Data drives the modern organizations of the world so don't be surprised if I call this world a data-driven world. Today's business enterprises owe a huge part of their success to an economy that is firmly knowledge-oriented. The volume, variety, and velocity of available data have grown exponentially. How an organization defines its data strategy and its approach towards analyzing and using available data will make a critical difference in its ability to compete in the future data world. As there are a lot of options available in the data analytics market these days so this approach includes a lot of choices that organizations need to make like which framework to use? Which technology to use etc. One of such approach is the choice between Big Data and Machine Learning.
Big data analytics is the process of collecting and analyzing the large volume of data sets (called Big Data) to discover useful hidden patterns and other information like customer choices, market trends that can help organizations make more informed and customer oriented business decisions.
Big data is a term that describes the data characterized by 3Vs: the extreme volume of data, the wide variety of data types and the velocity at which the data must be processed. Big data can be analyzed for insights that lead to better decisions and strategic business moves.
Machine learning is a field of AI (Artificial Intelligence) by using which software applications can learn to increase their accuracy for the expecting outcomes. In layman's terms, Machine Learning is the way to educating computers on how to perform complex tasks that humans don't know how to accomplish.
Machine Learning field is so vast and popular these days that there are a lot of machine learning activities happening in our daily life and soon it will become an integral part of our daily routine. So, have you noticed any of these machine learning activities in your everyday life?
You know those movie/show recommendations you get on Netflix or Amazon? Machine learning does this for you.
How does Uber/Ola determine the price of your cab ride? How do they minimize the wait time once you hail a car? How do these services optimally match you with other passengers to minimize detours? The answer to all these questions is Machine Learning.
How can a financial institution determine if a transaction is fraudulent or not? In most cases, it is difficult for humans to manually review each transaction because of its very high daily transaction volume. Instead, AI is used to create systems that learn from the available data to check what types of transactions are fraudulent.
Ever wondered what's the technology behind the self-driving Google car? Again the answer is machine learning.
Now we know What Big Data vs Machine Learning is, but to decide which one to use at which place we need to see the difference between both.
Head to Head Comparison between Big Data vs Machine Learning
Key Differences between Big Data vs Machine Learning
Both data mining and machine learning are rooted in data science. They often intersect or are confused with each other. They superimpose each other's activities and the relationship is best described as mutualistic. It is impossible to see a future with just one of them. But there are still some unique identities that separate them in terms of definition and application. Here's a look at some of the differences between big data and machine learning and how they can be used.
Usually, big data discussions include storage, ingestion & extraction tools commonly Hadoop. Whereas machine learning is a subfield of Computer Science and/or AI that gives computers the ability to learn without being explicitly programmed.
Big data analytics as the name suggest is the analysis of big data by discovering hidden patterns or extracting information from it. So, in big data analytics, the analysis is done on big data. Machine learning, in simple terms, is teaching a machine how to respond to unknown inputs and give desirable outputs by using various machine learning models.
Though both big data and machine learning can be set up to automatically look for specific types of data and parameters and their relationship between them big data can't see the relationship between existing pieces of data with the same depth that machine learning can.
Normal big data analytics is all about extracting and transforming data to extract information, which then can be used to feed to a machine learning system in order to do further analytics for predicting output results.
Big data has got more to do with High-Performance Computing, while Machine Learning is a part of Data Science.
Machine learning performs tasks where human interaction doesn't matter. Whereas, big data analysis comprises the structure and modeling of data which enhances the decision-making system so require human interaction.
Big Data vs Machine Learning Comparison Table
I am discussing major artifacts and distinguishing between Big Data vs Machine Learning
The Future of Big Data vs Machine Learning
By 2020, our accumulated digital universe of data will grow from 4.4 zettabytes to 44 zettabytes, as reported by Forbes. We'll also create 1.7 megabytes of new information every second for every human being on the planet.
We're just scratching the surface of what big data and machine learning are capable of. Instead of focusing on their differences, they both concern themselves with the same question: "How we can learn from data?" At the end of the day, the only thing that matters is how we collect data and how can we learn from it to build future-ready solutions.