Machine learning (ML) is experiencing an unprecedented boom in several academic and business fields and is an important lever for transformation in the business world.
Notably, the success of some of the largest and rapidly growing global companies depends centrally on machine learning. According to Sundar Pichai, CEO of Google, “machine learning is a core, transformative, way by which we are rethinking how we’re doing everything. We are thoughtfully applying it across all our products, be it search, ads, YouTube, or Play.
This view that machine learning will determine an organization’s future is widely shared by CEOs in the US and across the globe.
Machine Learning techniques are a step ahead of traditional statistical techniques because they improve the model estimation process by increasing predictive power by employing new methodologies and techniques for variable selection and facilitating process automation.
The use of machine learning techniques and algorithms capable of learning from the new information leads to several advantages, some of which are the following:
- More types of data (both structured and unstructured) and sources of information are to be incorporated into the modeling process.
- Efficient use of large data volumes in the decision-making process.
- Detection of sophisticated or non-obvious patterns, not based on a priori assumptions.
- Greater automation of modeling and self-learning leads to an increase in model predictive power. Once designed and implemented, their calibration and maintenance requirements are lower compared to traditional models, thus reducing modeling time and cost.
Machine learning techniques begin with a set of observed data, from which classification rules or behavior patterns are derived on data not used in the analysis. Depending on the type of data (structured or unstructured) and the learning paradigm, different Machine Learning techniques are used. The technique chosen will be determined by the goal of the model to be built, the type of data available, and other factors.
Machine learning in business occurs at varying rates, depending on the industry and various factors such as company size, management style, and the general environment in which they operate.
Machine learning techniques are being implemented in a variety of industries at varying speeds and depths. Some relevant examples can be found in the education, finance, and health care industries and, from a broader perspective, in improving organizational efficiency.
In the educational field, artificial intelligence will provide systems that function as “learning companions” throughout the student’s life and are accessible through multiple channels.
In finance, machine learning algorithms are used to perform tasks like automated trading, the creation of roboadvisors for automated portfolio management, fraud detection, and risk assessment. RegTech, where Machine Learning techniques are used to comply with regulation and supervision, is one of the fastest-growing areas. In the RegTech market, machine learning techniques are used to analyze portfolios where there isn’t as much structured data in financial institution databases as with non-client prospect models. These models are also useful for customer segments with limited data, such as self-employed workers and microbusinesses, or individuals or self-employed segments who do not use banking services. Information from annual accounts (including business activity-related information like inventory or supplier churn and leverage, liquidity, solvency, or profitability ratios), supplemented by product and service information, or unstructured external data, can be used for this.
In the health sector, the goal is to improve diagnostic imaging, consultation management for treatment and recommendations, and collect medical data for research or robotic surgery. The market for artificial intelligence in the field of health is expected to reach 6.6 billion dollars by 2021, with the potential to save 150 billion dollars in the US health sector by 2026.
In areas such as manufacturing or logistics, solutions to improve machinery maintenance (through predictive maintenance models and sensorization) or distribution efficiency is proposed (for example, by optimizing the correlation between the transportation needs of multiple enterprises).
As for efficiency improvements in organizations, where IT systems generate large amounts of data, the IT and operations functions can be more proactive (logs, status reports, error files, etc.). Using this data, machine learning algorithms can detect the root cause of some problems, and predictive analytics can significantly reduce system failure. A personalized IT experience can also be incorporated: a virtual assistant for each employee will assist businesses in maximizing workplace productivity. This assistant will be able to connect information about an employee’s application usage with other stored databases, allowing it to detect patterns and anticipate the employee’s next actions, and access document databases.
Rolls-Royce, for example, has agreed to use Google’s machine learning engines in the Cloud to improve short-term navigation efficiency and safety, with the ultimate goal of achieving remote control without a crew. This example includes all of the typical machine learning elements, such as a collection of unstructured data based on sensors, data processing using new techniques, full automation, and so on.
Many companies integrate machine learning technologies into their processes (to varying degrees of integration), but not all with the same goal in mind. Some companies want to use new technologies to become more efficient and cut costs, while others want to reach new markets, but only a few want to create new business models. Rather than using machine learning to disrupt business models, some companies use it to improve specific business functions.
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