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Analyzing data, creating dashboards, working closely with technical teams and different departments across the company, integrating new AI tools… A Data Analyst’s day-to-day work is anything but repetitive. Charlotte GALLET, who graduated from IÉSEG in 2023 (Grande École Program), tells us more about her responsibilities, the challenges she faces, and the skills that make her job so rewarding.

Could you tell us more about your role as a Data Analyst?

As a Data Analyst, I collect and analyze data to create dashboards and tracking tools that help employees make informed business decisions. My role is not to make the final decision, but rather to bring all the relevant information together in one place and suggest solutions to help manage a project. I also do quite a lot of coding, which I really enjoy.

I work alongside other Data Analysts. Contrary to what people often think, it is very much a team effort. It is easy to develop tunnel vision in this line of work, so it is important to discuss things with others, have them challenge our analysis, and look at the same issue from a different perspective. There is a lot of support and collaboration within the team.

What does a typical day look like?

Every week, we review our objectives and performance figures to make sure everything is on track. If the numbers look good, we continue working on the project at hand, always with the aim of optimizing our objectives depending on the department we work in. Data Analysts can work in marketing, finance, operations, logistics, and many other areas. If the numbers are not good, we try to identify solutions and put them into practice quickly to address the issue.

In this role, we work closely with engineers on everything related to data, as well as with project managers and directors on the business side. There is a lot of coordination involved because projects often have an impact on other teams or areas of the business. We need to make sure that everything is aligned with the company’s overall vision, while also taking each team’s specific needs into account.

Could you give us a concrete example of a project you have worked on?

I work for a food delivery company, mainly operating in Spain. If a customer has an issue with an order — for example, if some fries are missing or their pizza arrives cold — we use our compensation and refund system to help minimize the negative impact on the customer experience.

One of the tools we can use to improve the customer experience is a promotional voucher. In this context, I worked on a project to determine the most appropriate value for these vouchers. Why offer €5 rather than €2 or €10?

To answer this question, we run tests by splitting our customer base into two groups: one group receives a €2 voucher and the other a €5 voucher, over a period of several weeks or months. Once the test period is over, we analyze the behavior of the customers concerned in the weeks or months following the incident. Do they come back? If so, how many additional orders do they place?

Another important factor, of course, is that we have a budget. We cannot simply give everyone whatever amount we want. We therefore need to calculate the return on investment for each test.

Ultimately, we might decide to offer a €5 voucher rather than a €2 voucher because we found that customers are more likely to return afterward, making the higher-value voucher more profitable in the long run.

Once the data analysis is complete, we share our findings with the finance teams or with the local teams in each country, explaining what we plan to implement in their market and why.

What are the main challenges of being a Data Analyst?

We are often seen as “translators.” We need to be able to communicate with engineers as well as with people on the business side. We act as a bridge between the two, and that is not always easy.

We receive requests from the business teams, analyze them, determine whether they are feasible, and then go back to the teams with our recommendations, including any aspects that may need to be changed. So, first, we may have to convince them that their initial idea is not necessarily the best approach and that certain aspects need to be reconsidered.

Then we discuss the project with the engineers, who may tell us that something is technically impossible or that it would take months to implement. You have to constantly balance all these different considerations. It is challenging, but also very interesting, because we do everything we can to make sure the project succeeds.

AI is not necessarily a challenge in itself, but today, you have to know how to use it. That means constantly learning new things. On top of the day-to-day workload, you need to be able to adapt to new software and new tools in order to work as efficiently as possible.

What does it take to be a good Data Analyst?

You need to be able to summarize and synthesize information effectively, while also having a broad enough perspective to answer all the questions you may potentially be asked about a project. Curiosity is also very important. When someone tells us, “I’d like to do this project” or “I’d like to implement this,” the first question we ask is: why? There are a lot of “whys” in our profession. Finally, you need to be patient and able to communicate with many different types of people.

How did you become a Data Analyst?

It was during my third-year internship at IÉSEG that I discovered my interest in data analysis. It was my first real exposure to the field.

In my fourth year, I wanted to explore the subject further, so I chose the dual-degree Master’s program in Data Analytics. It allowed me to combine my interest in marketing — particularly consumer habits and behavior — with data, enabling me to analyze consumer behavior and help teams decide how to approach different projects based on their objectives.

There are many different career paths within the Data Analyst field, but they all share a few fundamentals: statistics and coding skills, for example. It is very difficult to enter this field without a solid foundation in these areas. The dual-degree program therefore gave me the opportunity to learn these fundamentals and, most importantly, understand how to apply them. Every company has its own platforms and tools, but the fundamentals remain the same.