Welcome to Data Innovation Summit APAC

The Most Influential Data, AI and Advanced Analytics Event!

Welcome to the Data Innovation Summit APAC. The most influential annual Data, Analytics and AI event in the world, bringing together the most innovative minds, enterprise practitioners, technology providers, start-up innovators and academics, working with Applied Data Innovation, Data Science, Big Data, ML, Applied AI, Generative AI, Data Management, Data Engineering, Architecture, Databases and DevOps, in one place to discuss ways to accelerate AI-Driven Transformation throughout companies, industries and public organisations. With over 40+ APAC and international speakers in this 4th APAC edition, spread across four stages, two workshop rooms, 30+ TIP sessions, and plenty of learning and networking activities in the exhibition area, the APAC Data Innovation Summit is the place to be for all professionals and organisations working with utilisation of data and AI innovation for enhancing customer experience, improve operational processes, enable future sustainability, reinventing business models, or developing data-driven products and services.

With over 1500 practical case studies presented in the past 9 editions and with geo events in Europe, MEA, APAC, and the ANZ region, the event is a worldwide movement, ushering the community of data, analytics, and AI practitioners across functions, companies, industries, sectors, countries and regions to collaborate, benchmark, share and innovate.

KEY HAPPENINGS

Keynotes, Opening and Closing Stage

The keynote presentations are the pillars of the entire conference and the most memorable presentations of the event. As opening plenary talks, they set the tone of the entire conference, tie all themes together and provide excitement on the presentations that are about to come. This year’s keynote list includes visionaries and leading expert names in Data, Analytics and AI.

AI Value & Strategy Stage

This stage focuses on aligning generative AI with core business objectives, covering strategies to avoid common pitfalls and manage expectations. Key topics include building scalable AI infrastructure, measuring AI impact, balancing innovation with risk, and ensuring proper integration with existing systems. Focus on AI governance, cost management, accountability, and cross-functional collaboration for successful AI implementation.

Modern Data Strategy Stage

This stage focuses on modern data strategies and platforms, emphasizing the creation of scalable, secure, and efficient data ecosystems. Key topics include data ingestion, cloud ETL, and cross-platform integration, along with advanced storage solutions such as data lakes, warehouses, and lake-houses. We will explore best practices in data governance, real-time processing, and metadata management, with an emphasis on integrating AI/ML capabilities.

Business and Data Analytics Stage

This stage shifts focus from data-driven insights to decision-centric practices, covering emerging technologies in self-service analytics, real-time analytics, and AI-driven insights. Key topics include decision intelligence, natural language processing in analytics, edge analytics, visualization and embedding analytics in business processes to drive decisions and enhance user experience.

Machine Learning & Generative AI Stage

Technical stage on deploying, running, and scaling both applied and generative AI models in an Enterprise. Sessions cover customizing AI with proprietary data, data-centric vs. model-centric approaches, and MLOps for AI reproducibility. Key topics include transformers, AutoML, model drift, and ethical AI governance, ensuring robust and responsible AI deployment.