Machine learning is an advanced data analysis that enables automated analytical model building. Machine learning algorithms continuously assess and keep ‘learning’ from available data. It is more like empowering a computer with certain resources and giving a target. This computer now goes through a series of permutation and combination with all the data in hand, to achieve that final target. In the process, it starts learning and adapting the best possible technique to achieve that particular target. In this case, no external codes are written. The computer learns through real-time data analysis.

Machine learning reduces the work of computer coders by half. Imagine the series of codes a coder would have had to write had it been a manual process where codes have to be input for a computer to analyze and predict outcomes from huge sets of data.

Machine learning is one of the efficient ways of handling data, predicting the outcomes, and being on time.

Big Data Analytics and Machine Learning go hand-in-hand. The benefits of machine learning in big data analytics are many. Here are few of the most important ones.

Big Data Analytics and Machine Learning – Benefits

#1 Unlimited data analysis in a shorter time span

Machine learning can handle an unlimited amount of data, assess them and provide a proper analysis for the same. This factor enables businesses to target customers with relevant messages that are based on customer activities and interactions. Machine learning can pinpoint variously related variables once it has forged a model from multiple sources of data. This helps in avoiding integration complications and makes way for more precise data results.

#2 Real-time data prediction for analysts to research

Big data analysts are looking at machine learning as the most effective source for precise data prediction. It consumes a humongous amount of data, thoroughly goes over all the related trends and activities, and finally provides concise and precise forecasts with real-time data. This exact nature of data empowers analysts to dig in further into their research, understand data better and utilize it for the betterment of multiple industries. One way of explaining it is: machine learning can precisely optimize new grocery offers for departmental customers at different time slots. This means, what the customers will see at 3 PM is different from what they saw at 1 PM.

How is machine learning helping big data analysts?

Most businesses are employing big data analysts to push their revenue game. In turn, big data analysts are relying on machine learning to do this work for them.

• Machine learning uses churn analysis to pinpoint those customers that will leave.
• It puts into play customer leads, conversions, and revenue rates, buying patterns and spending history.
• It uses the same technology to go over customer defections towards competitor brands.

How this benefits the data analyst?

Churn analysis helps in identifying the loopholes that might have driven away potential leads. Machine learning detects the potential leads that will opt out soon. Using this data, analysts can dig deeper to understand “why” and “what can be done.”

By analyzing buying patterns, revenue history, etc. analysts can strategize their customers based on priority and accordingly use their resources to engage with them.

Data analysis dealing with customer defections can help analysts in keeping a retention plan handy so that the situation comes under control even before the mess starts.

Machine learning has numerous advantages in big data analysis, the foremost being its ability to learn on its own, analyze data sets and provide precise insights that are valuable for analysts.

It is proactive and is mainly designed for industries that go by the “actions and reactions” formulae. It becomes easier for systems to pick up actions based on predictions made by machine learning. Since these predictions are immensely accurate, machine learning is a boon for any business or industry. It is the key to propelling business based on real facts and industry-specific predictions.