It is no big surprise that machine learning/ artificial intelligence has increasingly gained more popularity in the past couple of years. In the digital era, data is rapidly expanding and so is the need of analyzing and understanding this huge data for making right decisions. Big Data arrives as the hottest trend in the IT sector at the moment; machine learning is an incredibly powerful tool to make predictions or calculated suggestions based on large amounts of data. Most common example of machine learning is ecommerce websites algorithms to make product suggestions based upon the choices made by you as a customer.

What is machine learning? In simple terms, Machine learning is the practice of educating a computer about finding patterns and making connections to understand huge amounts of data. So in order to finish a given task, the machine does not use any programming software and instead of doing that it looks at the Big Data and algorithms to find out patterns for accomplishing it. Machine learning enables applications to look for patterns in the data, think and make wise decisions in the future.

Why Machine Learning is Relevant Today?

Machine Learning solutions are serving to a variety of sectors for optimizing organizational processes such as:

Automotive & Manufacturing: Identifying and navigating roads and obstructions in real-time for autonomous driving, Predicting failure and recommending maintenance on vehicle components, predicting outcomes and minimizing R&D costs, optimizing manufacturing costs are some of the tasks performed by machine learning.

Health-Care: Machine learning helps in diagnosing known diseases from scans, biopsies, audio, and other data. It is also useful in predicting personalized health outcomes to optimize recommended treatments. Machine learning can evaluate doctor’s performance and provide outcome-improving feedback.

Finance: Machine learning is extremely useful in identifying fraudulent activities using customer transactions and other relevant data as well as it is quite helpful in discovering new trends, risks and benefits for predictive analytics.

Telecom: Machine learning can predict lifetime value and risk of churn for individual customers. It is used to allocate the resources, make radical personalization for individual prospects, and forecast demand trends.

Retail and E-Commerce: Machine learning is used for product recommendation, price and product optimization, demand and supply optimization, making customer support a better experience in terms of precision in delivering solutions.

At fusion informatics, our machine learning services deliver solutions that are focused on enabling personalized and engaging customer experiences as well as optimizing organizational processes.

With the advent of network driven systems imbibed in our lifestyles, data has become the core of all services. Raw data available in massive multitudes serves as the basis of information which can be converted into knowledge that further provides value to all businesses. This chain is of utmost significance as it is shaping the way we are producing and consuming products and services day in and day out.

Data science is our era’s new fad and can be understood as a systematic process of obtaining insights into terabytes of information gathered over the last few years. It makes use of big data i.e. high volumes of structured and unstructured data for its analytical purposes. And just like any other scientific process, data science also involves identifying a problem, formulating hypotheses and conducting studies to determine the presence of a certain phenomenon.

Applications of this genius baby of computer science and maths are plenty. It has given rise to data intelligence, an analysis of various forms of data in such a way that it can be used by companies to expand their services or investments, or to analyze their own operations or workforce to make better decisions in the future. It can be used in the field of social science to determine what triggers reactions in people and how, in economics to understand how process fluctuate due to monetary reforms, in engineering to identify new methods of creating materials and products, in law to conduct trend analysis and develop frameworks, and so and so forth.

Data science thus plays a vital role in improving the quality and relevancy of daily processes ingrained in our lives. It runs on analytical creativity and looks out for future needs even before they’ve arrived. All big businesses like Netflix, Amazon, Procter & Gamble, etc have understood that data science is not something to be ignored but rather to be embraced and adopted. It’s time we all do too.