By 2027, 45% of Asia/Pacific firms will adopt high-performance infrastructure for AI; by 2028, 60% of CDAOs will rival CIOs in tech decisions. — IDC Predicts

According to the IDC FutureScape: Worldwide Data and Analytics 2025 Predictions — Asia/Pacific Implications, by 2028, 60% of Chief Data and Analytics Officers (CDAO) in Asia-based top 2000 companies will rival the CIO in terms of influence on enterprise spending in technology. This signals the growing role of CDAOs in shaping technology investments across the Asia/Pacific region and a shift in organizational priorities.

Traditionally, CIOs led technology decisions, focusing on IT systems, security, and innovation. However, as data has become a strategic business asset, CDAOs are increasingly responsible for data governance, compliance, and analytics strategies that support business goals. While CIOs continue to manage technology infrastructure, CDAOs ensure data initiatives create value and comply with regulations. This shift is especially pronounced in the Asia/Pacific emerging markets, where the rise of GenAI is revolutionizing how businesses collect, analyze, and use data to drive innovation and operational efficiency. As a result, CDAOs are taking on a central role in shaping technology strategies, complementing the CIO's focus on IT systems while maximizing data's value to drive business success.

"As enterprises embark on their Agentic AI journey, they must first establish a strong Enterprise Intelligence architecture—one that forms the foundation not only for Agentic AI but for AI in all its forms. Achieving this requires the adoption of unified data platforms that seamlessly integrate best-in-class capabilities in data intelligence, data quality, and multimodal data management,” says Deepika Giri, head of research, Big Data & AI, IDC Asia/Pacific including Japan (APJ) Research. “Amid this shift, CDAOs are emerging as key decision-makers. With data-driven strategies becoming the backbone of AI transformation, enterprises must empower CDAOs to lead the charge in building scalable, intelligent, and future-ready AI ecosystems," ends Giri.

This study presents the top 10 predictions for Data and Analytics initiatives through 2030. Each prediction is assessed based on its impact (a mix of cost and complexity to address) and time frame to the expected stated adoption level. This study also highlights IT impact and guidance for technology buyers for each prediction statement. The following are some of the predictions representing the expected trends with potential impact on Data and Analytics initiatives:

Data and Model Governance: By 2026 only 25% of A2000 organizations will have aligned data intelligence with AI model intelligence to unify governance policies, practices, and technologies in the synthesis of data used with AI models.

Data Platforms: By 2027, over 50% of organizations in APeJ will be ready to use their data with GenAI due to the increased adoption of data platforms for data storage, standardization, and access.

Data Collaboration: By 2028, data collaboration via data exchanges and/or data clean rooms will have penetrated 70% of A2000 organizations.

Synthetic Data: By 2028 GenAI's creation of synthetic data will improve the accuracy and reliability of predictive analytics by 40% in areas with limited data.

Data Security and Privacy: By the end of 2025, data security teams in A2000 companies will provide 40% of the information collected from their tools to other lines of business to provide unified governance across the business.

45% of Asia/Pacific Firms to Embrace High-Performance Infrastructure for AI by 2027

IDC predicts that, by 2027, 45% of A2000 organizations will adopt performance-intensive, software-driven, scale-out storage infrastructure and unified data management to accelerate insights for AI and analytics, according to the IDC FutureScape: Worldwide Generative Artificial Intelligence 2025 Predictions — APEJ Implications. This shift is driven by the need to manage vast and expanding datasets, ensure compliance with data sovereignty regulations, and overcome challenges like insufficient storage capacity and computational power for AI workloads.

Deepika Giri, head of research, Big Data & AI, IDC Asia/Pacific

Traditional storage systems struggle to keep pace with the scale and speed required for AI applications, prompting businesses to invest in scalable, flexible infrastructure that can handle unstructured data more efficiently. These software-defined and scale-out storage solutions offer the performance and scalability needed to process and analyze large volumes of data, enabling faster insights. Unified data management simplifies the integration and governance of diverse data sources, helping organizations unlock valuable insights more efficiently. This strategic focus on cloud modernization, sustainability, and distributed data strategies reflects a broader effort to align digital infrastructure with innovation goals, operational efficiencies, and regulatory requirements, allowing enterprises to fully leverage AI's transformative potential and stay competitive in an increasingly data-driven world.

"Agentic AI is bridging critical gaps in large-scale GenAI implementation, driving more tangible ROI. However, its full potential can only be realized if enterprises prioritize the development of event-driven architectures supported by a robust data foundation layer," says Deepika Giri, head of research, Big Data & AI at IDC Asia/Pacific (including Japan).

This study presents the top 10 predictions for GenAI initiatives through 2030. Each prediction is assessed based on its impact (a mix of cost and complexity to address) and time frame to the expected stated adoption level. This study also highlights IT impact and guidance for technology buyers for each prediction statement. The following are some of IDC’s predictions representing the expected trends with potential impact on GenAI initiatives:

Unifying AI Governance: By 2026, 80% of APEJ organizations will be formalizing policies and oversight to address AI risks (e.g., ethical, brand, PII), aligning AI governance with strategic business goals.

GenAI POC to Production: In 2026, over one-third of APEJ organizations will be stuck in the experimental, point-solution phase of AI experimentation, requiring a shift of focus to enterprise use cases to deliver ROI.

GenAI for Cloud Security: By 2026, 40% of multicloud environments will leverage generative AI to streamline security and identity access management, reducing manual efforts by 50% across APEJ organizations.

Agentic Workflows: By 2026, 20% of frustrated knowledge workers with no development experience will take charge of transforming how they work by building their own agentic workflows, improving cycle times by 50%.

To learn more, visit: www.idc.com