According to The Economist (in 2017), data is the most valuable resource in the world. Because of this, many technology providers have entered the market to help business leaders gain intelligence from their data. This has caused widespread confusion amongst business leaders on a variety of different topics related to data as the techniques and tools used to gain value from data have increased exponentially.
The goal of this article is to help you understand the similarities and differences between data analytics and data science while giving examples of when the techniques should be used. While both are extremely valuable to organizations, the what and the how they add value to an organization is important to distinguish, especially if you are just beginning the journey of using data to identify and solve problems.
Although a formal definition of the term “Data Analytics” is still heavily debated amongst technical professionals, technology providers, and business leaders, the most commonly agreed-upon definition loosely defines data analytics as the process of examining datasets to draw conclusions about the information they contain. Or in layman’s terms: to look at information to understand the past or why something happened.
Data analytics is used to look at the past and determine why something occurred. An example of this in the business world would be when an organization is trying to determine why revenue decreased in Q1. The problem is known (and happened in the past) but the reason why the problem happened is not known. In this case, you would pull data from sources such as the Accounting, Sales, Marketing, and Customer Service departments to determine what caused the decline. You would arrive at answers with statistical data, such as evidence that Sales converted fewer leads, that Marketing provided fewer leads than normal, Customer Service experienced a higher rate of unhappy customers or even a combination of the above.
Using data analytics would allow you to find the true cause of the problem, as opposed to just guessing what the problem was and making a business decision based on gut feelings. You would now be able to make business decisions based in fact and according to actual data.
While both data analytics and data science use data to draw their conclusions, Data Science is primarily directed at predicting the future. This prediction is usually presented as the most likely scenario based on the available data. The prediction is often derived with no specific goal or problem at hand, rather it is used to uncover trends or future scenarios for which you should be planning.
This is often where artificial intelligence and machine learning are most applicable (my next article will discuss the differences and use cases between the two) due to the large data sets (both structured and unstructured) that are needed from many disparate sources.
A marketing executive would likely turn to data science as they are putting together their strategic plan to understand potential market conditions in the future. You could arrive at an understanding of Millennials’ purchasing patterns in the future or what could lead to evaporated market share. The possibilities are endless.
If you are looking for a simplified answer to the difference between data analytics and data science, I would describe data analytics as the technique(s) to understand the past while data science is looking to predict the future. This is a very important distinction to be made as the tools, techniques, business understanding, and data needed to understand the past and predict the future are very different. It is important to first understand what the business use cases are and then decide if data analytics or data science is most appropriate. Typically, data analytics is a much shorter and less costly path to arrive at ROI than data science.
Erik Merle is a digital transformation expert who has advised C-Level executives on their digital transformation journey, with an emphasis on data analytics and data science. He currently works with marketing executives looking to incorporate data analytics and data science into their strategy to improve the performance of their department.