Volcano Engine Upgrades Data Flywheel, Accelerating Enterprise Data-Driven Transformation with AI
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On September 19, at the 'V-Tech Data-Driven Technology Summit,' Volcano Engine announced the launch of an 'AI Assistant' for its VeDI data intelligence platform. By integrating large AI models, the platform helps enterprises improve the efficiency of data processing and query analysis. Even non-technical operations staff can perform tasks like data extraction, visualization, and attribution analysis through conversational interactions with the AI model. Currently, VeDI's related data products have entered an invitation-only testing phase.
Tan Dai, President of Volcano Engine, believes that the data flywheel will be a critical direction for the application of large AI models in the enterprise market. He stated that large models lower the barrier for enterprises to extract data value, enabling more efficient construction of a data flywheel centered on data consumption. 'An AI-powered data flywheel will become a new paradigm for enterprises to achieve data-driven transformation,' he added.
AI Applications Across the Data Lifecycle
According to reports, Volcano Engine's VeDI has equipped its big data R&D and governance suite DataLeap and intelligent data insights platform DataWind with AI Assistants, covering end-to-end scenarios in data production and consumption, including data asset queries, development, operations, and analytical insights.
Data asset queries and development form the foundation of data consumption. Traditionally, these tasks heavily relied on professionals, but large models have significantly lowered the barrier. Non-technical employees can now use DataLeap's 'Data Search Assistant' to efficiently and accurately locate data through conversational queries, enabling self-service data consumption. With DataLeap's 'Development Assistant,' users can generate and optimize SQL code using natural language, as well as consult on SQL-related issues conversationally, breaking down technical barriers and simplifying data development.
For data analysis, the DataWind 'Analytics Assistant' allows employees to conduct data visualization, queries, and business exploration through conversational interactions with the AI model, addressing the pain point of requiring extensive expertise for insights and shortening the data analysis cycle. Additionally, DataWind integrates with collaboration tools like Feishu, enabling users to perform extended analyses via IM message subscriptions and conversations, facilitating flexible, on-the-go analysis.
Tan Dai noted, 'VeDI's two products not only lower the barrier to data consumption for non-technical users but also free up professionals to focus on complex scenarios, improving R&D efficiency and code quality.'
Transforming the Way Data Value is Explored
The application of large models in data products has already shown preliminary success in ByteDance's internal operations. Luo Xuan, Head of ByteDance's Data Platform, presented a case study of DataLeap and DataWind's collaborative analysis in an e-commerce scenario.
In the past, e-commerce operators had to rely on developers to extract data from DataLeap. Now, operators without coding skills can simply ask questions like, "Which tables should I use to analyze the performance of the 'Goods Live Room' over the past seven days?" DataLeap leverages its business knowledge base to recommend relevant tables and explain the data dimensions each table covers.
Operators can have DataLeap automatically generate code for specific data needs, such as city-based order sales or hourly live-streaming traffic. They can also inquire about the meaning of the generated code, parse it with one click, and use SQL tools to inspect tables. With AI-powered auto-fixes, they can further optimize data assets.
Once data assets are ready, operators can use DataWind's drag-and-drop interface to create datasets and define field logic using natural language—for example, querying data for "expert live-streaming hours." After retrieving the data, they can perform visual analysis and exploration.
DataWind also offers AI-driven automated analysis to uncover insights behind charts. For instance, in visualizations like "Top Sales Categories in Live Rooms," operators can use AI-generated insights to conduct root-cause analysis through conversational interfaces, enabling deeper business understanding.
"Currently, 80% of ByteDance employees can directly use data products, with managed data assets covering 80% of daily analysis scenarios," said Luo Xuan. "With large model capabilities, data consumption has become more accessible, making it easier for businesses to adopt data-driven strategies."
Helping Businesses Implement Data-Driven Strategies
In April, Volcano Engine, drawing from ByteDance's data-driven practices, introduced the "Data Flywheel" model for enterprise digital transformation, with data consumption as its core focus. The Volcano Engine Data Intelligence Platform leverages cutting-edge AI to further lower the barrier to data consumption.
As a new production factor, data is driving enterprise digital transformation. However, many companies struggle to unlock its full value due to high costs in data management, steep learning curves for data tools, and low data asset utilization—stemming from a lack of synergy between business and data.
The Data Flywheel addresses this by enabling data-driven insights, efficient decision-making, and agile actions to enhance business value. Frequent data consumption and business gains also guide the development of high-quality, cost-effective data assets, creating a virtuous cycle where data and business improvements reinforce each other.
This data-centric approach has delivered tangible benefits for Volcano Engine's partners.
Tan Dai noted, "Building a Data Flywheel centered on data consumption is an inevitable trend in enterprise digital transformation. Volcano Engine VeDI will continue to evolve, using AI and the Data Flywheel to further reduce barriers to data consumption, unlocking greater value and helping businesses harness data for growth."