This article introduces Douyin Group’s comprehensive data asset management platform, explains why it emphasizes data assets over raw metadata, outlines its full‑linkage lineage capabilities, and presents practical insights on building, applying, and future‑proofing big data lineage within complex enterprise environments.
This article introduces Douyin Group’s one‑stop Data Asset Management Platform, explains why the company focuses on data assets rather than raw metadata, and details the evolution, architecture, applications, and future outlook of its comprehensive big‑data lineage system.
This report outlines a comprehensive framework for turning raw data into valuable assets, introducing DataOps and panoramic data architecture, and detailing practical methods for data value assessment, asset circulation, and operational mechanisms to help enterprises build a solid value baseline and expand data asset applications.
This article presents a detailed overview of Douyin Group's Data Asset Management Platform, focusing on the evolution, architecture, modeling, metrics, and application scenarios of its large‑scale data lineage system, and outlines future directions for full‑coverage, fine‑grained lineage capabilities.
This article explores how enterprises can systematically identify, inventory, and govern massive data assets by defining key concepts, adopting frameworks like DAMA and DCMM, building layered management structures, and implementing integrated platforms for data integration, metadata, master data, standards, quality, and security to unlock data-driven value.
This article explains how the Volcano Engine DataLeap team leveraged large‑language models to build the “Find Data Assistant”, detailing its design, challenges, embedding‑and‑reranker enhancements, LLM‑driven semantic search, mixing architecture, and practical lessons for improving data asset management and retrieval.
This article details how the DataLeap team applied large language model technology to build the "Find Data Assistant" platform, addressing the challenges of locating and using massive data assets through a hybrid retrieval architecture, enhanced embedding, reranking, mixed ranking, and answer summarization, while sharing practical lessons and future directions.
This article examines JD Retail's comprehensive upgrade of data asset management, detailing innovations in data governance, multi‑level acceleration engines, intelligent materialization, unified DSL, visualization tools, low‑code orchestration, and large‑model AI applications that boost efficiency, reliability, and business decision‑making.
This article presents a detailed roadmap for enterprise data governance, covering business digitization goals, data governance construction, typical digital platform architecture, core governance actions, implementation pathways, data asset inventory techniques, and real‑world case studies to illustrate practical execution.
This article provides an extensive overview of data governance in the big‑data era, covering common pitfalls, the role of metadata, data quality management, data standardization, and data asset management, and offers practical recommendations for organizations to implement effective governance practices.
From June 4‑5, 2019, the China‑hosted Big Data Industry Summit gathered more than 4,000 attendees and 60,000 online viewers to present award winners, release multiple whitepapers and standards, and hold six thematic forums and two roundtables that examined data platforms, asset management, security, law, and emerging technologies, outlining current opportunities and future challenges for big‑data growth.
The article presents the AIGOV five‑star data asset management model, analyzes its five management domains and thirteen capability items, compares it with domestic and international frameworks, and illustrates its practical value through detailed enterprise case studies and references to maturity models.
