From Data to Value in the age of Applied and Generative AI – Hybrid event edition bringing all the Data, Analytics and AI stakeholders in one place to showcase and discuss ways to maximize value from data in the age of Applied and Generative AI’
Welcome! The Data Innovation Summit is the largest and the most influential annual Data and AI event in the Nordics and beyond, bringing together the most innovative minds, enterprise practitioners, technology providers, startup 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.’
The Stages’
P1. 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.’
M1. Machine Learning & MLOps Stage – Technical presentations on deploying, running, and scaling both applied and generative AI models in an Enterprise. Focus on Model Ops, foundational models,AI/ML governance and AI life-cycle management. These sessions will also explore MLOps techniques to enhance delivery time, minimize defects, and boost the productivity of machine learning initiatives.’
M2. Engineering & DataOps Stage – Technical stage focusing on the tech stack, software infrastructure and ways to architect and implement on-premise, hybrid or cloud-native data pipelines and infrastructure to enable analytics and machine learning on rich and quality datasets. Focus on Data-Ops, ML Ops, Auto ML, Cloud ML, Fast Data, fine-tuning and much more.’
M3. Modern Data Platform Stage – Strategy and technical stage focusing on developing a disruptive modern data platform that supports shifting business models, adapts to changing workforce operations and dynamically and intelligently connects the enterprise. Focus on the building blocks of a Modern Data Platform, including sources, ingestion, storage, query and processing, transformation, analyses and output.’
M4. Modern Data Strategy Stage – A strategy and technical track focusing on ways to create a future-proof and agile Modern Data Strategy for enterprise grade application of Advanced Analytics and AI, from ownership to implementation. This year’s focus is on the Data Fabric, Data & Information Governance, Big Data Quality, Data Mesh, Data Foundation, Master Data, Warehousing, Data Lake and much more.’
M5. Business & Data Analytics Stage – Strategy and technical presentations on how companies can enhance their analytics capabilities, maintaining a balance between control and agility across their adaptable enterprise platforms suited for various user personas, analytics capabilities, and data use cases. The focus will be on governance, decision intelligence, data preparation, embedded analytics, automated insights, augmentation, data visualization, and Natural Language Querying (NLQ).’
M6. Data Science & AI Strategy Stage – At the Strategy and Business stage, we focus on methods to establish a world-class analytics and AI delivery platform. Learn how to lead and manage high-performing Data Science and Advanced Analytics teams, oversee the life-cycle of analytics and AI products, and enhance the provision of insights and business value to internal and external stakeholders.’
M7. Industrial Analytics & AI Stage – Strategy and technical stage focusing on how heavy asset organisations and manufacturers can develop and use machine data, analytics and application-oriented AI applications to improve operational processes, detect and classify anomalies, thereby effectively reducing downtimes and optimize production and everyday operations.’
M8. Applied Innovation & Responsible AI Stage – The Business and Strategy stage focuses on application-centric AI innovation within both private and public organizations. This includes enhancing customer experiences, improving business processes, reinventing existing business models, and creating new ones. Additionally, the stage emphasizes human-centric AI innovation, encompassing topics such as explainable AI, trustworthy AI, digital ethics, AI for Good, and much more.’
M9. Databases & Data Quality Stage – On this stage, we are showcasing expert-led technical presentations that delve into innovative strategies for managing enterprise databases, ensuring data remains accurate, consistent, and reliable at all times. These sessions will also highlight the pivotal role of effective data quality management in paving the way for successful analytics and AI applications.
