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data governance

767 articles · Page 1 of 8
Frontline Investigation
Frontline Investigation
Oct 3, 2026 · Information Security

Data Labels Show Sensitivity, Not Permissible Use: The Governance Gap in Sharing

The article explains that data classification and grading labels indicate protection requirements but do not define permissible usage conditions, which depend on specific purpose, scope, and time limits per regulations like China's Government Data Sharing Regulations; it warns that reusing existing interfaces for new purposes risks unauthorized expansion unless governance systems preserve the original authorization context.

GB/T 43697Government Data Sharing RegulationsNetwork Data Security Management Regulations
0 likes · 7 min read
Data Labels Show Sensitivity, Not Permissible Use: The Governance Gap in Sharing
Data Bricklaying Diary
Data Bricklaying Diary
Sep 30, 2026 · Artificial Intelligence

Ontology Intelligence Pilots: Readiness Depends on Specific Conditions, Not Company Size

The article argues that suitability for ontology intelligence pilots depends on specific readiness conditions—concrete tasks, accessible data, verifiable results, clear authority for rule confirmation, and maintenance plans—not company size, illustrating with a comparison of two enterprises and providing a six-item checklist for project preparation.

after-sales servicebusiness rulesdata governance
0 likes · 17 min read
Ontology Intelligence Pilots: Readiness Depends on Specific Conditions, Not Company Size
Digital Planet
Digital Planet
Sep 25, 2026 · Industry Insights

Enterprise Digital Transformation & AI Enablement: 4-Stage Industrial Implementation Guide

This guide outlines a four-stage roadmap for industrial enterprises to combine digital transformation with AI, emphasizing digital foundations as prerequisite for AI value, with concrete scenarios across R&D, manufacturing, supply chain, and management, plus pitfalls, differentiated strategies by company size, and a case study showing 28% inventory reduction and 24% downtime decrease.

AI EnablementAI agentsImplementation Roadmap
0 likes · 20 min read
Enterprise Digital Transformation & AI Enablement: 4-Stage Industrial Implementation Guide
Data Bricklaying Diary
Data Bricklaying Diary
Sep 24, 2026 · Artificial Intelligence

Why Your Knowledge Graph Fails to Explain Business: The Missing Ontology Layer

This article uses an order management example to explain why a knowledge graph alone cannot capture business meaning, defines ontology as explicit computer-processable descriptions of concepts and constraints, distinguishes ontology from knowledge graphs and graph databases, compares OWL and OPM modeling approaches, and provides six validation questions for ontology-driven data governance.

OPMOWLSHACL
0 likes · 21 min read
Why Your Knowledge Graph Fails to Explain Business: The Missing Ontology Layer
Frontline Investigation
Frontline Investigation
Sep 24, 2026 · Industry Insights

Why Temporary Data Copies Are the Real Governance Blind Spot

This article argues that uncontrolled data copies created for analysis, debugging, or sharing pose greater governance risks than the main database, as they lose context, responsibility, and retention rules, and proposes lightweight context-tracking and expiration mechanisms to manage copies without heavy approval processes.

PIPLcompliancedata context
0 likes · 10 min read
Why Temporary Data Copies Are the Real Governance Blind Spot
Data Bricklaying Diary
Data Bricklaying Diary
Sep 21, 2026 · R&D Management

Ontology Review Exposes Governance Conflicts: Customer & Revenue Disputes

The article explains how ontology reviews surface business governance conflicts over definitions like 'customer' and 'revenue,' and provides a framework to distinguish identity, roles, granularity, facts, and rules; assign confirmation authority by domain, cross-domain, and management levels; implement decisions via versioned decision cards; and validate through concrete test cases rather than forced unification.

business semanticscross-domain governancecustomer master data
0 likes · 22 min read
Ontology Review Exposes Governance Conflicts: Customer & Revenue Disputes
Smart Sea Tide
Smart Sea Tide
Sep 18, 2026 · Databases

6 Core Data Quality Metrics for Robust Data Governance

This article outlines six fundamental data quality metrics—accuracy, completeness, consistency, timeliness, uniqueness, and validity—detailing their focus areas and measurement approaches, and illustrates how a monitoring dashboard with radar charts and trend analysis can track a 92.1 composite score, 96.3% compliance rate, 8.7% daily issue reduction, and 268% ROI, emphasizing that sustained quality requires standards, organization, processes, technology, and continuous improvement.

Data QualityROIdata governance
0 likes · 3 min read
6 Core Data Quality Metrics for Robust Data Governance
Data Bricklaying Diary
Data Bricklaying Diary
Sep 17, 2026 · Artificial Intelligence

Why Enterprises Need Business Ontologies Despite LLMs' World Knowledge

This article explains why large language models' general world knowledge cannot replace enterprise business ontologies, which provide versioned, traceable semantic models for object identity, institutional definitions, state validity, and responsibility boundaries within a specific organization.

