Machine Learning: Driving Value from Big Data
Machine intelligence, particularly when employed to analyze Big Data, can provide a low-cost way to optimize processes and productivity.
It is estimated that artificial intelligence (AI) technology, including machine learning (ML), will have a favorable impact on the global economy. A recent report by PwC stated that due to AI capabilities, the global gross domestic product (GDP) will be 14% higher in 2030.1 ML, also commonly known as machine intelligence or expert systems, will enable industries and businesses to advance their current capabilities.
Business leaders emphasize the optimum utilization of available resources, with a focus on improving business processes and increasing productivity. In the current state of industrial processes, however, many processes are already optimized. It is difficult to gain any extra improvements without significant upfront investments. Machine intelligence, particularly when employed to analyze Big Data, provides a low-cost solution to this problem.2
Understanding Expert Systems
The concept of expert systems is fairly simple: ML algorithms enable the machine to learn from the volumes of data available to it. In other words, the machine can learn from its environment and refine its tasks without depending on rule-based programming.3 For example, with extensive log data from a production line, ML can predict the final quality of a particular batch and stop any further processing of that batch at an early stage, if necessary.
Machine learning is essential to make sense of Big Data. Without these algorithms, Big Data is a useless heap of numbers. A common example would be data obtained from sensors that measure a great number of parameters along the production process. Given the large volume of data, it is not humanly possible to analyze it all. However, machine algorithms can use the data to optimize the use of inputs such as various raw materials, for example. Reduced consumption of materials not only reduces costs but is also more environmentally friendly.4 Machine algorithms help industries gain insight from Big Data obtained from various resources such as sensors, logs or the Internet of Things (IoT).
A data scientist develops machine intelligence through several approaches. One widely discussed approach is known as “neural networks.”5 Taking inspiration from the human brain and its complexities, programmers have developed neural networks that are algorithms to identify images or patterns. Unlike the human brain, machines do not get fatigued and can consistently perform tasks with speed and accuracy. A good data scientist matches the approach with the task at hand.
Machine learning provides various capabilities, including automated predictive analysis, process automation, improving operational efficiencies and developing a better understanding of customer needs.6 In addition, machine learning helps in detecting deviations from patterns and can help predict when a device is going to break down.
Implementing ML Systems
Machine learning is easy to implement for many different businesses, including small and mid-sized organizations. It dramatically increases productivity without requiring processes to be redesigned. In most cases, manufacturing facilities already log hundreds of parameters from their processes. ML reads those parameters to offer guidance regarding the element of a production line to tune in order to optimize the quality of final product. Changes can include parameters such as temperatures, timing, amount of input materials, etc. The system is programmed to work in a fully automatic mode or to provide an existing operator with recommendations.
ML has a short deployment cycle that spans over a few weeks, and it can provide an immediate return on investment. In a recent survey, participants reported that the return on investment for machine learning was quantifiable and was observed in early stages of implementation. Even fully optimized processes can achieve 5-10% of improved efficiency through the addition of ML. At a global scale, the industrial sector generates revenues of over $25 trillion. ML applications have the potential to save 5% (approximately $1 trillion) or more in almost all industries, without heavy investing.
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1. “AI Will Add $15.7 Trillion to the Global Economy,” www.bloomberg.com/news/articles/2017-06-28/ai-seen-adding-15-7-trillion-as-game-changer-for-global-economy.
2. Poutonnet, Philippe, “Survey Says: Machine Learning Happening Now and Paying Off,” https://cloud.google.com/blog/big-data/2017/04/survey-says-machine-learning-happening-now-and-paying-off.
3. “Machine Learning,” https://en.wikipedia.org/wiki/Machine_learning.
4. Zavalishina, Jane, “Machine Learning,” www.youtube.com/watch?v=OTBZdHEtum8&t=654s.
5. Marr, Bernard, “What is the Difference between Artificial Intelligence and Machine Learning?,” www.forbes.com/sites/bernardmarr/2016/12/06/what-is-the-difference-between-artificial-intelligence-and-machine-learning/#2c55122d2742.
6. “10 Reasons to Benefit: Business Process Optimization,” www.consultparagon.com/blog/10-reasons-why-your-company-could-benefit-from-business-process-optimization-right-now.