Big Data vs Machine Learning

By Satish Varre On Sep 17, 2020

Ever wondered how Uber so effectively determines what time you will arrive at your destination, how Amazon and Google already know what you could be looking for through their recommendations, or how Gmail seems to know what you consider spam? Well, that is machine learning at work. There are many other ways in which machine learning is making huge differences in our lives.

Behind this powerful technology are data scientists who have worked hard to see it evolve to what it is today. This has been occasioned by the need to extract value from the vast amounts of data the world generates every day. Machine learning today features remarkable data computing and analysis power way beyond what humans can possibly do and is being used widely to perform complex analysis much faster and more effectively.

The ML market, it is predicted, will be worth $8.81billion by 2022 up from $1.03 billion in 2016. This translates to a 44.1% growth rate which also reflects the growing demand for professionals with machine learning certification, skills, and experience to deliver machine learning-driven solutions.

What is Big Data?

Big data refers to the explosive generation of large volumes of structured, semistructured, and unstructured data from sources such as social platforms, transactions, websites, search engines, and IoT devices. On the other hand, big data analytics refers to the process of collecting, organizing, storing, and analyzing big data. Through big data analytics, valuable patterns, trends, and insight can be discovered in data, that will guide crucial decisions for business strategy and growth.

Big data is defined by the three Vs;

Volume referring to the large volumes of data collected by systems from various sources and which cannot possibly be stored or analyzed by traditional database management systems.
Velocity refers to the speed with which data is generated and flows in every second. At least 1.7MB data is created every second and as of 2020, the world was worth up to 44 zettabytes of data. Such data requires efficient data analysis tools that can perform real-time or near-real-time processing to get the most value out of it.
Variety. From text messages, tweets, Facebook posts, audio files, word documents, images, videos, to emails, data comes in a wide variety of formats.

In addition to its complexity, other Vs have been used to define big data including variability, value, veracity, and visualization.

What is Machine Learning?

Machine learning (ML), a subset of artificial intelligence, is a form of data analytics in which machines learn from data and improve from experience through continuous exposure to large streams of data to make accurate predictions without explicitly being programmed to do so. Machine learning applications bring together statistical analysis tools with data to predict outcomes that are useful when making important business decisions. It is an important aspect that has been integrated into big data storage and management systems.

In ML, algorithms label input data and then identify hidden patterns within it. These patterns form insights that guide business decisions. These algorithms are also used to automate some aspects of the decision-making process.

Some common machine learning applications include:

Fraud detection applications in financial institutions
Self-driving cars
Real-time ads on sites and smartphones
Price determination in ridesharing apps
Real-time dynamic pricing in eCommerce
Personalized product recommendation on eCommerce sites
Email spam filtering

Machine learning can be supervised, semi-supervised, unsupervised, or reinforced. Supervised learning is the most commonly applied form of machine learning.

How Big Data and Machine Learning are interrelated

Let’s just say that big data came in first and machine learning was developed as a solution for certain data analysis limitations like human thinking within the complexity of big data. Big data offers access to massive data and machine learning is how this data is analyzed and insights extracted from it. ML is the most effective way of identifying hidden patterns and insight from unstructured big data. As such, big data and machine learning complement each other.

At the core of big data are data mining and data analytics techniques. While data mining refers to the acquisition of data from various sources and preprocessing it, data analytics involves manipulating or analyzing it to discover hidden patterns and gain insight from it. Analyzing data takes many forms, but some of the most efficient ways of doing so is by using a splunk competitor program to analyze and sort data crucial for your business. Such insight helps businesses make informed decisions that align with customer needs and values.

As we have already seen, ML is more of a data analytics technique that uses algorithms to collect, analyze, and assimilate data to accurately predict future outcomes from it. ML algorithms are very useful in large organizations that generate and harness big data for their growth strategies. As data continues streaming in, ML algorithms keep improving to deliver accurate insights. In other words, the more data machines ingest, the more ML algorithms evolve and adapt to it to predict outcomes more accurately.

ML can be integrated into data labeling, data segmentation, data analytics, simulation, and other aspects of big data operations.

Big Data vs Machine Learning

While the applications of big data and machine learning exist within the same environments, they do have their differences. In a broader context, big data analytics refers to various techniques used to analyze and extract insights from large sets of data and ML is just one of these techniques.

Here are some distinctions between big data and machine learning

	Big data	Machine learning
	Big data analytics makes use of existing data to identify patterns, trends, and gain insight for decision making.	Machine learning uses existing data to form a foundation for learning data. As more data streams in, it improves to make a more accurate prediction of outcomes
	Big data is mainly used for the purposes of storage and discovering patterns from data	ML is mainly used for training algorithms to learn and make accurate future predictions from data
	Big data is, in essence, a collection of large sets of data that can be analyzed through non-complex techniques like sequence analysis and classification using frameworks like Hadoop	ML is a technique that teaches machines to automatically learn from big data and execute mostly analyses that are too complex for humans
	Big data analytics is a performance-oriented computing	Machine learning falls under data science
	Big data analytics makes use of very large data sets, in other words, big data	ML can be applied on both large and smaller data sets
	Applications of big data include the collection of purchase, transactional, patient, or sales data among others, and financial research.	Applications of machine learning include product recommendation, dynamic pricing, self-drive cars, traffic predictions

Conclusion

Given the current volume, velocity, and variety of and the value of big data, it is necessary for organizations, large and small, to employ technologies that will explore its full potential. Data should be managed, stored, and analyzed effectively for it to be useful to its users and yes, the human ability is too limited to do this.

Machine learning is one such technology that goes beyond ordinary data association and classification techniques to train algorithms to learn data to make accurate future predictions for the purposes of process improvement, increased customer value, and overall business growth strategy. As such, big data and machine learning have found a wider range of applications across industries, from predicting machine failure in manufacturing to learning customer behavior and their preferences and many more. The future is promising for data scientists who will have earned machine learning skills because machine learning is now replacing routine human operations.