Most of the businesses in the present time have shifted their operations to the digital domain in one form or the other. It is very difficult for a business to thrive in the digital environment if it is not well equipped with machine learning solutions. Machine learning solutions provide a business with the necessary roadmap to steer its growth in a competitive market.
The applications of machine learning techniques and solutions have drastically increased in the business domain. Machine learning has proved beneficial for the operations of organizations and the management of contemporary businesses. In this article, we attempt to analyze the relevance of machine learning solutions to various facets of prospective businesses.
Machine learning as the nucleus of business intelligence
Machine learning solutions are extremely vital for powering the growth prospects of a business. Different techniques of machine learning like reinforcement learning and unsupervised learning helps in understanding diverse business prospects with the help of wide data sets. Deep learning techniques help in improving the cognitive abilities of different systems that provide critical analytics to the organization.
This helps in engaging business discussions as the professionals at the helm of affairs have quantitative and analytical data sets to work with. Needless to mention, machine learning is a cornerstone of the decision-making process of an organization and contributes to business intelligence.
Machine learning advantages to business organizations: A quantitative analysis
It is extremely difficult to summarize the advantages that accrue to businesses once they adopt different machine learning techniques. This is because different types of businesses harness the power of machine learning in various ways depending upon the scope of a business and its area of operation. However, we narrow it down to a few prospects that are common in most businesses.
The first important area where machine learning benefits businesses is the operations sector. It helps in automating different types of redundant tasks.
In the sectors of manufacturing and logistics, machine learning has helped in the automation of more than 66 % of the tasks.
It is an open fact that most businesses operate in the digital domain at the present time. So, most of their decisions are data-driven and data-dependent. Machine learning helps in the process of decision optimization when we talk of such businesses. The statistics point out that more than 40% of businesses harness the techniques of machine learning for decision optimization.
Machine learning has also helped in increasing the work efficiency of businesses by more than 78%. It has increased the efficacy of businesses that deal with e-commerce by more than 77%.
Throwing caution to the winds
There is no doubt in the fact that machine learning can prove to be a catalyst for a large number of businesses. However, there are certain caveats that need to be taken care of before giving leeway to machine learning in business operations. Given the high computation power of machine learning techniques, there is a probability that the power requirements and energy needs of a business may increase which may also increase the cost of operations. It has also been found that the efficacy of machine learning techniques is low if the training data supplied is limited and not validated or processed properly.
Deployment of machine learning techniques, systems, and solutions may expose the sensitive data of an organization and make the critical infrastructure vulnerable to attacks. This can happen if the organization lacks an effective intrusion detection system that can detect security breaches.
The way ahead
Our past experiences suggest that machine learning can be a turning point for those businesses that are data-driven as well as data-dependent. For instance, a leading financial organization called PayPal used to provide financial services to a wide range of clients around the globe. It was competing with many other businesses that had the same domain of operation. However, it was one of the first companies to adopt machine learning techniques for securing its transaction mechanisms and detecting different types of lacuna in financial channels well in advance. As such, it was able to provide a robust security infrastructure to its clients for different financial services. Consequently, it witnessed a growth of 25% in its business operations in a span of 8 months.
This suggests a wide range of benefits and growth prospects that businesses around the world can leverage once they adopt machine learning solutions in their operations. It is high time that we provide training to emerging technology startups in machine learning systems so that they get acquainted with MLOps at the earliest.
