In recent years, small and major enterprises alike have started to focus more on understanding how they can apply Machine Learning to enhance their business operations. In a guest article from First Consulting, the firm explains how, based on concepts that were developed a couple of decades ago, practical applications now range from allowing IT'ers to filter spam messages out of email traffic, to delivering safe self-driving cars on roads. Machine Learning technology is, subsequently, being applied within a business context at an accelerating rate. 

First Consulting has implemented machine learning applications for many of its clients, including the use of large quantities of data generated by Internet of Things (IoT) sensors and the creation of real-time price prediction models. New technologies allow businesses to make their processes transparent and more efficient with Machine Learning offering unprecedented capabilities in this regard and, thus, in creating value for organisations.

Machine Learning is a method of data analysis in which a computing application derives predictive insights from data. It uses algorithms to analyse data sets to look for patterns and/or correlations which result in useful insights. Among the advantages of Machine Learning is that the predictive insights generated are continuously and automatically updated by the latest data collected. These updated insights can be used as input for the algorithmic decision logic to further improve the prediction accuracy. Depending on the chosen Machine Learning method (for example dimensionality reduction, meta learning or reinforcement learning), varying the inputs to the algorithmic logic can reveal hidden insights.

To make effective use of Machine Learning, a clear definition of the business problem is required before commencing. In addition, the methodology (and required software) should only be applied effectively if two key conditions are met:

Problems which can be solved with simple “if-then” business rules are better candidates for simpler solutions, although these can also be solved through Machine Learning. Insufficient or poor-quality data will result in the algorithms deriving misleading insights into the business problem in question; but where such data is available, it is important to follow a layered analysis and implementation approach for Machine Learning.

Machine Learning applications can be useful in any industry. Common business applications include predictive maintenance, customer segmentation and fraud detection. For example: the case of an insurance company that wants its customers to pay on time. Using billing data already collected through day-to-day operations, the company can derive predictive insights which can help the detection of specific customers that are likely to be unable to pay their bills in the future. In this way, customers can proactively be approached on payments settling, a much more effective strategy compared to reactively trying to deal with non-payment issues.

Hortilux is a family-owned market leader in the production, installation, and maintenance of greenhouse lighting systems. Hortilux produces several products that monitor plants’ growth conditions, and the company was seeking to make use of the data collected. First Consulting implemented a scalable cloud architecture that could handle large data transfers from these sensors. This, in turn, allowed for the creation of a crop-growth prediction model. The model output is visualised for the customer in a web-based application, which was also developed by First Consulting. Machine Learning algorithms were applied to allow the accuracy of this prediction model to improve over time as more data is generated, allowing for better calibration of their products and, thus, increased value for the customer.

Related: Internet of Things cultivation boosts greenhouse horticulture.