The world has entered a data-driven era, where individuals and organisations must adapt to remain competitive. Accurate data modelling, the strategic use of artificial intelligence (AI) as a tool rather than a replacement, and strong critical thinking skills are essential for interpreting and applying data-driven insights effectively. With vast amounts of data generated daily, professionals in analytics need expertise in identifying trends, uncovering patterns and making informed, evidence-based decisions that drive innovation and efficiency across industries.

Dr Catherine Truxillo, director of Advanced Analytics Education at SAS, has been actively involved in shaping the journey of data scientists for over 20 years. Currently focused on implementing responsible machine learning models in AI systems, she shares key insights on the evolving role of data analytics in today’s world.

“There is a real risk of misusing machine learning techniques, owing to a lack of understanding the underlying models and data complexities. And it isn’t just a lack of knowledge that creates risk. There is the risk of intentional harm by bad actors using AI tools to take advantage of vulnerabilities. News sites report daily on data breaches, AI-assisted scams, political interference and more. Staying protected and relevant requires greater understanding of maths, statistics and data than was the case even 10 years ago.

“To secure the future through responsible, ethical and secure innovation, it is critical for professionals in all industries to have a foundation of AI literacy. One area that analytics professionals should pay close attention to is bias. Why is bias so important? Unwanted bias in AI systems can lead to unfair, inaccurate or harmful outcomes. For example, biased models might unfairly favour certain groups over others. Biased models can result in actions that take away an individual’s autonomy. They can reinforce flawed patterns from historical data. They can also land a company in legal trouble as governments increasingly require AI to be transparent and non-discriminatory.

“Unintended bias enters AI systems via all phases of the analytics life cycle and is particularly tricky to detect and mitigate. Fortunately, there are tools available to help. Some of these tools, such as data auditing, statistical fairness metrics and model explainability metrics, enable you to look for imbalances in the representativeness of your data, measure bias in predictive model outputs and interpret how your models make decisions. Still other techniques, such as reweighting, optimised fairness constraints and human-in-the-loop systems, assist in mitigating unwanted bias when it occurs. These techniques are more powerful in the hands of a knowledgeable analyst with a deep understanding of statistics and statistical data modelling.”

In today’s data-driven world, mastering statistical modelling and AI-driven analytics is no longer an option — it is a necessity. The BSc (Hons) in Statistical Data Modelling (BSM) at Sunway University’s School of Mathematical Sciences provides a strong foundation in statistical theory, advanced data analytics and AI-driven computational techniques. Designed to equip graduates with skills to tackle complex data challenges, the programme blends mathematical rigour with AI-powered tools and real-world case studies to succeed in the ever-changing data landscape. From machine learning and deep learning to predictive modelling, AI-driven techniques are revolutionising industries. Professionals trained in statistical modelling can extract meaningful insights from vast datasets, optimise predictive models and make data-backed decisions that drive innovation. This expertise is crucial in industries such as healthcare, finance, technology and environmental science, where data-driven decisions can lead to groundbreaking advancements. Statistical modelling also plays a vital role in understanding uncertainty, assessing risk and making reliable forecasts, helping professionals make informed decisions in business, research and policymaking.

The impact of statistical modelling extends beyond theory — it is shaping the real world. In healthcare, AI-powered models improve disease forecasting, patient care and pharmaceutical research. In finance, they enhance risk assessment, fraud detection and automated trading. Businesses leverage AI-powered analytics to understand consumer behaviour, refine marketing strategies and boost operational efficiency. Meanwhile, environmental scientists use AI-assisted statistical modelling for climate forecasting, pollution control and sustainability planning. As industries become increasingly reliant on data-driven insights, professionals with expertise in both statistical modelling and AI are in high demand.

While AI has transformed the way data is processed and analysed, it remains a tool, not a replacement for human expertise. AI can automate processes and detect patterns, but human oversight is essential to validate models, interpret results and ensure ethical data use. The ability to distinguish correlation from causation, mitigate biases and make strategic decisions requires critical thinking that AI alone cannot replicate.

The BSc (Hons) in Statistical Data Modelling at Sunway University prepares graduates not just to leverage AI, but also apply data insights responsibly and drive meaningful change in the digital era. As the demand for skilled data professionals continues to grow, this programme empowers students to lead the future of data-driven decision-making.