We wade through the commonly confused terms related to data analytics and data science to help you make more informed decisions for your business.
There’s no shortage of hype around data analytics right now.
And no shortage of buzzwords either. As more businesses are recognizing the potential of data analytics, related buzzwords are getting a lot more airtime.
But here’s the problem. A little knowledge can be a dangerous thing – especially when the concepts and technologies being discussed are complex and overlapping. And that’s especially true of data analytics – it’s why definitions of big buzzwords aren’t always reliable.
Take data analytics and data science for example – scour the internet and you’ll find a bunch of definitions for those terms that overlap, as though they’re the same thing. The same goes for artificial intelligence and machine learning.
If you’re serious about investing in data analytics, getting the truth about these terms should be your top priority. It’s important that you know what you’re talking about – and what you’re getting into. First, so you know what data analytics can actually do for your business – and what it can’t do. Second, so you can gauge the potential scope of your investment in time, expertise, labor hours, and, of course, cost.
All of this is why we thought we’d demystify four of the most hyped (and most commonly confused) terms in data analytics for you in this blog post.
Data analytics vs. data science
These two are often seen as synonymous, but they’re actually completely different. Let’s break them down quickly:
Analytics is about discovering, interpreting, and communicating meaningful patterns in data. It encompasses a whole bunch of things from predictive analytics, prescriptive analytics, and enterprise decision management, to supply-chain analytics and big data analytics.
Businesses use analytics to make smarter, better-informed decisions and predictions.
Definitions of data science have evolved massively over the years, but in a nutshell, data science encompasses the scientific method, math, statistics, and other tools that are used to analyze and manipulate data.
Data scientists are the ones who make predictions about data, then test them using machine-learning models.
Artificial intelligence vs. machine learning
Online definitions of artificial intelligence and machine learning overlap so much it’s natural to think they’re the same thing. But they’re not. Have a read through these definitions and see how different AI and ML really are:
AI is a branch of computer science aimed at building machines that essentially ‘think’ for themselves, for example, by being able to problem-solve and improve their problem-solving abilities over time – hence the word “intelligence”. AI systems that concentrate on specific tasks (like understanding language or recognizing pictures) are known as “weak AI”. Systems that actually exhibit the same skillful and flexible behaviour that humans do are known as “strong AI” – but they’re still hypothetical…for now.
ML is about giving systems the ability to automatically learn from data sets without additional programming – you can think of it as the implementation of compute methods that support AI. The more high-quality data you feed to ML systems, the more accurate their outputs. Breakthrough technologies like self-driving cars, practical speech recognition, effective web search, and a deeper understanding of the human genome are all thanks to ML.
Dive deeper into data analytics
Hopefully, you’ve now got some clarity on what the four most hyped about terms in the data analytics space actually mean. For more information, check out our Data Analytics resources here.