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To mitigate the challenges of cloud vendor lock-in, 44% of organisations in Thailand will prioritise data centre consolidation and infrastructure modernisation in 2024.
Explore the key trends impacting Thailand’s technology adoption. Keep an eye out for more data-backed insights on tech markets across Southeast Asia.
ASEAN, poised to become the world’s 4th largest economy by 2030, is experiencing a digital boom. With an estimated 125,000 new internet users joining daily, it is the fastest-growing digital market globally. These users are not just browsing, but are actively engaged in data-intensive activities like gaming, eCommerce, and mobile business. As a result, monthly data usage is projected to soar from 9.2 GB per user in 2020 to 28.9 GB per user by 2025, according to the World Economic Forum. Businesses and governments are further fuelling this transformation by embracing Cloud, AI, and digitisation.
Investments in data centre capacity across Southeast Asia are estimated to grow at a staggering pace to meet this growing demand for data. While large hyperscale facilities are currently handling much of the data needs, edge computing – a distributed model placing data centres closer to users – is fast becoming crucial in supporting tomorrow’s low-latency applications and services.
As technology pushes boundaries with applications like augmented reality, telesurgery, and autonomous vehicles, the demand for ultra-low latency response times is skyrocketing. Consider driverless cars, which generate a staggering 5 TB of data per hour and rely heavily on real-time processing for split-second decisions. This is where edge data centres come in. Unlike hyperscale data centres, edge data centres are strategically positioned closer to users and devices, minimising data travel distances and enabling near-instantaneous responses; and are typically smaller with a capacity ranging from 500 KW to 2 MW. In comparison, large data centres have a capacity of more than 80MW.
While edge data centres are gaining traction, cloud-based hyperscalers such as AWS, Microsoft Azure, and Google Cloud remain a dominant force in the Southeast Asian data centre landscape. These facilities require substantial capital investment – for instance, it took almost USD 1 billion to build Meta’s 150 MW hyperscale facility in Singapore – but offer immense processing power and scalability. While hyperscalers have the resources to build their own data centres in edge locations or emerging markets, they often opt for colocation facilities to familiarise themselves with local markets, build out operations, and take a “wait and see” approach before committing significant investments in the new market.
The growth of data centres in Southeast Asia – whether edge, cloud, hyperscale, or colocation – can be attributed to a range of factors. The region’s rapidly expanding digital economy and increasing internet penetration are the prime reasons behind the demand for data storage and processing capabilities. Additionally, stringent data sovereignty regulations in many Southeast Asian countries require the presence of local data centres to ensure compliance with data protection laws. Indonesia’s Personal Data Protection Law, for instance, allows personal data to be transferred outside of the country only where certain stringent security measures are fulfilled. Finally, the rising adoption of cloud services is also fuelling the need for onshore data centres to support cloud infrastructure and services.
Singapore. Singapore imposed a moratorium on new data centre developments between 2019 to 2022 due to concerns over energy consumption and sustainability. However, the city-state has recently relaxed this ban and announced a pilot scheme allowing companies to bid for permission to develop new facilities.
In 2023, the Singapore Economic Development Board (EDB) and the Infocomm Media Development Authority (IMDA) provisionally awarded around 80 MW of new capacity to four data centre operators: Equinix, GDS, Microsoft, and a consortium of AirTrunk and ByteDance (TikTok’s parent company). Singapore boasts a formidable digital infrastructure with 100 data centres, 1,195 cloud service providers, and 22 network fabrics. Its robust network, supported by 24 submarine cables, has made it a global cloud connectivity leader, hosting major players like AWS, Azure, IBM Softlayer, and Google Cloud.
Aware of the high energy consumption of data centres, Singapore has taken a proactive stance towards green data centre practices. A collaborative effort between the IMDA, government agencies, and industries led to the development of a “Green Data Centre Standard“. This framework guides organisations in improving data centre energy efficiency, leveraging the established ISO 50001 standard with customisations for Singapore’s context. The standard defines key performance metrics for tracking progress and includes best practices for design and operation. By prioritising green data centres, Singapore strives to reconcile its digital ambitions with environmental responsibility, solidifying its position as a leading Asian data centre hub.
