What Do Data Analysts Actually Do
Contrary to popular belief, data analysts don’t spend most of their time analyzing data. This is just a portion of what you are tasked to do. Remember you are a part of a team. So there are going to be meetings, requirements conversations, presentations,s and reporting that will take a large portion of your time. I have worked in Data Analysis and Data Science for 14 years. Here is a breakdown of where you should expect to spend your time.
Typical Day of Data Analyst
9:00 am: Check Emails
9:45 am: Stand Up with Team
10:00 am: Check any Alerts or Important data Sources
10:30 am: First Business Meeting
11:30 am: Continue Working on the Large Analysis Project
Usually, you have a larger project that requires several days or weeks to complete. Therefore you might be doing any of the following
- Writing Insights
- Cleaning Data Set
- Writing Code
- Data Modeling
- Dashboard
12:00 pm: Lunch
1 pm: Team Meeting
1:30 pm: Updating Asana and responding to new requests
- Provide progress updates on projects.
- Accepting and responding to new requests and providing ETAs.
2:30 pm Ad-Hoc Analysis and Reports
This is typically going to happen. Management or a Team lead will ask for a quick report on an existing project or dataset. You will mostly like provide a bullet point, graphic, or email to answer this question. I try to finish these by EOD if possible.
4:00 pm – Meetings about Existing or New Projects
These can be related to you or be meetings where your expertise is needed to help with outcomes.
5:00 pm – Make progress on some smaller tasks or report
Before we dive into the myths of the data analysis profession, let’s first understand what a data analyst does on a day-to-day basis. A data analyst:
What are a Data Analyst’s Responsibilities
- Collects Data: Gathers data from various sources, ensuring its accuracy and integrity.
- Cleans Data: Prepares and cleans data to ensure it’s in the right format and quality for analysis.
- Analyzes Data: Uses statistical tools and techniques to understand patterns and derive insights.
- Visualizes Data: Uses tools like Power BI, Tableau, and Excel to create visualizations that explain these insights clearly.
- Communicates Findings: Presents findings to stakeholders, often translating complex data into simple and actionable insights.
- Collaborates: Works with different departments (like marketing, finance, or IT) to understand their data needs and provide solutions.
- Continuous Learning: Keeps updated with the latest industry trends, tools, and techniques.
If you want to see how to Analyze Anything: Check out the video below.
What are some Myths about Data Analyst
Myth #1: Data analysts and data scientists are the same.
This couldn’t be further from the truth. While both roles are data-centric, data analysts primarily deal with structured data, using tools like Excel, SQL, and Power BI to visualize and communicate insights. Data scientists, in contrast, are involved in multiple stages of the data lifecycle, from data collection to creating predictive models. They often use scripting languages like Python. It’s worth noting that while there’s overlap between the roles, many start as data analysts and transition to data scientists if they wish.
Myth #2: Data analysis is boring.
This is a misconception often found online. Far from spending most of their time on mundane tasks, data analysts actively engage with stakeholders, understanding the objectives of their analyses, figuring out solutions, and communicating their findings for decision-making.
Myth #3: Data analysts must be experts at math and statistics.
While a foundation in math and statistics is beneficial, it’s not the sole requirement. Data analysis is multidisciplinary. It demands skills like critical thinking, problem-solving, domain knowledge, and data visualization. Communication, especially, is key. Analysts often need to break down complex insights into understandable bits for stakeholders.
Myth #4: Data analysis is a solitary job.
On the contrary! As data analysts, collaboration is at the heart of what we do. Whether it’s working with marketing, finance, or even engineers and data scientists, analysts are integral team members. Their success largely depends on how well they can work and communicate with a diverse group of individuals.
Myth #5: Data analysts must be good at programming.
While programming can be a beneficial skill, it’s not a strict necessity for data analysts. Tools like Excel can be immensely powerful. Of course, learning languages like Python or R can make certain tasks more efficient. But there are also platforms like Power BI, Tableau, and Alteryx that offer low-code or no-code solutions for data analysis.
There you have it! Five myths about data analysis debunked. I hope this sheds light on the multifaceted, dynamic, and collaborative world of data analysis. If you found this informative, please like and subscribe. See you in the next post!
