Big Data Analytics

Big data and machine learning have become buzzwords we hear thrown around a lot, without necessarily understanding the nuances of each concept. While the two fields certainly aren’t mutually exclusive – and in fact intersect in ever more crucial ways – there are some key differences between big data and machine learning that businesses should understand before undertaking a project in either direction.

The good news is you probably already know more about both fields than you think. Both data analytics and machine learning touch our everyday lives in more ways than ever before. Below, we’ll zoom in on the difference between big data and machine learning from a business perspective, and strive to illuminate how the two fields will relate moving into the future.

What is Big Data Analytics?

One important distinction to make off the bat is that machine learning couldn’t really exist without big data. When we talk about big data, we’re talking about the enormous volume, variety, and velocity of data being produced by entities and individuals every single day.

Big data analytics is simply the process by which we collect, manage, and analyze this large volume of structured and unstructured data. The aim of this analytic process is to discover patterns about anything from consumer decisions to market trends that can inform business decisions and strategies.

To that end, we can summarize big data analytics as follows:

  • Process: To extract, store, and analyze vast data sets targeting a particular population, product, trend, etc.
  • Purpose: To analyze that data efficiently and find patterns that will help analysts, business leaders, or stakeholders optimize business decisions and strategies.

What is Machine Learning?

If big data describes all the information at our disposal, machine learning describes one particular way to analyze that data.

A field of artificial intelligence, machine learning is the process by which software applications (algorithms) “learn” to increase their accuracy for target outcomes – say, recommending TV shows to Netflix users based on their watch history. This can be contrasted with other ways Netflix stakeholders might use data analytics to inform their decisions – for example, using favorable watch rates of a particular genre to justify greenlighting more productions in that genre.

In other words, machine learning describes how programs teach themselves to become better at their jobs.

How do they do this? The simple answer is exposure to high volumes of data through good model training. Think of the scientific method: By performing trial after trial under specifically designed conditions, we accumulate “data” about certain phenomena, which we can then use to make predictions about the world. The bigger and more varied our data set is, the more accurate our predictions will be. Machine learning is not unlike this process, and can be considered one form of data analytics.

Whether you know it or not, you interact with (or have heard of) machine learning applications every day. Some of these applications include:

  • Price determination, wait time minimization, and driver/rider matching on ridesharing apps
  • Fraud detection by financial institutions
  • Self-driving cars
  • Personalized product recommendations and real-time price adjustments for online shoppers

The above consumer-driven examples may seem the most familiar. But machine learning has even been put to environmental and humanitarian use as well, including:

How Do Big Data and Machine Learning Work Together?

Let’s recap the major difference between big data analytics and machine learning. The former describes the broad process by which we glean useful insights from huge data sets. Machine learning describes one subset of such analysis, in which programs use data to teach themselves to function more accurately on our behalf, leading to improved business operations, service quality, customer relationships, and more.

Machines “learn” better the more data they have at their disposal. Big data analytics thus gives machines the volume and variety of data they need to make increasingly better and more efficient “decisions” in the performance of tasks. It makes sense – a veteran basketball player with a bigger and more varied “data set” of experience will usually play better than a rookie.

We’re already seeing breakthroughs in data analytics through machine learning in the healthcare, retail, financial, and auto industries, just to name a few examples.

Moving Forward with Machine Learning

The sheer volume and variety of data, as well as the velocity at which it’s produced, presents the challenge of being able to effectively manage and, well, analyze it. Information means nothing without accurate interpretation, and technology is famous for outpacing human capacity.

Businesses across industries must generate the talent, algorithms, and compute infrastructure to actually use all the information at their fingertips, including designing appropriate model training for machine learning processes.

In other words? Businesses can more fully reap the benefits of big data by embracing forward-thinking machine learning processes – but only with talented data scientists and state-of-the-art programs to turn that data into knowledge.