Enterprise Knowledge ManagementSemantic Modelingbusiness ontology
0 likes · 19 min read
Why Enterprises Need Business Ontologies Despite LLMs' World Knowledge
DataFunSummit
DataFunSummit
Sep 16, 2026 · Industry Insights

OpenAI's Data Agent: Why Semantic Layers Are Now Essential Infrastructure

OpenAI's Data Agent integrates with enterprise data stacks like Snowflake and BI tools rather than replacing them, revealing that AI agents require governed business context—metric definitions, semantic models, permissions—to deliver accurate analysis, making semantic layers critical infrastructure for AI-driven analytics.

AI AnalyticsBI ToolsBusiness Context
0 likes · 18 min read
OpenAI's Data Agent: Why Semantic Layers Are Now Essential Infrastructure
Frontline Investigation
Frontline Investigation
Sep 15, 2026 · Industry Insights

Why Shared Data Doesn't Lead to Collaboration: The Missing Context Problem

The article explains that cross-department data sharing often fails to enable true collaboration because data loses its original context—purpose, applicability, responsibility, and feedback loops—when transferred via interfaces. It proposes a 'collaboration object' framework with four layers (source, purpose, responsibility, feedback) to turn data into actionable shared assets, referencing China's national data infrastructure guidelines.

Judgment Boundariescollaboration objectcross-department collaboration
0 likes · 11 min read
Why Shared Data Doesn't Lead to Collaboration: The Missing Context Problem
dbaplus Community
dbaplus Community
Sep 14, 2026 · Industry Insights

Why 90% of ChatBI Projects Fail: Data Governance, Not AI, Is the Bottleneck

Despite 70% of BI products adding AI chat features, 90% of deployed ChatBI projects see under 30% adoption because semantic gaps, inconsistent metrics, and missing data governance make NL2SQL unreliable; true intelligent analysis requires unified definitions, semantic layers, and organizational adaptation, not just better models.

AI AdoptionBusiness IntelligenceChatBI
0 likes · 14 min read
Why 90% of ChatBI Projects Fail: Data Governance, Not AI, Is the Bottleneck
DataFunTalk
DataFunTalk
Sep 11, 2026 · Big Data

Ant Group's Financial Data Ontology: Automating Business Semantics with LangGraph

Ant Group solves inconsistent business definitions across thousands of tables by building a financial data knowledge ontology using a six-node LangGraph state machine that automates schema perception, entity resolution, and conflict marking without forced merging, enabling a three-layer retrieval architecture that cuts cross-opportunity identification from days to hours and achieves 85% anomaly analysis accuracy.

Automated Ontology ConstructionFinancial Data OntologyLangGraph
0 likes · 4 min read
Ant Group's Financial Data Ontology: Automating Business Semantics with LangGraph
Data Bricklaying Diary
Data Bricklaying Diary
Sep 10, 2026 · Backend Development

Terminology Normalization Beyond Similarity: Coverage, Routing, Drift & Regression Testing

This article explains why terminology normalization requires a continuous governance framework covering candidate recall, risk-based routing, manual review, drift attribution, and regression testing — not just similarity scores — and details metrics, routing rules, drift types, acceptance criteria, and a minimum viable loop for production systems.

candidate recalldata governancedrift detection
0 likes · 18 min read
Terminology Normalization Beyond Similarity: Coverage, Routing, Drift & Regression Testing
Smart Sea Tide
Smart Sea Tide
Sep 7, 2026 · Databases

Data Governance: 12 Brutal Truths Behind Common Excuses

This article dismantles twelve common data governance fallacies — from demanding instant metadata completion to treating governance as a cost center — arguing that effective governance requires confronting upstream data pollution, aligning incentives across departments, and prioritizing critical data paths over blanket coverage.

Data LakeData Qualitydata governance
0 likes · 10 min read
Data Governance: 12 Brutal Truths Behind Common Excuses
Frontline Investigation
Frontline Investigation
Sep 7, 2026 · Big Data

Why More Data Interfaces Make Quality Issues Harder to Trace

As data interfaces proliferate, quality issues shift from simple errors to semantic drift across systems; this article analyzes meaning, time, and responsibility drift, proposes a four-question lineage framework, and advocates lightweight change records to maintain trust in evolving data ecosystems.

Data QualityGB/T 34960change management
0 likes · 12 min read
Why More Data Interfaces Make Quality Issues Harder to Trace
Data Bricklaying Diary
Data Bricklaying Diary
Sep 6, 2026 · Fundamentals

Standard Ontologies Aren't Business Schemas: How to Map Changing Terms to Stable Models

The article argues that standard ontologies and business schemas serve distinct roles—ontologies provide standard concepts while schemas offer stable application contracts—and a normalization layer must connect raw terms to both using evidence, context, versioning, and review status, because similarity scores only produce candidates, not confirmed facts.

audit trailbusiness schemadata governance
0 likes · 16 min read
Standard Ontologies Aren't Business Schemas: How to Map Changing Terms to Stable Models
Architects Research Society
Architects Research Society
Sep 5, 2026 · Artificial Intelligence

Why Enterprise Knowledge Isn't Just Documents for LLMs: GNOSIVELA's Knowledge Fabric

The article argues that enterprise knowledge for AI agents requires more than vector retrieval; GNOSIVELA provides a knowledge fabric that unifies documents, data, semantics, rules, and provenance with governance, distinguishing source facts, normalized knowledge, and task-specific projections to ensure explainable, permissioned, and timely knowledge access.

