What does a day in the life of a data analyst look like?
In today’s data-driven world, the role of a data analyst has become more popular than ever. They sift through vast amounts of Big Data, uncovering valuable insights that drive business growth and profitability.
But what about a data analyst in an AI company like Sertis? Join us as we sit down with Preaw, our skilled data analyst, who has played a pivotal role in numerous successful projects at Sertis, providing invaluable insights to our clients’ businesses.
Let’s explore Preaw’s daily routine as a data analyst. What tools does she use? What does she enjoy most about her role? Let’s dive right in!
What does the daily life of a data analyst look like?
- 10.00 AM — 11.00 AM — Project-based daily stand-up meeting — “My day as a data analyst kicks off with a project-based daily stand-up meeting for about an hour. This is where I get to sync up with the project-based team to plan tasks and share progress updates. I also engage in client meetings, typically twice a week.”
- 11.00 AM — 11.30 AM — Team’s stand-up meeting — “Then, I switch gears to my team’s, data analytics, stand-up meeting. As each member is assigned to different projects, here is where everyone pitches in with their updates to gain a comprehensive view of the team’s situation.”
- 1.30 PM — 7.00 PM — Project-based tasks — “After the lunch break, I focus on project-specific tasks for the rest of the day. The tasks vary based on where I’m at in the project timeline.”
What are typical data analyst tasks in each project?
“During the initial phase, my role involves aligning and collaborating with various teams, such as data engineers, responsible for building data pipelines, and the Consulting and Delivery team, which acts as project managers to oversee project planning and management. We align on the scope of work and project procedures. As a data analyst, my main task is focused on data analysis.
After receiving and studying the client’s requirements, we gather and clean all relevant data to prepare for analysis.
Next is the exploration and analysis phase. I typically write SQL scripts to query data from databases, analyze the data to uncover desired insights, and deliver final results to clients in various formats such as reports, dashboards, and data models, depending on the requirements.
If the client requests a dashboard, I utilize business intelligence tools like Power BI to create visually interactive dashboards. These dashboards are designed to present insights in the most easily understandable and accessible format, supporting the client’s further use.
Also, at Sertis, including the Data Analytics team, we place a strong focus on self-development and upskilling. Everyone is encouraged to set personal OKRs, which often involve acquiring new skills and updating their knowledge. For example, we may choose to learn more about cloud engineering or Power BI, based on our personal interests. We also organize monthly team knowledge-sharing sessions where everyone can share their current interests. For example, last month we explored PySpark, and this month we’re diving into the topic of Generative AI.
What are the typical tools a data analyst uses?
“The choice of tools depends on the client’s requirements in each project and can be categorized by tasks.
- For data management tools, I use cloud-based platforms such as Google BigQuery or Databricks
- My primary programming language is SQL.
- To create dashboards, I typically use business intelligence tools such as Power BI.
- Within the Data Analytics team, we use Google Sheets, Google Slides, and Draw.io to facilitate other tasks.”
What do you like the most about being a data analyst?
“What I like most about being a data analyst is the opportunity to uncover what others may overlook. While data in its raw form may seem mundane, as analysts, we get to delve into it, extract insights, and uncover what makes it fascinating. I also love that I get to learn new things all the time. In my work, there’s always a new challenge to tackle, which means I have to constantly expand my skills and knowledge base.”
What is the project that you are most proud of?
“One project that stands out to me is the ‘Data Unification’ project. Our client, a large business group with multiple sub-business units across various industries, needed insights into their total number of unique customers and the performance of each unit. To achieve this, we connected databases from each business unit to identify their unique customers and gain a comprehensive view of their performance.
I was able to learn and improve various skills through this project. I had the opportunity to collaborate with my team at different stages, from identifying value and potential use cases, preparing and analyzing data, all the way to delivering the final results to the client.”
How do you see yourself in the next 2 years?
“In the next two years, I see myself continuing as a data analyst, but with a broader knowledge base and improved skills as I truly enjoy being a data analyst.”
Originally published at https://www.sertiscorp.com/sertis-ai-research
