Data Analysts need to know a whole lot more than just how to crunch numbers. Digging through spreadsheets and connecting the dots are crucial aspects of what a data analyst does, but you’ll also need to know how to communicate and collaborate with others to get your point across, to ensure your team comprehends what’s happening. What else do data analysts do all day? In this profession, you’re tasked with scouring over large amounts of raw data sets, cleaning that information so that it makes sense, then gleaning business insights and analysis, to turn that information into actionable steps to help your company.
The information you find could help your business in various ways, like improving operational processes, allowing the company to cut back costs, or increasing ways to earn more revenue. For instance, if you were a data analyst in the NBA, your main responsibilities could include using analytical techniques to uncover why certain consumer behavior is prevalent on different game days. In different industry contexts, data always has the power to help solve problems. Because of this, there are endless ways companies utilize data analysts for business needs.
•Light coding knowledge (You don’t need extensive programming experience like that needed in a data scientist role, but it helps to be familiar with languages like Python. Check out the difference between data analyst vs. data scientist for more info on what sets these professions apart.)
See What These Three Data Analysts do on a Daily
The data analytics industry is ripe with job opportunities, and you could find yourself working on an array of interesting and complex subjects. Let’s take a look at what data analysts do on the daily by meeting three data analysts working in the industry.
Alex Brown, Data Analyst at Smarter Balanced Assessment Consortium and Mentor at Thinkful
With substantial data experience, Alex has worked directly with the CFO as a data analyst for a fastener distribution company helping different departments create reports and develop data dashboards. And currently, he is a data analyst for a k-12 standardized testing company where he deals with student assessment data. He’s also a helpful Thinkful Data Analytics Flex and Immersion mentor.
Ben Novak, Senior Data Analyst at Thinkful
Ben has extensive data analyst experience within the sustainability, retail, travel and healthcare industries. He’s worked on accurately calculating transit and facility emissions for large events, supporting improvements for hospital operations, and currently helps Thinkful with redefining data metrics and improving student enrollment reporting and analytics.
Blake Bowling, Thinkful Grad and Data Analyst at Campbell Soup Company
Blake completed Thinkful’s Data Analytics Flex course after spending 10 years working in sales, logistics, and operations. He wanted to advance his data analyst skills and position himself for better analytical roles. Check out his experience while learning at Thinkful.
How did you show that you are the best data analyst for the job?
Blake: One thing that helped me out in the interview is that beforehand I really focused on the data analyst vernacular. I wanted to make sure that I was able to "speak the language". This enabled me to describe the technical skills that I learned in a way that really impressed the interviewer.
What are tasks that you do every day?
Blake: Three tasks that I perform daily are fuzzy lookups, data rationalization, and data validation. With the last two, I use various tools to get those done. I use everything from adding complex sort columns, countifs, vlookups, and concat.
Alex: I don't think I could go a day without merging or joining data frames whether that's with a vlookup or index/match in Excel, joins in SQL, or merging via R or Python. I also use some sort of pivoting or group by almost every day.
Ben: At the start of every day, I have to make sure that I’m going into every analysis, dashboarding session, and meeting with the belief that I’m solving problems that will drive positive outcomes.
I also make sure to manage my email inbox, my browser tabs, and desktop – keeping only the essential things open. I make sure to move emails to tabled folders and get them out of my inbox. I always close browser tabs unless I’m using them. If I know I want to go back to that page, I create a bookmark and file the bookmark. My desktop has one folder on it, and it is for memes, everything else is saved in a folder that is labeled and organized in my files.
What are important technical and non-technical skills that you use on a daily basis?
Blake: My job is largely to be an Excel superuser. I deal with hundreds of thousands of rows of data in multiple datasets daily. It might sound pretty simple but an important part of dealing with the size of these datasets in a productive way is to be able to navigate them. Using shortcut keys Ctrl-down or up arrow, Ctrl-Shift, Ctrl-F, Ctrl-T, sort, and filters is the only way to go.
