What Does a Data Analyst Do? Exploring the Day-to-Day of This Tech Career

What does a data analyst do?

Generally speaking, a data analyst will retrieve and gather data, organize it and use it to reach meaningful conclusions. “Data analysts’ work varies depending on the type of data that they’re working with—for example sales, social media, inventory, etc.—as well as the specific client project,” says Stephanie Pham, analyst for Porter Novelli®.

Companies in every industry, from hospital systems to fast food chains, can benefit from quality data analysis. The insights that data analysts bring to an organization are valuable to employers who are willing to respond to findings and better address problems, meet demand and satisfy their customers or clients.

Regardless of which industry they work in, data analysts can expect to spend their time developing systems for collecting data, seeking meaningful patterns and compiling their findings into reports that can help improve their company.

Analysts can be involved in any part of the analysis process. In a data analyst role, you could be included in everything from setting up an analytics system to providing insights based on the data you collect—you may even be asked to train others in your data-collection system.

Now that you have an idea of what data analysts do in general, you’re ready to dig into the specifics of life on the job as a data analyst.

What are some common data analyst responsibilities?

We enlisted some experts to help you get a sneak peek of the daily duties of a typical data analyst.

1. Producing reports

“As an analyst, I spent a significant amount of time producing and maintaining both internal and client-facing reports,” says Casey Pearson, churn analytics manager at Disney® Media & Entertainment Distribution. Those reports give management insights about new trends on the horizon as well as areas the company may need to improve upon.

Writing up a report isn’t as simple as throwing numbers onto a blank page and sending it to your manager. “Successful data analysts understand how to create narratives with data,” says Jess Kendra, manager of analytics at Porter Novelli. “To remain valuable, the reports, answers and insights that data analysis provides have to be understood by the next decision-maker, who frequently is not an analyst.”

2. Spotting patterns

The most effective data analysts are able to use data to tell a story. In order to produce a meaningful report, a data analyst first has to be able to see important patterns in the data. “At the base level, data is used to find trends and insights that we can use to make recommendations to our clients,” Pham says.

Reporting in regular increments, such as weekly, monthly or quarterly, is important since it helps an analyst notice significant patterns. “They all contribute to an overarching time frame where we can see trends over time,” Pham adds.

3. Collaborating with others

Surprised to see this on the list? The word “analyst” might make you think of someone working more or less independently, but that’s far from the truth. The wide variety of data analyst roles and responsibilities means you’ll collaborate across many other departments in your organization, including marketers, executives, operations managers and salespeople. You’ll also likely collaborate closely with those who work in data science, like data architects and database developers.

Being able to communicate well is important. “Your success is dependent on your ability to work with people—the people you are gathering the research questions from, peers you collaborate with to execute the work and the people you deliver the final presentation to,” Kendra says.

4. Collecting data and setting up infrastructure

Perhaps the most technical aspect of an analyst’s job is collecting the data itself. This often means working together with web developers to optimize data collection, according to Pearson.

Streamlining data collection is key for data analysts. There’s just so much raw data to work with. Analysts develop routines that can be automated and easily modified for reuse in other areas to make their job easier and more efficient. Analysts keep a handful of specialized software and tools in their arsenal to help them accomplish this.

Where do data analysts work?

The short answer to this question is pretty much everywhere. Companies across industries are looking for new ways to gather, analyze and use data to better their business. While more and more businesses are beginning to find ways to incorporate analytics into what they do, here are five of the top industries currently looking to hire data analysts:

• Business intelligence
• Finance
• Sharing economy services
• Healthcare
• Entertainment

Of course, that’s not a comprehensive list. Everything from forecasting market conditions to optimizing factory production processes can fall under the umbrella of a data analyst’s work. There’s a huge variety of potential employers and projects to apply this technical skill set to.

Data analyst vs. data scientist

With all that in mind, you might be wondering about another prominent data role—the data scientist. While there is some overlap in the type of work they do, there are significant differences between data analysts and data scientists.

Since the role of a data scientist is relatively new and sometimes nebulous, those in the field have worked to differentiate their role from that of the data analyst. Let’s break it down based on skills and job duties.

Data analysts:¹

• Have moderate math and statistical skills
• Have a strong business acumen
• Have moderate computer science/coding skills
• Develop key performance indicators
• Create visualizations of the data
• Utilize business intelligence and analytics tools

Data scientists:¹

• Have strong math and statistical skills
• Have a strong business acumen
• Have strong computer science/coding skills
• Identify trends with machine learning
• Make predictions based on data trends
• Write code to assist in data analysis

Though data analysts and data scientists have different backgrounds and strengths, keep in mind that these roles can be a little squishy in how they’re defined. Some organizations’ definition of a “data scientist” role can in practice be much closer to that of an analyst. Because of this, data professionals will benefit to focus more on the duties, skills required and job description than the specific job title during a job search.

Types of data analytics

At its core, data analytics is about answering questions and making decisions. And just as there are different types of questions, there are also different types of data analytics depending on what you’re hoping to accomplish. While there’s no set-in-stone glossary of these types of data analytics, the folks at ScienceSoft® do an excellent job breaking this work down into four primary areas:²

• Descriptive analytics answers, “What happened?”
• Diagnostic analytics answers, “Why did something happen?”
• Predictive analytics answers, “What is likely to happen?”
• Prescriptive analytics answers, “What action should be taken?”

Data analysts can tailor their work and solution to fit the scenario. For instance, if a manufacturer is plagued with delays and unplanned stoppages, a diagnostic analytics approach could help identify what exactly is causing these delays. From there, other forms of analysis can be used for fixing these issues.

What tools do data analysts use?

Data analysts rely on various tools to collect and make sense of their data. For instance, Kendra’s team uses specialized tools to efficiently gather data from social media, news sites and magazines, as well as tools to sort and categorize data to visualize that data for reports and presentations.

These are some common tools in a data analyst’s tool belt:

• SQL
• Microsoft Excel®
• SAS® software
• Google Analytics®
• Google Tag Manager
• Tableau®
• Google AdWords®

Should you become a data analyst?

So what does a data analyst do? The answer is so many different things that they become hard to quantify. If that wide-open field of possibilities excites you rather than intimidates you, you might be well suited to working in data. After all, data itself is just as massive and full of possibility for the people willing to collect and study it. Ready to explore a degree in data Analysis? Check out facts about the Rasmussen Data Analytics program. 

We’ve covered the basics of the data analyst job description—now it’s time to learn how to get started in this field. Curious is data analyst is a good career? Check out this additional blog to read more. 

Read more about the difference of Data Analytics vs. Data Science here. 

¹Ryan Thorpe, “Blurred Lines: Data Analyst vs. Data Science,” Towards Data Science, February 6, 2018, [accessed August 2022], https://towardsdatascience.com/blurred-lines-data-analytics-vs-data-science-12ff92a3bd4e.
²Alex Bekker, “4 Types of Data Analytics to Improve Decision-Making,” ScienceSoft, July 7, 2017, [accessed August 2022], https://www.scnsoft.com/blog/4-types-of-data-analytics.

Porter Novelli is a registered trademark of Porter Novelli, Inc.
Disney is a registered trademark of Disney Enterprises, Inc.
ScienceSoft is a registered trademark of ScienceSoft USA Corporation.
Microsoft Excel is a registered trademark of Microsoft, Inc.<
SAS is a registered trademark of SAS Institute, Inc.
Google, Google AdWords and Google Analytics are trademarks of Google, Inc.
Tableau is a registered trademark of Tableau Software.