AI agentsAccess ControlKnowledge Management
0 likes · 6 min read
Why Enterprise Knowledge Isn't Just Documents for LLMs: GNOSIVELA's Knowledge Fabric
Frontline Investigation
Frontline Investigation
Sep 1, 2026 · Industry Insights

Why Detailed Data Classification Fails Frontline Users: The Missing Actionable Guidance

This article explores why increasingly detailed data classification and strict approval rules paradoxically discourage frontline data usage, arguing that governance must provide scenario-based authorization, minimal necessary granularity, and auditable usage paths to turn static labels into actionable, explainable compliance routes.

GB/T 43697-2024compliancedata classification
0 likes · 11 min read
Why Detailed Data Classification Fails Frontline Users: The Missing Actionable Guidance
TechVision Expert Circle
TechVision Expert Circle
Sep 1, 2026 · Industry Insights

What Hospital Leaders Must Do First for a Full AI Rollout

The Cleveland Clinic’s AI‑driven Ambient Note saved each outpatient doctor 40 minutes a day, prompting hospitals to ask how to scale AI; this article outlines the manager’s checklist—from choosing a system over a tool, through data governance, architecture, security, organizational change, to a phased 12‑month rollout.

AI agentsRetrieval-Augmented Generationdata governance
0 likes · 12 min read
What Hospital Leaders Must Do First for a Full AI Rollout
DataFunSummit
DataFunSummit
Sep 1, 2026 · Industry Insights

How Ant Group Scaled Apache Ossie Semantic Layer from Zero to 5,000 Metrics

The article explains why large language models struggle with business data, introduces Ant Group's semantic‑layer approach built on Apache Ossie to impose strong business constraints, compares it with other retrieval methods, and details the engineering journey that delivered a unified source of truth for over 5,000 metrics while also announcing a related conference.

AI agentsAnt GroupApache Ossie
0 likes · 4 min read
How Ant Group Scaled Apache Ossie Semantic Layer from Zero to 5,000 Metrics
Data Integration and Governance
Data Integration and Governance
Aug 31, 2026 · Big Data

Data Agent Architecture: 7 Layers to Connect Enterprise Data Beyond Text-to-SQL

This article argues that enterprise Data Agents require a seven-layer data architecture—source, preparation, object, semantic, permission, execution, and verification—rather than simply connecting databases to LLMs, detailing how each layer resolves ambiguity, ensures stability, enforces security, and enables trustworthy analytical reasoning.

AI AnalyticsBusiness IntelligenceData Agent
0 likes · 18 min read
Data Agent Architecture: 7 Layers to Connect Enterprise Data Beyond Text-to-SQL
Data Integration and Governance
Data Integration and Governance
Aug 28, 2026 · Big Data

What Does Metric Management Actually Manage? The Full Lifecycle Explained

The article explains that metric management is not just cataloging metrics but governing their full lifecycle—business definitions, calculation rules, responsibility assignment, data lineage, and versioned change control—to ensure metrics are trustworthy, traceable, and sustainably governed across business, data, and technical layers.

FineDataLinkcalculation ruleschange management
0 likes · 19 min read
What Does Metric Management Actually Manage? The Full Lifecycle Explained
Smart Sea Tide
Smart Sea Tide
Aug 25, 2026 · Big Data

Data Governance & Metrics Library Planning: A Symbiotic Framework for Business Value

This article explains how data governance establishes reliable data standards and quality to support metrics library planning, while metrics libraries define business-driven KPIs that guide governance priorities, creating a virtuous cycle where governance ensures metric accuracy and metric outcomes measure governance effectiveness, ultimately maximizing data value.

Data QualityKPIbusiness value
0 likes · 6 min read
Data Governance & Metrics Library Planning: A Symbiotic Framework for Business Value
Digital Deification
Digital Deification
Aug 24, 2026 · R&D Management

Parachuted CIO Survival: Diagnose First, Then Break Through — Or Adjust Your Value/Position

A veteran IT director shares why most parachuted CIOs fail within 100 days and reveals a two-step framework — diagnose across four dimensions (boss, business, vendors, internal reality) before driving breakthroughs — plus a career strategy: either raise your professional value or lower position expectations to secure the role.

CIOIT leadershipcareer management
0 likes · 7 min read
Parachuted CIO Survival: Diagnose First, Then Break Through — Or Adjust Your Value/Position
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Aug 24, 2026 · Artificial Intelligence

How Paimon and Milvus Build an AI‑Native Multimodal Data Lake

The article analyzes the structural challenges of maintaining separate data lake and vector database systems for AI agents and multimodal workloads, and presents an open‑source integration of Apache Paimon and Milvus that unifies storage, governance, and high‑performance vector retrieval on a single data plane.

AI infrastructureAgentic AIApache Paimon
0 likes · 24 min read
How Paimon and Milvus Build an AI‑Native Multimodal Data Lake
Frontline Investigation
Frontline Investigation
Aug 24, 2026 · Industry Insights

Why Seamless Government Data Sharing Demands Rigorous Exit Strategies

As China's 2025 Regulations on Government Data Sharing take effect, the focus shifts from merely connecting data interfaces to managing the full lifecycle of sharing relationships—ensuring they can be paused, audited, and cleanly terminated when original purposes expire or conditions change.

