{"id":14379,"date":"2023-05-26T08:29:28","date_gmt":"2023-05-26T15:29:28","guid":{"rendered":"https:\/\/www.springboard.com\/?p=14379"},"modified":"2025-01-27T04:18:24","modified_gmt":"2025-01-27T12:18:24","slug":"what-does-data-analyst-do","status":"publish","type":"post","link":"https:\/\/www.springboard.com\/blog\/data-analytics\/what-does-data-analyst-do\/","title":{"rendered":"What Does a Data Analyst Do? [2025 Career Guide]"},"content":{"rendered":"\n

Data analysis is used in almost every field, including marketing, retail, finance, law enforcement, science, government, and medicine. And with the ever-increasing amount of available data, the demand for data analysts is only growing, with an expected growth of 25% by 2030. But what does a data analyst actually do<\/em> with all that data? Keep reading to find out. <\/p>\n\n\n\n

What Is a Data Analyst?<\/strong><\/strong><\/h2>\n\n\n\n

A data analyst studies and interprets data, using these insights to help their organizations make better decisions and perform more effectively. They use critical and analytical thinking skills to ask the questions that need to be answered, find the data sets that can provide those answers, prepare and interpret the data, and then communicate their findings.<\/p>\n\n\n\n

What Does a Data Analyst Do?<\/strong><\/h2>\n\n\n\n

Data analysts have different job duties, depending on their industry, but all data analysts start by mining and organizing data. Data analysts use the data analysis life cycle, which begins with discovery and ends with deploying models based on their findings. They start with raw data, determine what is meaningful and irrelevant, and then process the meaningful data to uncover patterns and trends.  <\/p>\n\n\n\n

Discovery<\/strong><\/h3>\n\n\n\n

In the discovery process<\/a>, the data analytics team learns about the problem they’re trying to solve, and may consult relevant experts to understand the problem at hand better. Then, they’ll determine what relevant data sources are available. At this point, they may work with data scientists to create new ways of capturing the data they need. <\/p>\n\n\n\n

At this stage, they’ll determine how their organization will benefit from the available data, and what story they want to tell with it. Then, they’ll formulate a hypothesis they can test with data. <\/p>\n\n\n\n

For instance, a data analyst working in healthcare may want to understand the risk factors associated with an opioid overdose. In the questioning phase, they will evaluate which data sets would best show those patterns, and how to access them. They would also consider what tools they’ll need. The analyst might hypothesize that some preexisting conditions are a risk factor for opioid overdose and outline a plan for testing that hypothesis.<\/p>\n\n\n\n

Collecting and Gathering Data (Data Preparation)<\/strong><\/h3>\n\n\n\n

In the data preparation phase, the focus shifts to the data itself. Data analysts determine what data is available to them and gather it<\/a> from a variety of sources, including: <\/p>\n\n\n\n