From Research to Practice: building an Analytics career

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Get valuable insights and hard-won lessons from our Talent Spotlight: Tatiana Rešovská

Understanding how data reflects real lives is a skill that develops over time through study, curiosity, and hands-on experience.

In this Talent Spotlight, Tatiana Rešovská shares how her academic background in data science and economics led to an in-depth analysis of social and material deprivation using European survey data. From learning how to ask the right analytical questions to applying SAS software in thesis research and beyond, she shares insights into her studies, her transition into a global treasury role, and advice for students working on data-driven research today. Explore Tatiana's full Master's final thesis here.

What did you study at university, and what inspired you to choose that field? What was your learning journey like?

"I studied Data Science in Economics at the University of Economics in Bratislava, Faculty of Economic Informatics. I also hold a Bachelor’s degree in Accounting, which has given me a strong understanding of how economic indicators and financial reporting function in practice. I started with the fundamentals, such as statistics and data preparation, and gradually progressed to applied modelling and interpretation. What I have learned most is that analysis is not just applying a method; it’s about asking the right questions, defining variables accurately, and being able to explain results clearly. What inspired me to pursue data science was the idea that numbers can explain real-life outcomes. I’ve always been interested in questions like who is affected, how much, and why, and data analysis felt like the right way to answer them in a structured way."

What led you to select your thesis topic, and why did you decide to use SAS software?

"I chose my thesis topic because I wanted to work with a socially relevant issue using real European survey data.  My research focused on severe material and social deprivation (SMSD) in Slovakia and the Czech Republic, examining how the risk changed between 2019 and 2023, based on EU-SILC data. The crisis period after 2019 made the topic even more relevant because it affected households differently depending on their situation. I decided to use SAS Enterprise Guide because it is reliable for statistical modelling, especially with larger datasets, and it’s commonly used in academic and institutional analysis. My main analytical method was binary logistic regression, which I complemented with marginal means and contrasts to make group comparisons more interpretable. In SAS, I worked mainly with procedures such as LOGISTIC and GENMOD, and statements including LSMEANS, CONTRAST, and EFFECTPLOT."

How would you describe your experience with SAS?

"I genuinely enjoyed working with SAS. It’s a powerful tool that keeps your analysis organised and makes the whole process very systematic. It supported my work from start to finish, helped me stay consistent in my methodology, and made the entire analytical process much smoother and more efficient."

When did you graduate, and what are you doing now compared to what you expected?

"After graduating in May 2025, my goal was to move into a role where I could work with data in a structured way and apply analytics to real business processes, ideally within an international environment. My current position aligns well with that direction. I work as a Global Treasury Operations Analyst at an international company, where I focus mainly on cash flow management, financial reporting, and preparing data for treasury controlling. I also support cash flow forecasting, where I can directly apply the analytical mindset I developed at university, especially in terms of working with data quality, consistency, and turning numbers into actionable insights."

Which skills from university do you think you’ll use most in your career, and where do you think your studies could have better prepared you?

"The skills from university that I expect to use most in my career are mainly analytical thinking, statistical reasoning, and the ability to work with data in a structured and reliable way. During my studies, I built strong foundations in data preparation, validation, and interpretation, which are essential in any analytical role. I also learned how to approach a problem logically, choose appropriate methods, and translate results into clear conclusions that can support decision-making. Overall, I feel that my studies prepared me very well, and I see them as a solid basis for my future career development, particularly in roles where data analysis and practical business understanding need to work together."

What advice would you give to current students preparing their thesis using SAS or working on data-driven research?

"When working on a thesis in SAS or on any data-driven research, my main advice is to stay systematic from the very beginning. It helps a lot to start with a clear research question and a simple analytical plan and then build the work step by step instead of trying to do everything at once. In SAS, it’s easy to produce a large amount of output quickly, but the real quality of the analysis depends on how well you understand your data and how carefully you set-up each step. I would recommend spending enough time on data preparation, checking missing values, defining categories correctly, and being consistent with coding and reference groups, as even small details can strongly influence the interpretation later. Visualisations and structured comparisons can help a lot, especially when working with multiple categories or comparing results across different years."

From academic research to applied financial analytics, Tatiana’s journey shows how strong methodological foundations and thoughtful use of tools like SAS can support a confident transition from study into professional practice.

Want to read more stories from academic researchers shaping the future of analytics?

Hardus Bodenstein | Planning for Success in Data-Driven Research and Business

Miriam Kánová | From Math to meaningful data: a graduate’s Clinical Research journey with SAS

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[MurrayDVilliers]

This offers some solid advice to students and young professionals alike (as well as to the not-so-young ;-))