Contributed By Ramesh Subramanian, CTO, Infogain
Machine Learning—a subset of Artificial Intelligence(AI) is the latest buzzword in the technology industry which is emerging as the pathway to the future for enterprises. Machine Learning provides enterprises with the required framework, insights, and algorithms to ensure better predictive ability.
The ever-increasing usage of electronic means of interaction and commerce, as well as IoT devices has been producing an incredible volume of data and statistics which is impossible for humans to analyse manually. ML technology helps combine all the data gathered from myriad touch points for delivering useful insights to enterprises that contribute to the various strategic outcomes.
A survey conducted in 2016 by the National Business Research Institute revealed that 62 per cent enterprises will deploy AI technologies by the year 2018. It is evident that both Machine Learning and AI is becoming a vital facet for several burgeoning as well as eminent industries as it can offer deeper insights to businesses besides enhancing the process of decision-making.
Machine Learning allows computers to be capable of categorizing, processing, and generating data based on buying and spending patterns of customers, their feedback and interactions, their peers and social groups, and virtually anything else.
An ML-based computer algorithm can identify which customers are most likely to abandon your brand besides helping you profile their identity, buying habits, and the reasons that are making them leave your brand. While such use-cases for ML abound, enterprises are adopting ML algorithms to increase flexibility of shop floors, supply chains, collaborative partnerships, even detect the price points that consumers will prefer.
How are Enterprises Adopting Machine Learning?
More and more businesses are embracing this technology with open arms as it generates positive ROI and leads to the advancement of future products. Technology giant Google was among the first to recognise the significance of integrating ML technology; nevertheless, at the present time, deep-learning techniques have a wide appeal across industries. For example, financial institutions are using ML technology for identifying potential cases of fraudulent claims, and also for conducting a risk analysis. Similarly ML is helping healthcare providers save lives by identifying severe health issues by analysing patient data.
Today ML is controlling applications such as real-time speech translation, biometric identification system, gene mapping, web-content curation, and so on. In order to leverage Machine Learning enterprises are working in tandem with their customers to comprehend their grievances so as to make sure that their products and services address the specific needs of the customers. For example, Pinterest has been using ML technology to display more interesting content to you. Similarly, Disqus utilises ML to eliminate comment spams. Likewise, several e-commerce companies are employing ML strategies to provide their clients with the advantages of machine learning when the clients browse for products on their sites.
Enterprises that have incorporated ML technology into their business processes are mainly focusing on the following application areas—
Measurable Business Outcomes of Machine Learning
One of the crucial objectives of implementing ML technology in business is to transform business objectives into assessable goals. Here are some measurable business outcomes of machine learning—
What kind of readiness is required for ML adoption?
In order to implement ML technology successfully, it is important for enterprises to ensure readiness across the following factors—
ML Technology– Helping Enterprises Turn Unstructured Data into Competitive Advantage
Respondents of a study conducted by MIT Technology Review Custom in association with Google Cloud revealed that the most important advantage of ML technology is that it enables implementers to achieve a competitive advantage. The study further states that 25 per cent of existing ML implementers believe that they have already accomplished that goal. 76 per cent respondents stated that they are utilising ML for escalating sales growth.
Here are a few organisational goals cited by the survey that ML adopters can achieve by implementing this technology—
What kind of initiatives is required in the adoption of Machine Learning?
In today’s digital landscape, it is indispensable for businesses to incorporate machine-driven approaches. Since ML technology is playing a key role in driving an era of innovation, businesses should adopt machine learning for gaining a competitive edge in their respective industries.
News Originally Posted on: PC Quest