When it comes to non-technical skills, the most important thing that I use is my ability to communicate. During my workday, I communicate with multiple stakeholders across multiple business domains. I must tailor my message and communicate my findings in a way that adds value to whatever stakeholder I am talking to. In order to do this successfully, it is important to see the big picture and understand how all of the pieces fit.
You can do that by asking yourself; how does the data and its accuracy that I own impact person x? Why is it important I am doing these tasks for person y?
Alex: I think an important skill to do is to think through the whole problem in logical pieces and steps. That way, hard, complex problems to solve become bite-sized, manageable problems. I also think documenting how you handled each part of the "bite-sized" problems becomes important so you can easily come back to the problem.
Ben: The most important technical skill that I use on a daily basis is dimensional thinking. Understanding how to manipulate data using dimensions is crucial to creating scalable reports and dashboards, no matter which program you are using.
The most important non-technical skill that I use daily is empathy, it is crucial to understand what and why a stakeholder or customer wants from you. If you don’t look through the requestor’s eyes and ask lots of questions then you risk doing the analysis that you want, not the one that they need.
What's a challenge that you encounter often? How do you tackle that challenge?
Blake: One challenge you'll find when working for larger companies especially is different needs for different stakeholders. Person x might be looking for specific insights related to their business domain, where person y might be using your data for something unrelated. You can get pulled in several different directions.
The way you combat this is to bring everyone to the table and try to come to a consensus. You need to figure out what the priorities are, build another process to meet the needs of the second priority if need be, and get an idea of what is actually needed. Sometimes you'll find that a person just needs one specific data point that is already provided in another dataset that you own. Again, this is why it is important to understand the big picture and how the work you do aligns with organizational goals.
Alex: A challenge I encounter often is the organization of data. More than half of the battle, for doing any kind of analysis, is getting the data in a usable format. Tackling that challenge involves a lot of comparison by merging, pivoting, and finding distinct values.
Ben: The challenge that I encounter most often is fielding requests from a broad set of domains. When I get disparate requests I make sure to document the requests as thoroughly as possible. Once I have the request documented, I let it leave my mind. When solving lots of problems at the same time, many people experience cognitive load issues and my strategy is to document in a way that makes sense to me and then forget about it. There are only so many tasks that the brain can handle at once so I always make sure to limit my focus to the highest priority task with the understanding that priorities can change by the minute.
Why Do You Love Working as a Data Analyst?
Blake: The people are the best part of my job. It might sound cliche but the men and women I work with are all helpful, friendly, and incredibly competent. When working in an environment like that, it isn't hard to step up to the challenges that a role like this tends to have. You want to contribute to the team and the company in a way that makes everyone’s jobs easier and helps the end-user of the products that are provided to our consumers.
Alex: The constant learning. I am always trying to find the best, most efficient way to solve a problem. Every time I do something it gets better and more elegant.
Ben: Every day I’m asked to solve problems that allow our company to succeed. The best part of being a data analyst is using data to answer the questions that no one else can!
What’s Some Advice to Aspiring Data Analysts?
Blake: Remember you are going to be counted on for your technical expertise, but also your ability to come up with solutions to complex business problems. Whether you are working as an analyst for a hospital, non-profit, or business, immerse yourself into the way that business operates. It isn't just about being able to use the tools, it's about being able to guide your process in a way that leads to answering a question, or a series of questions, that positively impact the operations of the business or organization you represent.
If you have the luxury to become an analyst at a business you love, an organization you support, or a cause that is important to you, take it. Use the skills you possess and the passion you have to succeed to become an integral part of a work team that you take pride in being on.
How did Thinkful prepare you for a data analytics job?
Blake: Thinkful provided me with technical skills and direction on how to use outside resources if I run into a situation where I am unsure how to proceed. Whether it is a line in SQL or a complex formula in Excel, I can now get past these hurdles on my own. I think my employer appreciates that. It is important to understand that most people I work alongside, in similar roles, have specific expertise. For example, they may be able to write code in Python but have trouble with vlookups or macros. Thinkful touched on a wide array of these tools which has put me in a position to be successful.
Working as a data analyst in the world’s next workforce is a smart and successful career path. If you’re interested in seeing if a career in data analytics is for you, schedule a call with one of our admissions advisors.