Malaysia. Initiatives like MyGovCloud and the Digital Economy Blueprint are driving Malaysia’s public sector towards cloud-based solutions, aiming for 80% use of cloud storage. Tenaga Nasional Berhad also established a “green lane” for data centres, solidifying Malaysia’s commitment to environmentally responsible solutions and streamlined operations. Some of the big companies already operating include NTT Data Centers, Bridge Data Centers and Equinix.
The district of Kulai in Johor has emerged as a hotspot for data centre activity, attracting major players like Nvidia and AirTrunk. Conditional approvals have been granted to industry giants like AWS, Microsoft, Google, and Telekom Malaysia to build hyperscale data centres, aimed at making the country a leading hub for cloud services in the region. AWS also announced a new AWS Region in the country that will meet the high demand for cloud services in Malaysia.
Indonesia. With over 200 million internet users, Indonesia boasts one of the world’s largest online populations. This expanding internet economy is leading to a spike in the demand for data centre services. The Indonesian government has also implemented policies, including tax incentives and a national data centre roadmap, to stimulate growth in this sector.
Microsoft, for instance, is set to open its first regional data centre in Thailand and has also announced plans to invest USD 1.7 billion in cloud and AI infrastructure in Indonesia. The government also plans to operate 40 MW of national data centres across West Java, Batam, East Kalimantan, and East Nusa Tenggara by 2026.
Thailand. Remote work and increasing online services have led to a data centre boom, with major industry players racing to meet Thailand’s soaring data demands.
In 2021, Singapore’s ST Telemedia Global Data Centres launched its first 20 MW hyperscale facility in Bangkok. Soon after, AWS announced a USD 5 billion investment plan to bolster its cloud capacity in Thailand and the region over the next 15 years. Heavyweights like TCC Technology Group, CAT Telecom, and True Internet Data Centre are also fortifying their data centre footprints to capitalise on this explosive growth. Microsoft is also set to open its first regional data centre in the country.
Southeast Asia’s booming data centre market presents a goldmine of opportunity for tech investment and innovation. However, navigating this lucrative landscape requires careful consideration of legal hurdles. Data protection regulations, cross-border data transfer restrictions, and local policies all pose challenges for investors. Beyond legal complexities, infrastructure development needs and investment considerations must also be addressed. Despite these challenges, the potential rewards for companies that can navigate them are substantial.
In today’s competitive business landscape, delivering exceptional customer experiences is crucial to winning new clients and fostering long-lasting customer loyalty. Research has shown that poor customer service can cost businesses around USD 75 billion in a year and that 1 in 3 customers is likely to abandon a brand after a single negative experience. Organisations excelling at personalised customer interactions across channels have a significant market edge.
In a recent webinar with Shivram Chandrasekhar, Solutions Architect at Twilio, we delved into strategies for creating this edge. How can contact centres optimise interactions to boost cost efficiency and customer satisfaction? We discussed the pivotal role of voice in providing personalised customer experiences, the importance of balancing AI and human interaction for enhanced satisfaction, and the operational advantages of voice intelligence in streamlining operations and improving agent efficiency.
Despite the rise of digital channels, voice interactions remain crucial for organisations seeking to deliver exceptional customer experiences. Voice calls offer nuanced insights and strategic advantages, allowing businesses to address issues effectively and proactively meet customer needs, fostering loyalty and driving growth.
There are multiple reasons why voice will remain relevant including:
There has been a growing trend towards ‘agentless contact centres’, where businesses aim to pivot away from human agents – but there has also been increasing customer dissatisfaction with purely automated interactions. A balanced approach that empowers human agents with AI-driven insights and conversational AI can yield better results. In fact, the conversation should not be about one or the other, but rather about a combination of an AI + Human Agent.
Where organisations rely on conversational AI, there must be a seamless transitioning between automated and live agent interactions, maintaining a cohesive customer experience. Ultimately, the goal should be to avoid disruptions to customer journeys and ensure a smooth, integrated approach to customer engagement across different channels.
