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.
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 enormousvolume, variety, and velocityof 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:
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 informtheirdecisions – 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 teachthemselvesto become better at their jobs.
How do they do this? The simple answer is exposure to high volumes of data throughgood model training.
