Afleveringen
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Ask the Right Questions
Entering the analytics profession and discovering that 80 per cent of the job is cleaning dirty data is a reality check that no course prepares you for. Some say that analysts who thrive in an AI-augmented data science career, will be defined not by their ability to prompt a model, but by their capacity to ask the right questions and critically evaluate the answers.
Today we learn how the team at RBI is building knowledge graphs to ground LLMs in real organisational context, why SQL coding skills are declining in value while critical analysis is rising and what should change about the MSBA curriculum, including a stronger focus on ethics, data lineage, and AI-augmented workflows.
We are joined by Naida Dzigal, a 2023 graduate of the CEU Master of Science in Business Analytics programme and IT expert at Raiffeisen Bank International. With a PhD in technical physics and prior experience as a nuclear specialist at the International Atomic Energy Agency, Naida brings a rare combination of scientific rigour, multilingual diplomacy, and real-world analytics leadership to this conversation.
THINGS WE SPOKE ABOUT
- From nuclear physics to banking analytics via the MSBA
- Dirty data, frustration tolerance and the analytics reality check
- Using AI for 80 per cent of the working day at RBI
- Building knowledge graphs and context layers to ground LLMs
- Redesigning the MSBA curriculum for an AI-augmented analytics world
GUEST DETAILS
Naida Dzigal is an IT expert at Raiffeisen Bank International (RBI), where she is part of a strategic data transformation team reshaping how data is managed across the bank's entire network, influencing data processes that affect billions of euros annually. A physicist by training, she holds a PhD in technical physics from Technical University Vienna and previously served as a nuclear specialist at the International Atomic Energy Agency. She completed her Master of Science in Business Analytics at CEU in 2023 and speaks five languages, bringing scientific rigour and cross-cultural professional experience to the field of data and AI.
QUOTES
- "I was just so surprised that I was getting paid for essentially cleaning up data and spending maybe 20% of my time actually doing real analytical work." - Naida Dzigal
- "The biggest experts always have the highest frustration tolerance." - Naida Dzigal
- "I think where we fail most of the time is in our critical analysis of the answer." - Naida Dzigal
- "LLMs are very powerful. AI in general is very powerful, but it lacks context." - Naida Dzigal
- "The students that will succeed are the students who are able to ask the right questions." - Naida Dzigal
KEYWORDS
#AiDataScience #AnalyticsCareer #KnowledgeGraphs #DataTransformation #BusinessAnalytics -
Taste Over Technique
Teaching data analytics in the age of AI demands a fundamental rethink of what students need to learn, how they are assessed, and what analytical competence actually means when AI can write the code, run the models, and produce the results.
This episode explores the rapid collapse of traditional assessment, shares why taste may be the most valuable skill a data analyst can develop, and explains why working in teams of humans and AI agents is the direction the entire profession is heading. Our guest also reflects on how his Data Analysis with AI course at CEU has had to be redesigned with each passing semester and what universities must do differently if they are to stay relevant.
Our guest is Gabor Bekes, Professor of Economics and Programme Head of the MSBA at Central European University, Budapest. An applied economist whose research spans international trade, open source software collaboration, and industrial policy, Gabor is co-author of Data Analysis for Business, Economics, and Policy, published by Cambridge University Press, and has been teaching data analysis for over 15 years.
THINGS WE SPOKE ABOUT
- Why asking good questions matters more than choosing the right method
- The Data Analysis with AI course and what students discover
- How rapidly improving AI models overhauled the curriculum
- Taste as the emerging signal of analytical competence
- Zero-tech exams, dark factories, and the future of curriculum design
GUEST DETAILS
Gabor Bekes is Professor of Economics and Programme Head of the MSBA at Central European University, Budapest. An applied economist whose research spans international trade, open source software collaboration, and industrial policy, he is co-author of Data Analysis for Business, Economics, and Policy (Cambridge University Press) and has taught data analysis for over 15 years.
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