Contact centres in Asia Pacific are looking to deploy AI capabilities to enhance both employee and customer experiences.
Using predictive AI algorithms on customer data helps organisations forecast market trends and optimise resource allocation. Additionally, AI-driven identity validation swiftly confirms customer identities, mitigating fraud risks. By automating transactional tasks, particularly FAQs, contact centre operations are streamlined, ensuring that critical calls receive prompt attention. AI-powered quality assurance processes provide insights into all voice calls, facilitating continuous improvement, while AI-driven IVR systems enhance the customer experience by simplifying menu navigation.
Agent Assist solutions, integrated with GenAI, offer real-time insights before customer interactions, streamlining service delivery and saving valuable time. These solutions automate mundane tasks like call summaries, enabling agents to focus on high-value activities such as sales collaboration, proactive feedback management, and personalised outbound calls.
Organisations possess a wealth of customer data from various touchpoints, including voice interactions. Accessing real-time, accurate data is essential for effective customer and agent engagement. Advanced analytics techniques can uncover hidden patterns and correlations, informing product development, marketing strategies, and operational improvements. However, organisations often face challenges with data silos and lack of interconnected data, hindering omnichannel experiences.
Integrating customer data with other organisational sources provides a holistic view of the customer journey, enabling personalised experiences and proactive problem-solving. A Customer Data Platform (CDP) breaks down data silos, providing insights to personalise interactions, address real-time issues, identify compliance gaps, and exceed customer expectations throughout their journeys.
Ecosystm research shows that customer engagement is emerging as the main beneficiary of AI implementations in Malaysia with 44% of AI investments being led by CX/Marketing/Sales teams.
Explore the key trends that are transforming the Malaysian technology landscape and stay tuned for more data-backed insights on Southeast Asia’s tech markets.
In my last Ecosystm Insight, I spoke about the importance of data architecture in defining the data flow, data management systems required, the data processing operations, and AI applications. Data Mesh and Data Fabric are both modern architectural approaches designed to address the complexities of managing and accessing data across a large organisation. While they share some commonalities, such as improving data accessibility and governance, they differ significantly in their methodologies and focal points.
Both approaches aim to overcome the challenges of data silos and improve data accessibility, but they do so through different methodologies and with different priorities.
The concepts of Data Mesh and Data Fabric are supported by various vendors, each offering tools and platforms designed to facilitate the implementation of these architectures. Here’s an overview of some key players in both spaces:
Data Mesh is more of a conceptual approach than a product-specific solution, focusing on organisational structure and data decentralisation. However, several vendors offer tools and platforms that support the principles of Data Mesh, such as domain-driven design, product thinking for data, and self-serve data infrastructure:
Data Fabric solutions often come as more integrated products or platforms, focusing on data integration, management, and governance across a diverse set of systems and environments:
The choice of vendor or tool for either Data Mesh or Data Fabric should be guided by the specific needs, existing technology stack, and strategic goals of the organisation. Many vendors provide a range of capabilities that can support different aspects of both architectures, and the best solution often involves a combination of tools and platforms. Additionally, the technology landscape is rapidly evolving, so it’s wise to stay updated on the latest offerings and how they align with the organisation’s data strategy.
Ecosystm research finds Sustainable IT is becoming integral to technology modernisation in Singapore, with 55% of organisations assessing the carbon emissions of their infrastructure – both cloud and on-premises.
Find out the key trends shaping Singapore’s technology landscape.
The Australian economy stands at the brink of a transformative era, driven by the fast adoption of AI. The country’s commitment to leveraging AI across critical sectors as articulated in initiatives such as the CSIRO’s Artificial Intelligence Roadmap, promises significant opportunities for growth and innovation.
This eBook, in partnership with Hewlett Packard Enterprise and NEC explores the key factors organisations need to consider when establishing their strategic AI roadmap. Download your copy.
Download eBook – Take Steps to Prepare Your Organisation for the Oncoming AI Tsunami
(Clicking on this link will take you to the NEC website where you can download the eBook)
GenAI has taken the world by storm, with organisations big and small eager to pilot use cases for automation and productivity boosts. Tech giants like Google, AWS, and Microsoft are offering cloud-based GenAI tools, but the demand is straining current infrastructure capabilities needed for training and deploying large language models (LLMs) like ChatGPT and Bard.
