for your application.
Feel free to contact me at firstname.lastname@example.org to ask more about Reinforcement learning, Industrial AI, DataMind or anything that comes to mind.
Written By: Eliya Elon, Director, Head of International Business Development at Razor Labs.
Originally posted on Eliya’s LinkedIn.