Why Machine Learning is the Future of Data Analytics Posted on : Oct 20 - 2016

Facebook makes use of a user’s likes and preferences to show ads that they may be interested in. When you mistype a search query on Google, the search engine instantly cross-references it against the millions of similar typos to interpret the correct query and shows you results appropriately. Tesla makes use of your car’s vital parameters and benchmarks this against available data to know when you are due for servicing. Netflix studies engagement and behavior from millions of users to precisely know what images and promotions elicit better response from users.

All of this is just the tip of the iceberg. In the last few years, machine learning techniques have proven to be incredibly effective for predictive and deep insights; when used with data analytics. Many companies keep big data as their biggest asset because it reflects their aggregate experience. After all, every partner, customer, defect, transaction, and complaint gives the company an experience to learn from.

While in the recent years, many companies have focused more on how to store and manage all this data, it’s not just about the quantity of data or how it’s being stored. By combining data analytics with machine learning, companies can predict the future with their existing data and not just use it for historical analysis.

The real value of machine learning comes from its ability to create predictive models which can guide an organization’s future actions and discover never seen before patterns. Take the online advertising industry. The conventional tools provide advertisers with the ability to set budgets and channels to target. But with historical data of previous auctions, it is possible to create projections for future ad campaigns so that advertisers may increase revenues while optimizing cost. Even a cent saved through machine learning can bring about millions of dollars in savings on an annual basis.

Compared to traditional methods, machine learning can easily outperform all other forms of predictive analytics on speed, scale, and accuracy. Take credit card fraud for example. A machine learning program uses the information from the transaction to instantly compare it against millions of similar transactions to predict potential fraud. This is also a wonderful example for using machine learning with data analytics.

It needs to be pointed out that machine learning is not a new technology per se and has been around for a long time. Pandora can instantly create an ideal playlist simply based on our past history. Similarly, various email service providers like Gmail can, most times, correctly identify an incoming email as spam or not simply based on historical benchmark. The fact that all of this happen so seamlessly without us noticing it goes on to prove how well all of this is integrated to our lives. 

In conclusion, machine learning when combined with data analytics can impact the world in a truly meaningful ways. Since it runs on a machine scale and is data driven, it can easily extract value from disparate data, with way less dependence on human input. Also, unlike the traditional methods, machine learning actually thrives on big datasets, which means the more data is fed into a machine learning system, the more it will learn, and the better results it will deliver. Source