In this interview, lecturer Mostafa Khatami from the University of Wollongong shares how he discovered his passion for optimising systems and explains how Business Analytics helps people make smarter decisions in areas like Finance, Supply Chain Management, Marketing, and Agriculture.
He also talks about why it’s important to understand the story behind the data, how generative AI is changing the way students are assessed, and why basic coding and Machine Learning skills matter today.
Key takeaways:
- Easily understand what Business Analytics is through simple examples that explain the key concepts.
- Machine Learning is now a basic skill in Business Analytics, no longer a special advantage but a core expectation for all students.
- Find out why context matters when interpreting data, and how AI is reshaping assessments and future skills.
Mostafa Khatami’s journey into Business Analytics combines Engineering, Mathematics, and real-world problem-solving. After completing his Bachelor’s and Master’s in Industrial Engineering in Iran, he moved to Australia for a PhD in Mathematics focused on Operations Research, followed by research roles at the Queensland University of Technology.
Now a Lecturer in Business Analytics at the University of Wollongong, Mostafa Khatami focuses on solving real problems that help businesses run smoothly. This includes optimising schedules, choosing the best locations for services, and improving how goods move from one place to another. His research is published in respected academic journals, and in 2023 he received a national Rising Star Award for his growing influence in the field.
Studyportals: How did you find your passion for business analytics?
Mostafa Khatami: I was in the final year of my high school in Iran and I was exploring what degree to pursue. Back then I was very much interested in Mechanical Engineering, but at one point I read a description of an Industrial Engineering programme. It said that it was mostly about finding ways to optimise systems. This word, “optimisation”, clicked in my mind. So I started with a Bachelor’s in Industrial Engineering, then a Master’s in the same field, and then I came to Australia.
But here, they didn’t have this discipline, only the so-called Operations Research, which is the optimisation part of Industrial Engineering. So I did my PhD in Mathematics, because in Australia the Mathematics Schools are the ones that include the Operations Research area. I continued with a postdoc and then became a lecturer here at Wollongong.
Studyportals: Looking back, what early experiences shaped your interest in maths and technical education?
Mostafa Khatami: I've always been interested in maths. I'm interested in numbers, so I was a numbers person. I knew I wanted Engineering, but I wasn’t sure which direction. There are so many branches in Engineering. At the same time, I was interested in Economics. I wanted engineering, but economics is not engineering. So Industrial Engineering, which we now call Business Analytics, was the closest to economics.
What Business Analytics really means in simple, real-world examples
Studyportals: Many high school students struggle to choose a degree. What advice would you give them?
Mostafa Khatami: I would say the most important thing is to love what you will do later in your career. Find out what your daily work life will look like when you choose that degree. For example, look up what an industrial engineer does, what a business analyst does every day, or the daily work of any other career. See if you like it, because it is going to be the rest of your life. You have to love it.
Look up what an industrial engineer does, what a business analyst does every day, or the daily work of any other career. See if you like it, because it is going to be the rest of your life. You have to love it.
Studyportals: How would you explain business analytics to a high school student exploring different study options?
Mostafa Khatami: Business analytics is about predicting the future using data.
For example, let’s say I'm running a coffee shop. If I record how many cappuccinos I sell every day, at one point I can use that information to estimate how many cappuccinos I can expect to sell in the future. But there are three steps to take to make that prediction, and each step is a branch of Business Analytics.
The first step is to describe history, to understand the pattern of how many cappuccinos I’ve been selling. This is called “descriptive analytics”.
The second step is to predict how many customers will show up next day, or next week, based on what I’ve seen before. This is “predictive analytics”.
The last step is about making decisions to prepare for what comes next. “Making decisions” is the key part of business analytics. For example, how many people do I need to hire? How many boxes of coffee beans should I buy? This is “prescriptive analytics”, using historical data to make better decisions.
Learning to work together with experts from different industries
Studyportals: How is business analytics used across different fields and industries?