Data Lifecycle Managementdata governancedata security law
0 likes · 10 min read
Why Seamless Government Data Sharing Demands Rigorous Exit Strategies
Digital Deification
Digital Deification
Aug 23, 2026 · Industry Insights

Governance Cannot Be Purchased: The Internal Capability That Determines Digital Transformation Success

The article argues that digital transformation fails when companies equate technology procurement with transformation, neglecting governance; governance capability cannot be bought but must be built internally through organizational responsibility, data rules, and business-technology alignment to retain assets, enable usage, and sustain iteration across technology cycles.

IT-business alignmentdata governancedigital transformation
0 likes · 11 min read
Governance Cannot Be Purchased: The Internal Capability That Determines Digital Transformation Success
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Aug 23, 2026 · Artificial Intelligence

Why AI Projects Look Great but Perform Poorly? A Practitioner’s Deep Retrospective

The article analyzes why AI projects that shine in proof‑of‑concepts often falter in production, highlighting four core challenges—probabilistic uncertainty, data quality, engineering complexity, and misleading accuracy metrics—and proposes four practical ways to break through these obstacles.

AI DeploymentMLOpsObservability
0 likes · 13 min read
Why AI Projects Look Great but Perform Poorly? A Practitioner’s Deep Retrospective
Data Bricklaying Diary
Data Bricklaying Diary
Aug 23, 2026 · Fundamentals

From Business Research to Ontology Modeling: 7-Step Framework for Verifiable Business Models

This article details a seven-step methodology to transform business research findings into verifiable business models ready for ontology engineering, using an order fulfillment risk case study to illustrate object identification, rule definition, evidence mapping, and validation through competency questions and boundary samples.

business modelingbusiness rulescase study
0 likes · 28 min read
From Business Research to Ontology Modeling: 7-Step Framework for Verifiable Business Models
TechVision Expert Circle
TechVision Expert Circle
Aug 22, 2026 · Artificial Intelligence

Who Owns the Data Behind Personal Digital Twins? Governance, Architecture, and Regulation

The article examines the emergence of personal digital twins, outlines their five‑layer technical architecture, analyzes three ownership dilemmas—including raw data vs. model rights, cross‑platform portability, and liability for autonomous actions—and reviews UK, EU, and Chinese regulatory proposals along with practical enterprise solutions such as data lineage, exportable state snapshots, and audit‑driven circuit‑breakers.

AI agentsAuditLoRA
0 likes · 12 min read
Who Owns the Data Behind Personal Digital Twins? Governance, Architecture, and Regulation
Data Bricklaying Diary
Data Bricklaying Diary
Aug 22, 2026 · R&D Management

From Vague Requirements to Verifiable Business Scenarios: A 7-Step Ontology Research Method

This article outlines a seven-step business research methodology for ontology modeling, transforming vague requirements into clear, verifiable business scenarios by distinguishing facts, rules, judgments, and hypotheses, evaluating candidate opportunities, defining value narratives and boundaries, and producing a scenario definition card as a modeling input.

Knowledge Engineeringbusiness researchdata governance
0 likes · 29 min read
From Vague Requirements to Verifiable Business Scenarios: A 7-Step Ontology Research Method
DataFunSummit
DataFunSummit
Aug 21, 2026 · Artificial Intelligence

Turning Search into Action: How Elasticsearch Agent Builder Makes Data Come Alive

The article analyzes how AI applications evolve from answering questions to executing tasks, outlines the data, context, and execution challenges, and explains how Elasticsearch Agent Builder integrates searchable data, tool capabilities, and governance into a verifiable execution chain, illustrated with a log‑analysis case study.

AI agentsElasticsearchRAG
0 likes · 12 min read
Turning Search into Action: How Elasticsearch Agent Builder Makes Data Come Alive
Data Integration and Governance
Data Integration and Governance
Aug 21, 2026 · Fundamentals

How to Manage Data Quality? A Complete Breakdown of the Six Dimensions

Enterprises often start data governance with standards, yet the real challenge is answering “Is this data accurate?” – a challenge solved by a continuous mechanism that discovers, locates, resolves, and verifies issues across six dimensions (completeness, consistency, accuracy, uniqueness, timeliness, validity) and follows a five‑step governance loop.

Data Qualityaccuracycompleteness
0 likes · 16 min read
How to Manage Data Quality? A Complete Breakdown of the Six Dimensions
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Aug 20, 2026 · Artificial Intelligence

How Ontology Drives AI Transformation: Lessons from Palantir’s Turnaround

The article explains why Ontology—structured business semantics, three‑layer models, and executable constraints—has become a mandatory foundation for enterprise AI, detailing its role in reducing LLM hallucinations, enabling safe AI agents, showcasing benchmark case studies, and providing a step‑by‑step roadmap for building, governing, and scaling Ontology in practice.