The microchip manufacturing process is intricate, involving hundreds of steps and spanning up to four months from design to mass production. The significant expense and lengthy manufacturing process for semiconductor plants have led to global demand surpassing supply. This imbalance affects technology companies, automakers, and other chip users, causing production slowdowns.
Supply chain disruptions, raw material shortages (such as rare earth metals), and geopolitical situations have also had a fair role to play in chip shortages. For example, restrictions by the US on China’s largest chip manufacturer, SMIC, made it harder for them to sell to several organisations with American ties. This triggered a ripple effect, prompting tech vendors to start hoarding hardware, and worsening supply challenges.
As AI advances and organisations start exploring GenAI, specialised AI chips are becoming the need of the hour to meet their immense computing demands. AI chips can include graphics processing units (GPUs), application-specific integrated circuits (ASICs), and field-programmable gate arrays (FPGAs). These specialised AI accelerators can be tens or even thousands of times faster and more efficient than CPUs when it comes to AI workloads.
The surge in GenAI adoption across industries has heightened the demand for improved chip packaging, as advanced AI algorithms require more powerful and specialised hardware. Effective packaging solutions must manage heat and power consumption for optimal performance. TSMC, one of the world’s largest chipmakers, announced a shortage in advanced chip packaging capacity at the end of 2023, that is expected to persist through 2024.
The scarcity of essential hardware, limited manufacturing capacity, and AI packaging shortages have impacted tech providers. Microsoft acknowledged the AI chip crunch as a potential risk factor in their 2023 annual report, emphasising the need to expand data centre locations and server capacity to meet customer demands, particularly for AI services. The chip squeeze has highlighted the dependency of tech giants on semiconductor suppliers. To address this, companies like Amazon and Apple are investing heavily in internal chip design and production, to reduce dependence on large players such as Nvidia – the current leader in AI chip sales.
NVIDIA, one of the largest manufacturers of GPUs, has been forced to pivot its strategy in response to this shortage. The company has shifted focus towards developing chips specifically designed to handle complex AI workloads, such as the A100 and V100 GPUs. These AI accelerators feature specialised hardware like tensor cores optimised for AI computations, high memory bandwidth, and native support for AI software frameworks.
While this move positions NVIDIA at the forefront of the AI hardware race, experts say that it comes at a significant cost. By reallocating resources towards AI-specific GPUs, the company’s ability to meet the demand for consumer-grade GPUs has been severely impacted. This strategic shift has worsened the ongoing GPU shortage, further straining the market dynamics surrounding GPU availability and demand.
Others like Intel, a stalwart in traditional CPUs, are expanding into AI, edge computing, and autonomous systems. A significant competitor to Intel in high-performance computing, AMD acquired Xilinx to offer integrated solutions combining high-performance central processing units (CPUs) and programmable logic devices.
Governments worldwide are boosting chip capacity to tackle the semiconductor crisis and fortify supply chains. Initiatives like the CHIPS for America Act and the European Chips Act aim to bolster domestic semiconductor production through investments and incentives. Leading manufacturers like TSMC and Samsung are also expanding production capacities, reflecting a global consensus on self-reliance and supply chain diversification. Asian governments are similarly investing in semiconductor manufacturing to address shortages and enhance their global market presence.
Japan is providing generous government subsidies and incentives to attract major foreign chipmakers such as TSMC, Samsung, and Micron to invest and build advanced semiconductor plants in the country. Subsidies have helped to bring greenfield investments in Japan’s chip sector in recent years. TSMC alone is investing over USD 20 billion to build two cutting-edge plants in Kumamoto by 2027. The government has earmarked around USD 13 billion just in this fiscal year to support the semiconductor industry.
