How AI Is Redefining Data Products: New Paths for Enterprise Intelligence

AI Development Enters a New Stage, Data Becomes a Key Variable

At the Volcano Engine FORCE conference, the data intelligence team highlighted two clear trends: enterprises are moving from focusing on raw compute power to improving training data quality, and AI applications are shifting from lightweight tasks like information retrieval to deep‑water scenarios involving enterprise data assets, business operations, and knowledge management.

From the DBA Era to the AI Data Era, the Data‑Product Question Is Rewritten

The fundamental question that data products have always answered is “where does the data come from and where does it go?”. In the DBA era, data originated from transaction systems, and the goal was stable storage and fast query using relational databases. In the big‑data era, data came from user behavior, scaling from GB to PB/EB, and data warehouses and distributed computing became the norm to serve products, operations, and analysts.

In the AI data era, data sources expand to multimodal formats—images, audio, video, text, and Agent execution traces. The consumers now include not only humans but also Agents and large models. This creates new requirements: governance of massive multimodal heterogeneous data, efficient storage of large training datasets, real‑time data flow for Agents, and enabling models to understand enterprise data contexts. Traditional databases and big‑data platforms alone cannot meet these needs.

AI‑Era Data Solution: From Data Foundations to Agent Closed‑Loop

Volcano Engine’s new AI data solution is organized into four layers:

Multimodal Data Lake : a dataset foundation for AI‑native scenarios, supporting preprocessing for large‑model training and downstream use cases such as autonomous driving and embodied intelligence; an additional Agent‑focused data foundation enables Agent data consumption, storage, and operation.

Professional Database : built on high‑quality datasets covering finance, consumer, autonomous driving, education, healthcare, etc., offering out‑of‑the‑box capabilities that lower the barrier from “having a model” to “having usable data”.

Enterprise Data Asset Management Platform : provides semantic construction and knowledge management for multimodal data, allowing Agents and large models to better comprehend enterprise data contexts and unlock data‑asset value.

Data Agent Upgrade : a unified Agent entry point that spans data analysis, marketing, development, and governance, while supporting PDF parsing, document and PPT generation, and information retrieval. It opens the Agent ecosystem with MCP and CLI interaction capabilities, and adds observation and evaluation features to support continuous Agent optimization.

The Agent‑generated data flows back into the multimodal data lake, forming a closed loop: governed enterprise data is consumed by models and Agents, the resulting new data is fed back for further training and optimization, turning AI applications from one‑off calls into continuously evolving data systems.

Conclusion

Data is the starting point for building AI competitiveness. As AI penetrates deeper into enterprise data domains, the real challenge begins with constructing robust, multimodal, AI‑native data architectures that support both model training and Agent operation.