Behind the Scenes: A Peek into My Role as a Data Analyst

Hey everyone,

Have you wondered what goes on behind the scenes in the world of data analysis?

Today, I’m pulling back the curtain to give you an insider’s look at the fascinating world of a data analyst.

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I’ve worked with many large and complex datasets and have helped businesses make informed decisions. Through these experiences, I’ve realized that it’s not just about mastering technical tools; it’s also about developing critical, essential skills that make our insights more impactful.

Along the way, I’ll share some of my personal experiences, hoping they resonate with you and provide some guidance on your own data journey!

Before we explore the skills, I will briefly describe what I typically do as a data analyst. Usually, I start by extracting data from databases using SQL, after which I explore and clean the data. Next comes the exciting part: extracting insights and visualizing findings using tools like Power BI or Excel. Finally, I wrap it all up by effectively communicating these findings to stakeholders.

However, before analyzing data, we must start with some key questions in mind. We need to understand the objectives of the company we are working for or the goals of the department we are supporting.

But it doesn’t stop there; I also wear the hats of a problem-solver, storyteller, collaborator, and effective communicator. However, above all, I’m fueled by curiosity — it drives me to dig deeper, question everything, and discover valuable insights within the data.

This year, I’ve conducted two comprehensive exploratory analyses on two different subject matters, shared the insights with my stakeholders, and recorded several impacts based on the insights shared.

I will be sharing the steps I took:

1. I knew what my company’s objectives were and worked with them. I always keep my company’s objectives in mind as they guide every aspect of my analysis. This includes gathering relevant features, variables, or attributes I’m feeding into the analysis.
2. What I did was to gather all the relevant features that would be fed into the analysis. It could range from demographic data like gender, age, occupation, and location to transactional features such as transaction count, amount, and average balance. It could be digital penetration of the customers, whether they are on the company’s digital platforms, etc.
3. Writing out the queries — This is where SQL comes in. I understand the database and which tables to get my data from. Spool my data and explore the data. Note that, during analysis, I refine and edit the queries as needed, sometimes adding new features. Also, I create tables here. Creating tables speeds up the process, allowing for faster results.
4. I then explore the data. I do my analysis here. As an analyst, I mostly use Excel. Some people prefer to use Power BI. It’s your choice. So far you can share quality and insightful insights to your stakeholders. This is very important. The goal is to extract meaningful insights that can inform decision-making.
5. After the exploration, I present my findings. Here, I prepare PowerPoint. This is what I share with my stakeholders. I visualize my findings in good visuals, I’m not here to show myself. I’m not here to show how good I can design or use colors. But to make my findings easy for my audience to understand and make decisions. The focus is on clarity and relevance rather than flashy design.
6. I also include actionable recommendations. We are not just analyzing for analysis’s sake. It’s not just about analysis; it’s about driving action. We are doing that to empower stakeholders to make informed decisions. It isn’t always easy to get ACTIONABLE RECOMMENDATIONS here. That’s why Business Acumen is very important here.
7. Then, I share my insights with my audience, sometimes via email or scheduled calls, where I explain what I did — while avoiding technical jargon and using words they can relate to. Additionally, I provide the necessary data to kickstart their engagement with the recommendations.
8. Lastly, I monitor the implementation of recommendations and track the impact of my work overtime. This helps in assessing the effectiveness of the analysis and making any necessary adjustments.

This is a snapshot of my role as a data analyst. Perhaps in the future, I’ll share my experiences as a data scientist.

If you found this post worthy of reshare, don’t hesitate to share it with your friends and colleagues. Let’s spread the knowledge and empower more individuals in the world of data analysis!

Feel free to check out my previous articles for more tips and resources on mastering data analysis:

1. A Must-Read for Data Enthusiasts Before Diving into Data
2. Reflecting on Two Years as a Data Analyst at Wema Bank
3. Clear Roadmap to Mastering SQL in 2024

I’m passionate about sharing valuable insights with my LinkedIn community!