Data Innovation Summit 2025
Data Innovation Summit 2025, organised by Hyperight, will take place on 7-8 May online.
The Data Innovation Summit is the largest and most influential annual Data and AI event in the Nordics and beyond, 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 IoT, in one place to discuss ways to accelerate AI-driven Transformation throughout companies, industries and public organisations.
With over 300 Nordic and international speakers in this 9th edition, spread across nine stages, seven workshop rooms, 200+ TIP sessions, and plenty of learning and networking activities in the exhibition area, the 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 1000 practical case studies presented in the past 9 editions and with new geo events in the 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.
Event stages:
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.
Data Engineering & DataOps Stage
This stage focuses on designing scalable and resilient data pipelines crucial for enterprise AI and ML. Key topics include building high-availability systems, scalable MLOps pipelines, and optimizing data pipelines for foundation models. Focus on data curation, distributed computing, serverless architectures, and advanced techniques for metadata management and data quality in LLM training.
Modern Data Platform Stage
This stage delves into building and optimizing modern data platforms, covering essential components and best practices such as data ingestion, cloud ETL, cross-platform integration, and data storage solutions like lakes, warehouses, and lakehouses. Focus on data governance, real-time processing, metadata management, and integrating AI/ML, ensuring scalable, secure, and efficient data platforms.
Modern Data Strategy Stage
This stage explores advanced strategies for managing decentralized data and designing scalable, flexible architectures. Key topics include federated data management, data lakehouse design, data governance, cross-organizational collaboration, data product development, and the strategic use of data fabrics, data mesh, and marketplaces, ensuring resilient and innovative data ecosystems.
Analytics and Data Science 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, edge analytics, ethical AI, visualisation and embedding analytics in business processes to drive decisions and enhance user experience.
AI Value and 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.
Developer Stage
This new technical stage examines the synergy between AI and DevOps, focusing on translating business needs into technical specs, fast data flow, and feedback loops. Key
topics include scalable AI design, securing AI APIs, cloud development, automation, event-driven architectures, Infrastructure as Code (IaC), and AI-enhanced developer tools and languages.
Applied Innovation & Responsible AI Stage
This stage highlights AI innovation through industry and public sector case studies, alongside critical discussions on the EU AI Act. Topics include navigating regulatory boundaries, transforming regulations into innovation drivers, and responsible AI governance. Sessions also
cover securing investment, fostering responsible AI, and exploring AI’s role in environmental sustainability.
Databases & Data Quality Stage
This stage explores the role of various databases in AI and analytics, focusing on graph, vector, time series, and columnar databases. Key topics include implementing knowledge graphs, optimizing multi-model databases, and the impact of autonomous databases. It also addresses data quality strategies for distributed systems, real-time monitoring, and AI-powered automation.