AIAI AgentEnterprise Knowledge Graph
0 likes · 31 min read
How Ontology Drives AI Transformation: Lessons from Palantir’s Turnaround
Digital Planet
Digital Planet
Aug 20, 2026 · Industry Insights

Why Does Company‑Wide Use of WorkBuddy Fail to Deliver Cost Savings and Efficiency Gains?

Enterprises that roll out the AI productivity tool WorkBuddy to all employees often cannot quantify any cost reduction or efficiency improvement because six management gaps—data governance, process integration, system integration, requirement translation, incentive alignment, and strategic focus—prevent the tool from being embedded into real business value streams.

AI AdoptionWorkBuddycost reduction
0 likes · 10 min read
Why Does Company‑Wide Use of WorkBuddy Fail to Deliver Cost Savings and Efficiency Gains?
Data Bricklaying Diary
Data Bricklaying Diary
Aug 20, 2026 · Artificial Intelligence

One Dataset Fits All? Why Training, Eval, RAG & Agent Data Must Be Separate

The article explains why AI systems need four distinct data products—training sets, evaluation sets, RAG knowledge bases, and Agent contexts—each with separate purpose, structure, timeliness, isolation, and acceptance criteria, warning that reusing a single dataset creates false quality metrics and operational risks.

AI data productsAgent ContextData-Centric AI
0 likes · 20 min read
One Dataset Fits All? Why Training, Eval, RAG & Agent Data Must Be Separate
Lakehouse Research Base
Lakehouse Research Base
Aug 19, 2026 · Big Data

How 1565PB of High-Quality Datasets Are Reshaping Lakehouse Architecture for AI

China's high-quality dataset count hit 120,000 totaling 1,565 PB with 60% quarterly growth, exposing four systemic gaps in traditional BI-oriented lakehouse architectures — storage, governance, compute performance, and security — and driving an AI-native reference architecture built on Paimon, StarRocks, tiered storage, operator-level lineage, and multi-modal retrieval.

AI training dataApache PaimonData Quality
0 likes · 43 min read
How 1565PB of High-Quality Datasets Are Reshaping Lakehouse Architecture for AI
Digital Planet
Digital Planet
Aug 18, 2026 · Industry Insights

Why Your One‑Code Marketing System Turns Into a Dusty Shelf After Six Months

The article explains why many one‑code marketing platforms become unused within months, tracing the failure to chaotic master data, missing unique IDs, data silos, broken business logic, and lack of operational ownership, and then offers a four‑step revival guide centered on data cleaning, ID‑based linking, a data‑steward role, and small‑scale pilots.

QR codecase studydata governance
0 likes · 11 min read
Why Your One‑Code Marketing System Turns Into a Dusty Shelf After Six Months
Smart Sea Tide
Smart Sea Tide
Aug 18, 2026 · Big Data

How to Choose and Architect a Data Lake Platform for Enterprise Digital Transformation

The article outlines the strategic need for a unified data lake in a digital‑focused enterprise, details functional and non‑functional requirements such as linear scalability, real‑time and batch processing, multi‑tenant support, security and governance, and presents a comprehensive architecture design that integrates storage, compute, and management components.

Data Lakebig datacloud-native
0 likes · 19 min read
How to Choose and Architect a Data Lake Platform for Enterprise Digital Transformation
Digital Deification
Digital Deification
Aug 17, 2026 · Industry Insights

BA vs DA: The Semantic Gap That Derails AI Projects — Ontology as the Missing Layer

The article explains how misaligned semantics between business architecture (BA) and data architecture (DA) — previously manageable via human translation — become fatal when AI systems require machine-executable logic, arguing that ontology provides the necessary rule-based semantic layer to align business objects with machine reasoning for reliable enterprise AI.

AI readinessBusiness ArchitectureSemantic Alignment
0 likes · 7 min read
BA vs DA: The Semantic Gap That Derails AI Projects — Ontology as the Missing Layer
AI Engineer Programming
AI Engineer Programming
Aug 17, 2026 · Artificial Intelligence

What Exactly Is an Enterprise Context Layer for AI?

The article analyzes the concept of an enterprise context layer for AI, breaking down its components—knowledge, expertise, and policies—into AI‑ready data, semantics, and reusable skills, and outlines the five capabilities needed to build, govern, and activate this shared corporate brain.

AIContext LayerKnowledge Management
0 likes · 23 min read
What Exactly Is an Enterprise Context Layer for AI?
Data Bricklaying Diary
Data Bricklaying Diary
Aug 16, 2026 · Big Data

Ontology-Driven Data Governance: 8 Steps to Connect 4A from Business Scenarios to Feedback

This article presents an eight-step methodology for ontology-driven data governance that connects business, data, application, and technology architectures (4A) by starting from high-value business scenarios, establishing semantic kernels, mapping data evidence, linking application actions, referencing technical constraints, enforcing semantic quality, publishing usable semantic products, and closing the loop with operational feedback.