Mostafa Khatami: Whether it is management, human resources, marketing, supply chain management, finance, or any other discipline, Business Analytics can solve their problems.
If I'm talking about finance, Business Analytics can tell us, for example, if we have different options to buy different stocks, we can predict stock prices for the future. We can also make optimal decisions. If it’s supply chain management, analytics can improve fuel costs and delivery times by calculating the shortest route. In agriculture, they can use analytics to find out which crop will be best, or what proportion of a large field should be allocated [MK1] to each crop.
Studyportals: Do students need prior experience in these fields before joining a Business Analytics degree?
Mostafa Khatami: Business Analytics is applicable to any industry, and we could never specialise in all of them. What happens is this: as an analyst, a client comes to you with a question or problem. They know their business. We know how to solve the problem. It becomes a collaboration between someone who is an expert in the field and someone who is an expert in solving problems and making decisions.
Studyportals: What makes this collaboration work well?
Mostafa Khatami: I always tell my students: knowing the context is very important. If I have data in an Excel file, I can always make conclusions and do the prescriptive analytics. But if I don’t know the context, I may come up with a decision that makes no sense in the real world and can even cause damage, or sound ridiculous to an expert.
We must always understand the meaning of those numbers. To get context, we need experts from that industry or business, because they know what matters and what doesn’t.
Knowing the context is very important. If I have data in an Excel file, I can always make conclusions and do the prescriptive analytics. But if I don’t know the context, I may come up with a decision that makes no sense in the real world.
Studyportals: What hands-on learning do your students experience to prepare for jobs after graduation?
Mostafa Khatami: In our capstone subjects, we give them the freedom to explore any business they want, depending on what they love. Here people usually do double majors. For example, one major is Business Analytics and the second is usually something else from the Business School. If a student does Business Analytics and Finance, they usually come with a finance question to solve using analytics tools.
They have a full semester to work on a project. They select the topic, the business, and then they apply predictive and prescriptive analytics to it.
Why Business Analytics is for students who love working with numbers
Studyportals: Do you have many international students in your programme?
Yeah, in undergrad I think it's around 95 percent. It’s mostly international students, usually from Asia, but also from Europe, South America, Africa, from everywhere.
Studyportals: What technical skills should students have before starting a Business Analytics degree?
Mostafa Khatami: Business Analytics deals with numbers. If a student doesn’t like numbers, it’s probably not a good degree for them. We also have programming and coding.
But the coding is not as heavy as in Computer Science or Computer Engineering. We use similar machine learning[MK2] tools as they do, but the difference in Business Analytics is that we use machine learning to make decisions. Machine learning is everywhere now. Everyone is talking about it. That is the basics. Everyone in Business Analytics should be able to do machine learning. It’s not a competitive edge; it’s something we all should know.
Everyone in Business Analytics should be able to do machine learning. It’s not a competitive edge; it’s something we all should know.
Studyportals: AI and machine learning are evolving fast. How has AI changed Business Analytics in recent years, and what changes do you expect next?
Mostafa Khatami: It's been a big shift since we have generative AI. Everyone is using it. Students use it, staff members use it. I can see a higher rate of usage every year.
It has changed how we design subjects and assessments. In business schools, assessments are usually case studies and projects that students do at home. With generative AI, the writing part is mostly done by AI.
I would recommend students to write first and use AI to improve their writing, rather than asking AI to write it for them. They need writing skills.
We now emphasise critical thinking in assessments. Some people have gone back to old-school exams, asking students to sit in a classroom for two hours on paper. I don’t think this is a solution. We need ways to assess critical thinking. When assessments still involve projects, students can use AI, but we must learn how to assess their contribution and their thinking.
Studyportals: What makes your Business Analytics programme stand out from others in Australia?
Mostafa Khatami: I would say that our programme is a mix of all parts. We don’t emphasise only predictive analytics, which is common in many institutions. We have a balance of predictive and prescriptive analytics. We try to give that competitive edge to students.
