How are Data Science and Data Analytics Different?
Technology and the internet have completely reinvented the landscape of the world in very little time. While perhaps late to the party in some cases, the business world has made the leap to the cloud as well, and with overwhelming results.
With the tremendous potential at businesses’ fingertips comes a confounding amount of data. In fact, the sheer quantity of data generated by technology exceeds its current ability to sort through and comprehend it.
This is why data analytics and data science have taken businesses by storm. They provide an opportunity to not only quantify data but also realize its potential. Due to the freshness of their application and the similarities they share, though, confusion surrounds the terms. Are they interchangeable? If not, how do they differ? Are the differences practical, or just technicalities?
These questions are why we’re here. So here are some data science and data analytics differences and what each means for your business.
Beyond the word “data,” one of the primary reasons it is easy to confuse data analytics and data science is that they are intertwined. To make this point as simple as possible, think of data analytics as a specific application of data science. In other words, data science is the umbrella under which analytics falls.
The inverse is also true—while you cannot have data analysis without data science, you can have data science without analysis. So, where data science is a bit like looking at the big picture and attempting to make discoveries, analytics examines a more specific data set and looks for correlations.
Data science is not focused by theory or by the pursuit of the desired outcome. Data analysis, on the other hand, has a clear purpose—to answer a predefined question.
The scope of both data analysis and science foreshadow the goals typically associated with each. As a general rule, data science has a very broad goal—to search large, often disparate sets of data for connections and insights that may be useful for a business. In a way, it’s looking for the right questions to ask. It hunts for new perspectives that would otherwise go unseen.
On the contrary, data analytics has the goal of answering a question, not unearthing more. It is concerned with making actionable discoveries that can be implemented to help business in a measurable way. In short, data analytics knows there is a problem and seeks to solve it.
The primary difference in the goals of data analytics and science is the origin. If there is a specific question driving the process, it’s analytics. If there is no predefined purpose for the exploration being done, it’s science.
With an understanding of the basic differences between data analytics and science in mind, the business uses for each can come into focus. For most, this is the crux of the conversation—the real meat and potatoes of the entire topic—how can data analytics and data science help your business excel?
Being concerned with the big picture and unearthing applicable questions from the unknown of massive data sets, data science is best suited for establishing a foundation that can be farther examined and uncovering future trends before they occur.
This could be exceptionally useful for large corporations who are analyzing their own performance and exploring ways in which they could improve efficiency. Another suitable application would be machine learning or artificial intelligence—technology that is concerned with drawing connections from diverse data sets. Digital advertising and search engine applications also fall under the data science umbrella. In other words, data science is great for improving the way data is filtered and understood.
Data analytics tends to be applied in more precise ways. For example, if a business has a specific question they need to be answered, analysis is the vest route to finding a useful solution. Data analytics is more concerned with immediacy than data science is, as it searches for practical, actionable results.
Industries such as healthcare and travel can benefit tremendously from the implementation of data analytics. These and other such businesses have specific questions for which they need answers as soon as possible. Data analytics excels when a business has a clear vision of what they know and what they do not know, as it can be used to bridge the gap between the two.
The Differences Are Subtle, Yet Important
The separation between data analytics and data science is not broad, but it is important. It can determine who a business should to hire to fulfill their specifics needs and understanding the subtle differences can save you both money and time. The understanding and proper interpretation of data are integral to the future of business, and both start with data analysis and science.
About the Author:
Gregory Miller is a writer with DO Supply who covers Robotics, Artificial Intelligence and Automation. When not writing, he enjoys hiking, rock climbing and pining about the virtues of coffee.