4A architectureAgentMCP Server
0 likes · 12 min read
Ontology-Driven Data Governance: 8 Steps to Connect 4A from Business Scenarios to Feedback
Data Bricklaying Diary
Data Bricklaying Diary
Aug 15, 2026 · Industry Insights

Beyond 4A: Ontology-Driven Data Governance for AI-Ready Enterprise Architecture

This article explains why traditional 4A enterprise architecture fails to support AI agents, proposes an ontology semantic platform as a computable cross-domain layer, and outlines an 8-step approach to transform static architecture assets into dynamic, AI-ready business context with semantic services and action contracts.

4A architectureAI agentsOntology-Driven Data Governance
0 likes · 13 min read
Beyond 4A: Ontology-Driven Data Governance for AI-Ready Enterprise Architecture
Frontline Investigation
Frontline Investigation
Aug 13, 2026 · Industry Insights

Why Granular Data Classification Discourages Business Usage

The article argues that overly detailed data classification creates semantic, permission, and temporal gaps between security labels and actual workflows, causing business teams to avoid data or bypass processes. It proposes embedding labels into application, usage, transfer, and exit stages to answer who can do what with which data for how long, and suggests starting with high-frequency scenarios rather than comprehensive models.

business usabilitycompliancedata classification
0 likes · 11 min read
Why Granular Data Classification Discourages Business Usage
Data Integration and Governance
Data Integration and Governance
Aug 13, 2026 · Artificial Intelligence

Why Traditional Wide-Table Data Warehousing Won’t Suffice in the Data Agent Era

The article argues that while wide tables remain useful for fixed, high‑frequency analyses, the rise of Data Agents requires data warehouses to go beyond simple tables and provide business semantics, unified metrics, context, and governance so AI can understand and answer complex business questions accurately.

AI AnalyticsData Agentbusiness semantics
0 likes · 18 min read
Why Traditional Wide-Table Data Warehousing Won’t Suffice in the Data Agent Era
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Aug 11, 2026 · Artificial Intelligence

Why Ontology Is Suddenly in China’s National Data Policy and What It Means for AI

The article explains how the Chinese National Data Administration’s new policy highlights ontology for the first time, clarifies what ontology is compared to databases and knowledge graphs, and argues that it is essential now to overcome large‑model limits, empower AI agents, and shift data governance from mere management to true semantic utilization.

AI agentsdata governanceknowledge graph
0 likes · 6 min read
Why Ontology Is Suddenly in China’s National Data Policy and What It Means for AI
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Aug 9, 2026 · Artificial Intelligence

Semantic Layer, Ontology, and Enterprise Context Layer: How They Nest for AI‑Ready Data

The article explains why most AI projects fail due to poor data structure, then breaks down the three nested layers—Semantic Layer, Ontology, and Enterprise Context Layer—showing their distinct purposes, how they build on each other, real‑world examples, governance challenges, and why proper investment sequencing matters for AI‑ready data infrastructure.

AI data architectureSemantic Layerdata governance
0 likes · 23 min read
Semantic Layer, Ontology, and Enterprise Context Layer: How They Nest for AI‑Ready Data
Random Bulletin
Random Bulletin
Aug 9, 2026 · Big Data

Metadata Management at Million‑QPS Scale: From Chaos to a Unified Catalog

The article explains how scattered, hard‑coded metadata in large‑scale systems leads to unknown impact of changes, and walks through building a unified metadata center with a single source of truth, automatic collection, lineage, impact analysis, asset discovery, active metadata, and governance, while highlighting practical trade‑offs and real‑world tools.

active metadatadata catalogdata governance
0 likes · 19 min read
Metadata Management at Million‑QPS Scale: From Chaos to a Unified Catalog
Architect
Architect
Aug 8, 2026 · Databases

Ontology as the Semantic Control Plane for Agent Fact Systems

The article explains how an ontology—defining objects, relationships, constraints, and inferable boundaries within a domain—serves as a semantic control plane between the fact and action layers of an agent‑driven system, ensuring consistent interpretation, validation, and lifecycle management of business facts.

Agent SystemsGraph DatabasesRDF
0 likes · 18 min read
Ontology as the Semantic Control Plane for Agent Fact Systems
Data Integration and Governance
Data Integration and Governance
Aug 7, 2026 · Fundamentals

What’s the Difference Between Data Elements, Resources, Products, and Assets? A Complete Guide

Enterprises generate massive data daily, yet without a complete lifecycle of management, governance, productization, and assetization, that data remains valueless; this article breaks down the four core concepts—data resources, data products, data assets, and data elements—and explains how they interrelate to create sustainable business value.

Data AssetData ElementData Value Chain
0 likes · 15 min read
What’s the Difference Between Data Elements, Resources, Products, and Assets? A Complete Guide
Frontline Investigation
Frontline Investigation
Aug 6, 2026 · Industry Insights

Why Data Sharing Projects Stall: When 'Same Metric' Means Different Things

This article argues that data sharing initiatives often fail not due to access permissions but because identical metric names mask divergent definitions, statistical scopes, and processing logic, a problem amplified in AI-driven workflows where semantic context must be explicitly preserved for reliable automated decisions.

