Being someone who is working in ‘Big Data,’ I get asked a lot what the differences and similarities are between Data Scientists, Data Engineers, and Data Analysts. When it comes to similarities, there is really two, BIG ones:
- All work with a lot of data and have the ability to work with it
- Code is the primary tool.
Outside of these things, these roles are very different. Before we get into the differences however, it is important to understand the definition of analysis from Meriam-Webster:
A careful study of something to learn about its parts, what they do, and how they are related to each other
Data Analysts
At their core, Data Analysts understand what the data is saying. The ability to find insights/learnings from the data that an organization both generates and curates from other sources. Being able to tell a story of what is in the data, what does it tell us, and what areas should have something done and possibly what that something should be.
Data Scientists
At their core, Data Scientists can make new data from the existing data. Data Scientists need to be Data Analysts also; they design algorithms that say something new based on the available data. Through predictive analytics, using machine learning technologies and techniques, Data Scientists are able to give an organization more insights/learnings beyond what just the naked eye might see.
Data Engineers
At their core, Data Engineers build the ‘infrastructure’ to allow Data Analysts and Data Scientists to do their work efficiently and effectively. Data Engineer are the bookends; in the beginning, they build data pipelines with that best curate the data to make it usable for Data Analysts and Scientists. In the end, Data Engineers optimize data science algorithms to perform optimally within the environment. All of this is done by building technical patterns and components that focus on the repeatable and therefore reduce the noise in the SDLC process.
Data Analysts, Scientists, and Engineers are all data hungry roles that require a powerful, quality data environment that focuses on both availability and accessibility in an on-demand fashion. Each role taking on the data with their skills and in concert with one another to help organizations make the most of their second greatest asset.