By Ts Dr. Hj Ramli Amir, former President of the Chartered Institute of Logistics and Transport (CILT) Malaysia and Vice-President of CILT International for Southeast Asia
KOTA KINABALU: In today’s world, data is at the heart of decision-making processes across industries. A recent study found that 91% of companies acknowledge the critical role of data-driven decision-making in their growth. Furthermore, 57% of these companies have already started integrating data into their strategies.
With this increasing reliance on data, the demand for analytics professionals has surged.
The US Bureau of Labor Statistics projected a 15% rise in data-related jobs from 2009 to 2020, significantly surpassing the national average job growth rate of 4%. Businesses now recognize that without data-driven strategies, success in the modern landscape is nearly impossible. This reliance on data has led to an explosion in roles related to data collection, interpretation, and application.
In Malaysia, the push towards digital transformation has been evident in both public and private sectors. Government initiatives like MyDIGITAL and the National Fourth Industrial Revolution (4IR) Policy highlight the country’s ambition to become a data-driven economy. Companies across industries—from banking and telecommunications to logistics and retail—are investing heavily in data analytics to stay competitive. Organizations such as PETRONAS, Maybank, and AirAsia have been at the forefront of implementing data-driven strategies, setting a precedent for other industries.
If you are an aspiring data enthusiast, you might have several questions. What is data science? What do data scientists do? How do their roles differ from data analysts? What specific skills are required for different roles, and how should one begin? By the end of this discussion, you will not only understand the distinctions between various roles but also gain insight into how to choose a suitable career path in the data field.
The Three Pillars of the Data Industry
Data Architect: The Master Planner
Among all data-related roles, the data architect is the most experienced and strategic. Their role is to design the foundation upon which all data operations run. Much like an urban planner designs a city for sustainable growth, a data architect envisions an IT blueprint that will last for the next 10 to 20 years.
Creating an IT blueprint is no simple task—it requires months of study, meetings, and signoffs from clients. Once the design is finalized, the data architect guides data engineers in implementing the plan. They oversee the process, ensuring that engineers build a robust data framework that aligns with business goals.
In Malaysia, data architects are in high demand, particularly in industries like finance, telecommunications, and logistics. Government-linked companies such as Telekom Malaysia and Tenaga Nasional Berhad (TNB) have been developing robust IT infrastructures that align with their long-term digital strategies. However, the challenge remains in developing local talent, as becoming a data architect requires deep industry-specific knowledge. It is often easier to train a subject matter expert in data architecture than to train a fresh graduate in both technology and business domain expertise.
Data Engineer: The Builders of the Data World
While data architects design the foundation, data engineers are the builders, making their job both tough and lucrative. In fact, these are some of the highest-paying positions in the field. Their work is labour-intensive, requiring them to extract data from various sources, clean and process it, and ultimately deposit it into a data Lakehouse—a centralized repository of well-structured, accessible data.
The work of a data engineer is similar to that of a chef preparing fresh ingredients in a kitchen. Data, like raw vegetables, is gathered from farms, washed, cut, and processed before being neatly stored in the kitchen (data Lakehouse). Only after the data is structured and stored properly does it become useful for analysis.
In Malaysia, companies like Axiata Group and CIMB Bank are investing in data engineering capabilities to streamline operations and enhance customer experiences. Startups and technology firms, such as Carsome and Grab Malaysia, are also scaling their data engineering teams to handle massive amounts of real-time data. These professionals play a crucial role in ensuring that businesses can make data-backed decisions efficiently.
Because their work is foundational, data engineers frequently interact with both data architects and data scientists. Once a week or fortnight, they present interim results to clients. Since preliminary outputs may not resemble the final product, the data architect plays an essential role in explaining them.
Clients also tend to request last-minute changes, which can lead to new signoffs or adjustments in the project scope. In some cases, these modifications result in additional costs, while at other times they are absorbed by the project team.
Our focus is on the high-paying field of data engineering—where skilled professionals can build robust infrastructures that power the entire data-driven ecosystem.
Data Scientist: The Creative Innovator
If data engineers are the builders, then data scientists are the chefs who cook with the prepared ingredients. Their job is to take clean, structured data and transform it into meaningful insights through artificial intelligence and predictive analytics. Given their role in crafting cutting-edge AI models, data scientists are often regarded as having the ‘sexiest job’ in the field.
Unlike data engineers, who focus on building robust infrastructure, data scientists specialize in leveraging data for innovation. Their work includes detecting anomalies, predicting future trends, and optimizing business strategies through machine learning. The data is already neatly prepared in the data Lakehouse—allowing them to focus on creating actionable insights rather than spending time on data cleaning.
In Malaysia, the government is aggressively pushing to develop more data scientists. Through initiatives like the Malaysia Digital Economy Blueprint (MyDIGITAL) and collaborations with universities, the focus is on producing professionals skilled in artificial intelligence, machine learning, and predictive analytics. Companies such as Khazanah Nasional and MDEC are leading efforts to train and upskill talent in this field.
The Future of Data Careers in Malaysia
The demand for skilled professionals in data architecture, engineering, and science is growing rapidly. As businesses continue to rely on data for strategic planning, the need for specialists in each of these domains will only increase.
For individuals considering a career in this field, now is an opportune time to acquire the necessary skills and enter the industry. With the right combination of technical knowledge, business acumen, and practical experience, a career in data science, engineering, or architecture can be both rewarding and impactful.
Understanding the distinctions between these roles and identifying the right path based on personal interests and skillsets will ensure success in this data-driven era. The journey into the world of data is both exciting and filled with opportunities, making it a promising career choice for the future in Malaysia’s rapidly evolving digital landscape.
Sabah’s Current Landscape
The state has been actively pursuing digital transformation to enhance its economic development. Initiatives such as the Sabah Education Sponsorship Portal have been launched to centralize educational support, particularly for students aiming to pursue higher education in fields related to data and technology.
Despite these efforts, challenges persist. A significant portion of Sabah’s workforce is engaged in elementary occupations, and the state has faced issues related to poverty and educational attainment.
These factors contribute to a limited local pool of data professionals, necessitating targeted strategies to develop and retain talent in this sector.
Strategies for Developing Local Data Professionals in Sabah
To address the shortage of data professionals and build a sustainable talent pipeline, Sabah can consider the following approaches:
Educational Initiatives: Collaborate with local universities and technical institutions to introduce specialized programs in data science, analytics, and engineering. Curricula should be aligned with industry needs to ensure graduates are job ready.
Upskilling Programs: Implement training initiatives aimed at reskilling the existing workforce. Programs like Malaysia’s Data Star Program by MDEC focus on equipping professionals with advanced data analytics and AI skills, enabling them to meet current market demands.
Industry Partnerships: Foster collaborations between the government, educational institutions, and private sector companies to create internship and apprenticeship opportunities. Such partnerships can provide practical experience and facilitate the transition from education to employment.
Investment in Infrastructure: Developing a robust digital infrastructure is essential for supporting data-related activities. Investments in high-speed internet, data centers, and related technologies will create an environment conducive to the growth of data professions.
Community Engagement: Encourage the formation of local tech communities and forums where professionals can share knowledge, collaborate on projects, and stay updated with industry trends.
Attractive Career Pathways: Offer competitive salaries and clear career progression routes to retain talent within the state. Highlighting the impact of data roles on local industries can also enhance the appeal of these careers.
Conclusion
As the global economy becomes increasingly data-driven, regions like Sabah must proactively develop their local talent to remain competitive. By implementing targeted educational programs, fostering industry collaborations, and investing in digital infrastructure, Sabah can cultivate a skilled workforce capable of driving its digital transformation and economic growth.