AI GovernanceData Qualitycross-domain data circulation
0 likes · 12 min read
Why Data Sharing Projects Stall: When 'Same Metric' Means Different Things
Data Integration and Governance
Data Integration and Governance
Aug 6, 2026 · Operations

How to Build a Complete Data Metric System: A Step‑by‑Step Guide

The article explains why many companies only have a metric list, not a true metric system, and outlines a six‑step framework—defining goals, decomposing metrics along business chains, unifying definitions, mapping to data sources, ensuring data quality, and managing the full lifecycle—to turn numbers into actionable business insights.

Business IntelligenceData MetricsMetric Framework
0 likes · 13 min read
How to Build a Complete Data Metric System: A Step‑by‑Step Guide
Digital Deification
Digital Deification
Aug 5, 2026 · Industry Insights

MDM Platform Beginner's Guide: 4 Core Functions New Hires Must Master

This article introduces MDM (Master Data Management) platform fundamentals for manufacturing newcomers, covering five core modules, comparing SAP MDG with domestic MDM products, and providing a 7-day hands-on learning plan to master data application, query, quality checks, and distribution monitoring.

Data DistributionData QualityMDM
0 likes · 11 min read
MDM Platform Beginner's Guide: 4 Core Functions New Hires Must Master
Smart Sea Tide
Smart Sea Tide
Aug 4, 2026 · Industry Insights

Why Are Fewer Companies Talking About Data Middle Platforms and Big Data Platforms Today?

The article explains that the decline of buzzwords like “big data platform” and “data middle platform” stems not from reduced data value but from a shift toward rational industry perception, highlighting the myth of scale, misaligned strategies, organizational challenges, and the amplified issues in the AI era.

AIbig datadata governance
0 likes · 8 min read
Why Are Fewer Companies Talking About Data Middle Platforms and Big Data Platforms Today?
Data Integration and Governance
Data Integration and Governance
Aug 3, 2026 · Information Security

How to Implement Data Masking: Static vs Dynamic vs Encryption

The article explains that true data masking goes beyond simply hiding phone numbers, detailing how to identify sensitive data, preserve business usefulness, and choose between static masking, dynamic masking, and encryption based on processing stage, recoverability needs, and access controls.

data governancedata maskingdynamic masking
0 likes · 15 min read
How to Implement Data Masking: Static vs Dynamic vs Encryption
DataFunTalk
DataFunTalk
Aug 3, 2026 · Artificial Intelligence

Why Ontology, Not Speed, Is the Real Bottleneck After Rapid AI App Deployment

The talk shows that while AI applications can be built in hours, the true engineering challenge shifts from fast coding to establishing shared ontologies, tool layers, and governance so that agents can scale across the entire value chain without creating isolated silos.

AI agentsRapid DeploymentValue Chain
0 likes · 9 min read
Why Ontology, Not Speed, Is the Real Bottleneck After Rapid AI App Deployment
Data Bricklaying Diary
Data Bricklaying Diary
Aug 3, 2026 · Artificial Intelligence

Ontology: A Modeling Mindset for Machine-Understandable Business Semantics

The article argues ontology is not a new technology but a modeling mindset that defines business objects, relationships, states, rules, and actions to create a machine-understandable semantic layer, enabling AI agents to act within explicit business constraints rather than just generating responses.

AI agentsKnowledge ManagementMBSE
0 likes · 13 min read
Ontology: A Modeling Mindset for Machine-Understandable Business Semantics
Yunqi AI+
Yunqi AI+
Aug 1, 2026 · Artificial Intelligence

From DDD to Ontology: Turning Domain Knowledge into AI‑Ready Semantic Contracts

The article explains how to evolve a DDD‑based domain model into a cross‑system ontology that provides AI agents with unified facts, computable logic, and executable actions, using a six‑step process illustrated by a customer‑health‑score case and detailed governance practices.

AI AgentDomain-Driven DesignSemantic Service
0 likes · 27 min read
From DDD to Ontology: Turning Domain Knowledge into AI‑Ready Semantic Contracts
Data Bricklaying Diary
Data Bricklaying Diary
Aug 1, 2026 · Big Data

AI Data Engineering: The Data Supply System for the Agent Era

This article defines AI Data Engineering as a data supply system for large models and agents, extending traditional data engineering with semantic modeling, RAG, controlled data services, permission governance, and feedback loops to make data understandable, retrievable, callable, and auditable for reliable enterprise AI deployment.

AI data engineeringAgentsData Services
0 likes · 11 min read
AI Data Engineering: The Data Supply System for the Agent Era
ITPUB
ITPUB
Jul 30, 2026 · Industry Insights

Why Data Professionals Avoid Saying They Work on Data Warehouses

The article explains that although modern data platforms are rebranded as lakehouses or AI data foundations, the core data‑warehouse tasks—ingestion, cleaning, modeling, metric alignment, quality, lineage and governance—remain unchanged, and practitioners often hide the term to avoid being labeled as using outdated technology.

