DACon 2026上海站启动 驶向AI深水区 构建下一代「Data+AI」基座

DACon 2026 focuses on building the next-generation Data+AI infrastructure to bridge the final mile from data to intelligence.

1. Handling unstructured data: Converting documents, images, audio, and video into AI-ready assets through vectorization to support RAG and knowledge management applications.

2. Ontology engineering practices: Drawing from Palantir’s methodology to build an enterprise semantic layer, compiling business logic into computable code to enhance AI reasoning accuracy.

3. AI-driven data governance: Automating metadata, data quality, and lineage management to reduce manual dependency.

4. Lowering data usage barriers: Integrating development and analysis through NL2SQL and intelligent insights to improve data value extraction.

5. Cost optimization strategies: Discussing token throttling, latency optimization, and balancing cost vs. performance in private model deployment.

6. Technological evolution: Advancing from retrieval-augmented generation to autonomous decision-making with Agentic RAG, and shifting from keyword-based to conversational AI search with cutting-edge case studies.

The conference explores how AI is reshaping consumption and marketing, offering insights for brand building and product development.

1. Consumer trend insights: The Digital Intelligence New Consumption Forum examines AI and VR/AR applications in physical retail, analyzing shifts in user behavior and interaction patterns.

2. Marketing innovation: Generative AI case studies demonstrate how to redesign user interactions and marketing strategies to enhance brand experience.

3. Product R&D inspiration: The LLM+R&D Efficiency Forum covers AI code generation and intelligent testing, empowering full-lifecycle development and optimizing product design.

4. User behavior digitization: Unstructured data processing techniques transform user-generated content (e.g., images, videos) into high-quality assets for precise user observation.

5. Channel development opportunities: AI-driven data platform cases provide digital channel insights, such as deploying AI agents in marketing.

The conference reveals AI-driven market opportunities and risk mitigation strategies to support business growth.

1. Growth opportunities: Agentic RAG advancements enable new business models, such as autonomous decision systems, tapping into blue-ocean markets amid changing consumer demand.

2. Risk management: The Agent Application Security Forum discusses behavior monitoring and hallucination detection algorithms to mitigate output risks; cost optimization strategies address rising token expenses.

3. Collaboration channels: Enterprise AI platform case studies showcase partnership models, with on-site closed-door meetings and roundtables offering networking avenues.

4. Operational efficiency: Integrated data development and analysis tools like NL2Pipeline lower usage barriers and boost operational effectiveness.

5. Issue response: The AI for Data Governance Forum shares intelligent data lineage analysis to tackle data quality challenges.

The conference offers insights into AI-enabled production and digital transformation, uncovering commercial opportunities.

1. Production design needs: The Unstructured Data Engineering Forum focuses on collecting and parsing documents and images, illustrating how to AI-process production data for optimized design workflows.

2. Business opportunity exploration: Agentic AI development frameworks, including memory system design, provide new directions for product intelligence.

3. Digital transformation guidance: Ontology engineering builds enterprise semantic layers to make data “understand” business logic, improving production data management.

4. E-commerce insights: The Digital Intelligence New Consumption Forum analyzes AIoT-driven physical operations, offering e-commerce transformation ideas.

5. Cost optimization: Strategies like reducing API calls lower the cost of production digitization.

The conference highlights Data+AI industry trends and solutions to address client pain points.

1. Industry trends: AI is reshaping the entire data workflow, shifting competition from models to high-value data assets, offering a panoramic view.

2. New technology applications: Agentic RAG, context engineering, and NL2SQL lower data usage barriers and enhance system performance.

3. Solving client pain points: The Intelligent Data Governance Upgrade Forum addresses inefficiencies in traditional governance with smart metadata management and quality inspection solutions.

4. Practical solutions: Agent-Ready data architecture designs unified access and semantic layers to resolve lineage and permission challenges; security forums provide harmful output filtering mechanisms.

5. Cutting-edge topics: Large model cost optimization and AI search with retrieval fusion deliver deployable tools for enterprises.

The conference discusses how platforms can adapt to the Agent era, optimizing operations and merchant acquisition.

1. Platform challenges: Agent-Ready data architecture addresses shortcomings in traditional setups, such as data lineage tracking and permission management.

2. Latest approaches: Unified data access layers, semantic layer development, and the evolution from Lakehouse to Agent-Ready offer platform upgrade pathways.

3. Merchant acquisition opportunities: Lightning talks and workshops provide collaboration channels; enterprise AI platform cases inform partnership strategies.

4. Operational optimization: Integrated AI-driven data development and analysis, like conversational exploration, enhance platform user experience and efficiency.

5. Risk avoidance: The Agent Application Security Forum covers behavior monitoring and content moderation to help platforms mitigate compliance risks.

The conference unveils new trends and research questions in Data+AI, offering policy implications.

1. Industry shifts: AI Agents lead technology trends, with competition pivoting to high-value data assets and redefining productivity paradigms.

2. Emerging issues: Agent application safety involves hallucination detection and output security; context engineering evolves from prompt optimization into a discipline managing complexity.

3. Policy implications: The Digital Intelligence Finance Forum explores AI in risk prediction, offering solutions for heavily regulated industries; policy suggestions are embedded in discussions on safety and controllability.

4. Business model analysis: Agentic RAG evolution and conversational AI search case studies illustrate new commercial models.

5. Frontier research: Ontology engineering builds “data flywheels,” with enterprise semantic layer practices providing academic reference points.