Moreover, Japan’s collaboration with the US and the establishment of Rapidus, a memory chip firm, backed by major corporations, further show its ambitions to revitalise its semiconductor industry. Japan is also looking into advancements in semiconductor materials like silicon carbide (SiC) and gallium nitride (GaN) – crucial for powering electric vehicles, renewable energy systems, and 5G technology.
South Korea. While Taiwan holds the lead in semiconductor manufacturing volume, South Korea dominates the memory chip sector, largely due to Samsung. The country is also spending USD 470 billion over the next 23 years to build the world’s largest semiconductor “mega cluster” covering 21,000 hectares in Gyeonggi Province near Seoul. The ambitious project, a partnership with Samsung and SK Hynix, will centralise and boost self-sufficiency in chip materials and components to 50% by 2030. The mega cluster is South Korea’s bold plan to cement its position as a global semiconductor leader and reduce dependence on the US amidst growing geopolitical tensions.
Vietnam. Vietnam is actively positioning itself to become a major player in the global semiconductor supply chain amid the push to diversify away from China. The Southeast Asian nation is offering tax incentives, investing in training tens of thousands of semiconductor engineers, and encouraging major chip firms like Samsung, Nvidia, and Amkor to set up production facilities and design centres. However, Vietnam faces challenges such as a limited pool of skilled labour, outdated energy infrastructure leading to power shortages in key manufacturing hubs, and competition from other regional players like Taiwan and Singapore that are also vying for semiconductor investments.
Small language models (SLMs) offer reduced computational requirements compared to larger models, potentially alleviating strain on semiconductor supply chains by deploying on smaller, specialised hardware.
Innovative SLMs like Google’s Gemini Nano and Mistral AI’s Mixtral 8x7B enhance efficiency, running on modest hardware, unlike their larger counterparts. Gemini Nano is integrated into Bard and available on Pixel 8 smartphones, while Mixtral 8x7B supports multiple languages and suits tasks like classification and customer support.
The shift towards smaller AI models can be pivotal to the AI landscape, democratising AI and ensuring accessibility and sustainability. While they may not be able to handle complex tasks as well as LLMs yet, the ability of SLMs to balance model size, compute power, and ethical considerations will shape the future of AI development.
The data architecture outlines how data is managed in an organisation and is crucial for defining the data flow, data management systems required, the data processing operations, and AI applications. Data architects and engineers define data models and structures based on these requirements, supporting initiatives like data science. Before we delve into the right data architecture for your AI journey, let’s talk about the data management options. Technology leaders have the challenge of deciding on a data management system that takes into consideration factors such as current and future data needs, available skills, costs, and scalability. As data strategies become vital to business success, selecting the right data management system is crucial for enabling data-driven decisions and innovation.
A Data Warehouse is a centralised repository that stores vast amounts of data from diverse sources within an organisation. Its main function is to support reporting and data analysis, aiding businesses in making informed decisions. This concept encompasses both data storage and the consolidation and management of data from various sources to offer valuable business insights. Data Warehousing evolves alongside technological advancements, with trends like cloud-based solutions, real-time capabilities, and the integration of AI and machine learning for predictive analytics shaping its future.
A Data Lake is a centralised repository for storing, processing, and securing large volumes of structured and unstructured data. Unlike traditional Data Warehouses, which are structured and optimised for analytics with predefined schemas, Data Lakes retain raw data in its native format. This flexibility in data usage and analysis makes them crucial in modern data architecture, particularly in the age of big data and cloud.
A Data Lakehouse is an innovative data management system that merges the strengths of Data Lakes and Data Warehouses. This hybrid approach strives to offer the adaptability and expansiveness of a Data Lake for housing extensive volumes of raw, unstructured data, while also providing the structured, refined data functionalities typical of a Data Warehouse. By bridging the gap between these two traditional data storage paradigms, Lakehouses enable more efficient data analytics, machine learning, and business intelligence operations across diverse data types and use cases.
The choice between Data Warehouse, Data Lake, or Lakehouse systems is pivotal for businesses in harnessing the power of their data. Each option offers distinct advantages and challenges, requiring careful consideration of organisational needs and goals. By embracing the right data management system, organisations can pave the way for informed decision-making, operational efficiency, and innovation in the digital age.
