The article argues that as AI models become commoditized, the decisive factor for enterprises is mastering data governance, data‑AI integration, and data flow, turning data into a strategic asset that creates a three‑layer moat and drives sustainable AI ROI.
The article analyzes how the shift to marketing digitalization forces traditional IT departments to move from peripheral support roles to strategic growth engines, detailing the three core challenges they face, the value‑redefinition framework, and three concrete pathways—including infrastructure, low‑code enablement, and data‑driven decision making—illustrated with real‑world case studies such as Dongpeng Energy Drink and Luzhou Laojiao.
The article analyzes a common promotional prize‑claim workflow that relies on manual verification, exposing three critical flaws—delayed gratification, high labor costs, and data loss—while proposing a one‑code digital solution that automates verification, reduces costs, and turns each scan into valuable customer data.
Product managers fearing AI should stop chasing new models and instead focus on how to embed homogeneous AI into unique user scenarios, leveraging proprietary data, agent capabilities, and friction‑free interactions to create real competitive moats.
This article introduces Douyin Group’s Data Asset Management Platform, explaining its shift from traditional metadata to a comprehensive data‑asset approach, detailing the platform’s capabilities, and focusing on the evolution and application of full‑link data lineage across four key topics to improve visibility, quality, security, and cost efficiency.
This report analyzes how treating data as a production factor reshapes manufacturing, outlines three major challenges—scenario explosion, business‑application enrichment, and intelligent‑application expansion—and shares concrete governance, platform, and AI‑model practices that enable agile, data‑driven digital transformation.
This guide explores six critical areas—data assets, indicator systems, tag frameworks, data architecture, data quality, and project management—detailing how they interrelate and support digital transformation, while offering practical insights and best‑practice recommendations for building a robust data‑driven enterprise.
This article examines data as a key production factor, analyzes the data‑element ecosystem, explains data‑trust concepts and solutions, reviews relevant policies and market structures, and presents domestic and international practices and case studies illustrating how data trusts can facilitate secure, efficient data circulation and fair benefit distribution.
This article examines the concept, development trajectory, property rights, and monetization pathways of data assets, outlines a comprehensive data asset management framework, and proposes practical implementation plans to help enterprises unlock and capitalize on their data resources.
This article explains what data assets are, how to categorize them by structure and source, outlines a six‑step inventory process, describes a hierarchical catalog architecture, and highlights the four key benefits of a unified data asset directory for modern enterprises.
Enterprises face significant hurdles in data governance and integration, from siloed systems and unclear responsibilities to poor data quality, but by establishing clear rules, fostering user department engagement, and aligning governance with business-driven data applications, they can create a cohesive data asset management framework that drives value.
This article outlines DeepEx Technology's comprehensive data governance methodology, covering construction paths, digital platform frameworks, core governance components, implementation steps, case studies, and a Q&A that together illustrate how enterprises can build reliable data assets, models, standards, and quality processes to unlock business value.
This article explains what data governance is, why enterprises need it, the key components such as data quality, metadata, master data, asset and security management, and provides a step‑by‑step framework, organizational structure, platform features, evaluation methods and common pitfalls.
This article explains what tags are, how they function as data assets, defines the concept and architecture of a data middle platform, and demonstrates why tags are the pivotal element that enables enterprises to turn raw data into valuable, reusable business services.
The article outlines a practical five-step methodology for constructing a data middle platform, covering data resource inventory, application planning, asset construction, detailed design and implementation, and organizational planning to guide enterprises toward effective data-driven transformation.
