Big data industry and business analytics are predicted to touch the value of $203 billion by 2020, according to IDC. Reports also estimate that we will end up generating almost 180 Zettabytes of data per year by 2025.
This indicates that there will be tons of data. And when you have tons of data to process, you will need machine learning. At one point when data scientists find it impossible to feed in instructions to a computer to analyze data manually, machine learning comes to aid.
Machine learning has helped in tapping the maximum potential of big data. Ideally, machine learning defines the process of enabling a computer to learn its own set of rules to achieve a preset objective. This helps in negating the need for programmers to feed in a huge set of instructions. That’s why the terminology used is machine – learning.
How is machine learning related to big data?
First things first. Before going into how machine learning is enabling us to extract the maximum out of big data, let’s see how machine learning and data correlate.
Let’s assume you are asked to make pancakes. You have all the raw materials like flour, butter, sugar, milk and baking soda. But you have never made a pancake, and also have no idea what a pancake is. In such situation, you are on your own with no set instructions on how to proceed. You have raw materials (which is the data), and you have been given a final objective. You try various combinations and ways to make this pancake. After several tries, at one point, without knowing, you will end up making a perfect pancake. It’s not because you suddenly discovered the recipe, but because with time you understood the perfect combination that helped you achieve the final objective. This is exactly how machine learning works with huge sets of data.
The computer learns to discover a pattern by sifting through tons of data and analyzing the signals. In the above scenario, you happen to represent the computer that self-learns the rules rather than a programmer feeding them. Machine learning can also be defined as the process of making a computer think like a human brain and enact accordingly.
Leveraging big data with machine learning in various fields
The influence and dominance of big data are not restricted to any one industry. Across all industries, tons of data is being generated every day, and these data are produced in raw form. Once the data is cleaned and is ready for analysis, in comes machine learning to handle humongous data sets.
We have already stepped into an era where robots are more intelligent and efficient than us humans. They can entertain you, assist you and help you make accurate data-based decisions. You can manage these with voice commands or program tools. With time, they learn your commands, your expected results, and actions. Machine learning has enabled these robots to function accurately in unfavorable conditions and unpredictable environments.
Machine learning algorithms are a boon to the finance industry as well. It goes without saying that finance industry deals with a lot of sensitive data. Machine learning helps in analyzing all these data to understand buyers’ patterns, browsers’ history of previous transactions, and various social media activities.
● Data mining
Data mining involves large sets of data, where data is analyzed to identify interesting relationships and connections within a significant set. It encompasses data storage and maintenance as well. Machine learning provides set of tools and algorithms that help in pinpointing all the possible relationships among a data set. With its implementation in predictive analysis, it is possible to predict phenomena and act accordingly.
Amazon’s recommendation engine is one of the most prominent everyday examples of machine learning handling huge chunks of data. eCommerce giant Amazon has a gamut of data containing buyer patterns. Based on these patterns, recommendations are made that are relevant and most likely will be purchased by the customer. Various machine learning tools are used for behavior analysis.
Brands have humongous data sets at their disposal. While machine learning has revolutionized various aspects of our everyday lives, adequate data security is the priority for machine learning to flourish.
Huge sets of data are being collected from smartphones and other mobile devices. Although the risks multiply, effective use of new features and gadgets can ensure higher security.