How Data Science is Powering Predictive Analytics in Malaysia

Assoc. Prof. Ts. Dr. Noryanti Muhammad, Centre for Mathematical Sciences, Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) in an interaction with Raja Ramya R, Correspondent, Higher Education Review shared her views on how predictive and prescriptive analytics can drive Malaysia’s transition toward a high-value digital economy, how does Malaysia’s current data infrastructure compare to other ASEAN nations in enabling data-driven economic growth and more.  

Assoc. Prof. Ts. Dr. Noryanti Muhammad is an academic and researcher at Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA), specialising in applied statistics, data analytics, and quantitative and qualitative modelling. She is actively involved in program and curriculum development, industry-linked research, and capacity building in analytics, with a strong focus on predictive and prescriptive decision-making.

How can predictive and prescriptive analytics drive Malaysia’s transition toward a high-value digital economy?

Predictive and prescriptive analytics drive high-value digital economies globally by enabling organizations and governments to anticipate future scenarios, optimize decisions, and proactively manage risks rather than reacting to past outcomes. These capabilities allow economies to move up the value chain by improving productivity, fostering innovation, and strengthening resilience through data-driven foresight. In Malaysia, this transition is critical to shifting from cost and labor driven growth to intelligence driven growth under national initiatives such as Malaysia MADANI and MyDIGITAL.

What are the most critical industries in Malaysia (e.g., manufacturing, logistics, healthcare, agriculture) that would benefit most from advanced analytics capabilities?

In a global perspective, industries characterized by complex systems, high uncertainty, and large-scale operations such as manufacturing, logistics, healthcare, and agriculture derive the greatest benefits from predictive and prescriptive analytics. In these sectors, predictive analytics enables early detection of equipment failures, demand fluctuations, disease outbreaks, and climate-related risks, while prescriptive analytics supports optimal decision-making through production scheduling, inventory optimization, resource allocation, and policy intervention planning.

Collectively, these capabilities lead to reduced operational costs, improved service reliability, enhanced productivity, and stronger resilience to disruptions. In Malaysia, these same industries form the backbone of the national economy but continue to face challenges related to operational inefficiencies, sustainability pressures, and global competitiveness. By applying advanced analytics, organizations can improve supply chain visibility, reduce waste and downtime, enhance healthcare service delivery, and increase agricultural productivity under uncertain environmental conditions.

How does Malaysia’s current data infrastructure compare to other ASEAN nations in enabling data-driven economic growth?

Globally, strong data infrastructure is defined not only by high levels of digital connectivity, but also by effective data integration, interoperability across systems, and robust data governance frameworks that ensure quality, security, and ethical use. While Malaysia performs relatively well compared to many ASEAN countries in terms of broadband penetration, cloud adoption, and digital readiness, it still lags behind regional leaders such as Singapore in deploying analytics at scale and achieving seamless data integration across institutions. Challenges such as fragmented data ownership, siloed information systems, inconsistent data standards, and uneven data quality continue to limit the full value of analytics. Recognizing these issues, Malaysia has introduced national initiatives such as MyDIGITAL, the Malaysia Digital Economy Blueprint, the National AI Roadmap, and expanded open data efforts through platforms like data.gov.my to strengthen data sharing and governance.

What policies or incentives could accelerate industry-wide adoption of data science and AI solutions?

International experience shows that analytics adoption accelerates when governments provide clear incentives such as tax relief, targeted funding schemes, regulatory sandboxes, and national data sharing platforms. In Malaysia, similar mechanisms are already being pursued through existing and planned initiatives, including the Malaysia Digital Economy Blueprint (MyDIGITAL), the National Artificial Intelligence Roadmap, Industry4WRD, and incentives administered by agencies such as MDEC, MOSTI, and SME Corp. These policies aim to lower adoption barriers by supporting digital investment, cloud migration, AI experimentation, and data driven innovation, particularly for SMEs that face financial and capability constraints. However, effective implementation requires strong evidence on impact, scalability, and sector readiness.

What are the biggest gaps between Malaysia’s current data analytics capabilities and what’s needed for predictive and prescriptive maturity?

Globally, a common challenge in many developing economies is the overreliance on descriptive analytics, where organizations focus primarily on dashboards, static reports, and historical summaries that explain what has happened, but provide limited insight into what is likely to happen next or what actions should be taken. While such tools improve transparency, they do not support proactive decision-making or strategic optimization. Malaysia faces a similar challenge, where analytics adoption is often confined to performance monitoring and compliance reporting, rather than forecasting future demand, risks, or outcomes, and optimizing decisions under uncertainty. This gap is further compounded by limited expertise in advanced areas such as decision analytics, optimization, simulation, and scenario analysis, which are essential for prescriptive analytics maturity. As a result, many organizations struggle to translate data into actionable strategies, particularly in complex environments such as supply chains, public services, and SME operations. 

How can SMEs - often limited by data and budgets build affordable predictive analytics capabilities?

Globally, SMEs achieve success in analytics adoption when solutions are incremental, cloud based, and focused on actionable decision-making, rather than being overly complex or expensive. In Malaysia, SMEs including micro and small enterprises (MSEs) make up over 97% of all businesses and contribute significantly to employment and GDP. Recognizing their strategic importance, the Malaysian government has implemented multiple initiatives to support MSE growth and digital adoption. Programs such as SME Corp’s Business Advisory Services, the SME Digitalisation Grant, and MyDIGITAL initiatives provide financial incentives, capacity building programs, and access to technology to encourage digital transformation. Similarly, regulatory agencies like SSM and Bank Negara Malaysia offer guidance on compliance, business registration, and financing, while platforms such as Malaysia Digital Economy Corporation (MDEC) facilitate technology adoption, e-commerce, and data driven practices among SMEs.