The article explains why data interoperability—beyond simple table linking—is essential for enterprise data governance, outlines its four-layer architecture, compares implementation approaches, and shows how proper data flow unlocks analytics, operations, supply‑chain coordination, and AI readiness.
In the AI era, data consumption moves from a few technical users to all business staff, forcing a fundamental redesign of data governance across five dimensions—resource consumption, frequency, semantics, knowledge base, and modality—and proposing three actionable strategies to make data semantically rich, fully multimodal, and AI‑consumable.
A live discussion with experts from Xiaohongshu and Guanyuan Data reveals that the real challenge of AI‑driven data agents lies not in model strength but in semantic convergence, knowledge structuring, explainability, cost control, and gaining business trust for production‑grade deployment.
The article explains that a data warehouse is an analysis‑oriented data management system that integrates, cleans, and stores enterprise data to provide a single, trustworthy source for reporting and decision‑making, outlines common data problems, and presents a step‑by‑step methodology—including goal definition, source integration, layering, governance, and BI connection—to successfully build and maintain one.
The article examines why single‑agent ChatBI solutions fall short in enterprise settings and presents a three‑layer, multi‑agent architecture—interaction, orchestration, and execution—built with Amazon Quick Suite, Amazon Bedrock AgentCore, and Snowflake Cortex AI, detailing routing, synchronous/asynchronous processing, semantic modeling, and deployment recommendations.
The article proposes a “digital portrait” framework for data governance, outlining concrete metrics to evaluate user experience across external customers, internal users, management, and technical staff, as well as architecture quality indicators such as model reuse, data distribution, standard stability, and asset coverage.
The article outlines five key ways AI large models can revamp enterprise data warehouses—automated data governance and cleaning, natural‑language query interfaces, real‑time predictive analytics, multimodal data integration with knowledge graphs, and security‑compliant automated operations—while also discussing supporting technologies, toolchains, and future trends toward industry‑specific models.
This article examines how artificial‑intelligence techniques such as natural‑language processing, knowledge graphs, federated learning and automated ETL are applied across six core data‑governance scenarios—standardization, asset management, master data, data‑warehouse automation, security/privacy, and real‑time quality monitoring—showing measurable efficiency gains and business impact.
This article explains how large‑model capabilities combined with metric and tag platforms can be used to build intelligent data‑analysis products for enterprises, covering challenges, solution routes such as NLP2SQL, NLP2API, NLP2Python, agent design, planning, and future outlooks.
This article analyses how the macro‑economic slowdown reshapes internet companies, outlines the challenges of a "slow" growth era, and proposes a four‑dimensional transformation of enterprise data systems—including top‑level strategy, organization, product matrix, and product‑manager skill upgrades—to build a digital growth engine.
This article shares the practical application of large language models within Zhihu’s internal Jianqiao analytics platform, covering business background, knowledge taxonomy organization, natural‑language‑to‑filter conversion, natural‑language data analysis, and summarizing challenges, solutions, and future outlooks.
This guide outlines a comprehensive five‑stage methodology for building an enterprise data middle platform—from high‑level planning and system design through development, trial operation, and continuous management—detailing business and technical planning, architecture, data modeling, integration, deployment, and operational best practices.
The article examines how enterprises can transform big data from idle Hadoop clusters into valuable assets by adopting data-driven processes and products, outlining the distinction between technology-driven and business-driven approaches, describing data and service product models, and highlighting process optimization across various business functions.
Big data has become a critical asset for companies, enabling them to understand users, precisely locate resources, enhance marketing and operations, deliver refined services, and anticipate crises, thereby turning raw information into strategic advantage across multiple business functions.
The article examines whether all enterprises should establish dedicated big‑data departments, weighing the hype, actual data needs, cost considerations, and expert viewpoints, and concludes that small firms are better off leveraging open‑source tools or outsourcing rather than building costly in‑house teams.
Enterprises can unlock Hadoop's full potential by following five strategic steps—from defining high‑impact use cases and assessing architectural fit to managing data, integrating systems, and addressing skill gaps—ensuring measurable business value and competitive advantage.
A recent Dresner survey reveals that while most enterprises claim big data is critical, only a minority actually deploy it, and many are already using big‑data techniques without labeling them as such, highlighting a gap between perception and practice.
