Data science and data analytics: people working in the tech field or other related industries probably hear these terms all the time, often interchangeably. However, although they may sound similar, the terms are often quite different and have differing implications for business. Knowing how to use the terms correctly can have a large impact on how a business is run, especially as the amount of available data grows and becomes a greater part of our everyday lives.
Much like science is a large term that includes a number of specialities and emphases, data science is a broad term for a variety of models and methods to get information. Under the umbrella of data science is the scientific method, math, statistics, and other tools that are used to analyze and manipulate data. If it’s a tool or process done to data to analyze it or get some sort of information out of it, it likely falls under data science.
Practicing data science boils down to connecting information and data points to find connections that can be made useful for the business. Data science delves into the world of the unknown by trying to find new patterns and insights. Instead of checking a hypothesis, like what is usually done with data analytics, data science tries to build connections and plan for the future. Data science often moves an organization from inquiry to insights by providing new perspective into the data and how it is all connected that was previously not seen or known.
If data science is the house that hold the tools and methods, data analytics is a specific room in that house. It is related and similar to data science, but more specific and concentrated. Data analytics is generally more focused than data science because instead of just looking for connections between data, data analysts have a specific goal in minding that they are sorting through data to look for ways to support. Data analytics is often automated to provide insights in certain areas.
Data analysis involves combing through data to find nuggets of greatness that can be used to help reach an organization’s goals. Essentially, analytics sorts data into things that organizations know they know or know they don’t know and can be used to measure events in the past, present, or future. Data analytics often moves data from insights to impact by connecting trends and patterns with the company’s true goals and tends to be slightly more business and strategy focused.
Why it Matters
The seemingly nuanced differences between data science and data analytics can actually have a big impact on a company. To start, data scientists and data analysts perform different duties and often have differing backgrounds, so being able to use the terms correctly helps companies hire the right people for the tasks they have in mind. Data analytics and data science can be used to find different things, and while both are useful to companies, they both won’t be used in every situation. Data analytics is often used in industries like healthcare, gaming, and travel, while data science is common in internet searches and digital advertising.
Data science is also playing a growing and very important role in the development of artificial intelligence and machine learning. Many companies are turning to systems that allow them to use computers to sift through large amounts of data, like on enterprise flash systems, using algorithms to find the connections that will most help their organizations reach their goals. Machine learning has immense potential across a number of industries and will undoubtedly play a huge role in how businesses are run in the future. Because of that, it is vital that organizations and employees know the difference between data science and data analytics and the role each discipline plays.
Although the differences exist, both data science and data analytics are important parts of the future of work and data. Both terms should be embraced by companies that want to lead the way to technological change and successfully understanding the data that makes their organizations run.