AILakehousedata governance
0 likes · 14 min read
Why Data Professionals Avoid Saying They Work on Data Warehouses
Digital Planet
Digital Planet
Jul 29, 2026 · Industry Insights

The Harsh Truth I Found After Helping Seven Companies Digitally Transform

The article argues that most digital‑transformation projects become superficial showpieces because data sharing is blocked by departmental power struggles, middle‑management inertia, and leadership that seeks low‑cost, non‑disruptive solutions, and it explains why only a true overhaul of processes, authority and culture can deliver real value.

consultingdata governancedigital transformation
0 likes · 9 min read
The Harsh Truth I Found After Helping Seven Companies Digitally Transform
Insight Construct
Insight Construct
Jul 27, 2026 · Industry Insights

Huawei’s Enterprise Architecture: The 4A Framework in Action

The article details Huawei’s 4A enterprise architecture—business, information, application, and technology layers—explaining its design process, governance model, and a real‑world MetaERP implementation that boosted efficiency by 40% and cut response time by half.

4A FrameworkBusiness ArchitectureHuawei
0 likes · 9 min read
Huawei’s Enterprise Architecture: The 4A Framework in Action
Data Integration and Governance
Data Integration and Governance
Jul 27, 2026 · Industry Insights

What Is a Metric Platform? Understanding Metric Management, Definitions, and Systems

Even though many enterprises have abundant data and dashboards, they still waste time reconciling numbers because the same metric often has multiple definitions; a metric platform provides a unified framework for defining, calculating, publishing, using, and governing metrics across the organization.

Business IntelligenceIndicator ManagementMetric Lifecycle
0 likes · 13 min read
What Is a Metric Platform? Understanding Metric Management, Definitions, and Systems
Frontline Investigation
Frontline Investigation
Jul 27, 2026 · Industry Insights

Compute Power as a Network: Why the Real Scarcity Isn't Hardware

As AI compute infrastructure evolves into a networked utility, the key challenge shifts from acquiring hardware to organizing distributed resources, data, models, and business needs through intelligent scheduling, governance, and cost-aware orchestration across edge, regional, and national layers.

AI DeploymentCost Optimizationcompute infrastructure
0 likes · 14 min read
Compute Power as a Network: Why the Real Scarcity Isn't Hardware
Digital Deification
Digital Deification
Jul 26, 2026 · R&D Management

EA & Ontology: Why Tools Fail — Misapplication vs. Inherent Flaws

This article evaluates two critiques of enterprise ontology and TOGAF ADM, validating their observed pain points — redundant modeling and expert dependency — while correcting the conclusion that the theories are flawed, arguing instead that failures stem from scenario mismatch, mechanical adoption, and missing governance, and offering tailored implementation guidelines.

Implementation StrategyTOGAF ADMarchitecture governance
0 likes · 19 min read
EA & Ontology: Why Tools Fail — Misapplication vs. Inherent Flaws
Digital Deification
Digital Deification
Jul 26, 2026 · Industry Insights

No Standard Answer for Digital Transformation: 10 Lessons from Huawei's 25-Year Journey

This concluding article of a 24-part series distills Huawei's 25-year digital transformation into 10 core insights and 10 actionable steps, emphasizing that successful transformation requires adapting principles to one's own context rather than copying Huawei's practices, with leadership commitment, process-first approach, data governance, and cultural change as key pillars.

Huaweibusiness strategycultural transformation
0 likes · 15 min read
No Standard Answer for Digital Transformation: 10 Lessons from Huawei's 25-Year Journey
ITPUB
ITPUB
Jul 21, 2026 · Big Data

Why Big Data Is Suddenly Falling Out of Favor

Although national data production reached 52.26 ZB in 2025 and continues to grow, the term “big data” is disappearing from strategic discussions because it no longer provides the organizational credit it once did, and enterprises now demand concrete value attribution, responsibility, and AI‑driven accountability.

AI impactbig datadata governance
0 likes · 14 min read
Why Big Data Is Suddenly Falling Out of Favor
DataFunTalk
DataFunTalk
Jul 21, 2026 · Databases

Managing Multimodal Data for Agents: OceanBase’s Multimodal Table Solution

The article analyzes OceanBase’s newly released lake‑house AI database, focusing on its multimodal table feature that unifies storage, transaction, governance and hybrid search for images, PDFs, vectors and other non‑structured data, addressing the data‑consistency and latency challenges of Agent‑driven applications.

AI DatabaseColumn Group ConsistencyMultimodal Table
0 likes · 12 min read
Managing Multimodal Data for Agents: OceanBase’s Multimodal Table Solution
Data Bricklaying Diary
Data Bricklaying Diary
Jul 20, 2026 · Big Data

High-Quality Datasets: Beyond Cleaned Data for AI Tasks

This article defines high-quality datasets as task-oriented data products with business semantics, evidence traceability, usage boundaries, and continuous governance — not merely cleaned data — and explains why they are essential for reliable AI training, RAG, and Agent applications.

AI data preparationAgentData Quality
0 likes · 13 min read
High-Quality Datasets: Beyond Cleaned Data for AI Tasks
Frontline Investigation
Frontline Investigation
Jul 19, 2026 · Artificial Intelligence

High-Quality AI Datasets: Why Business Judgment Beats Data Volume

The article explains that high-quality AI datasets for industry require scenario samples capturing complete business judgment cycles, expert involvement in defining evaluation samples, and continuous feedback from production, not merely accumulating more raw data.

Chinese data policyIndustry AIbusiness judgment
0 likes · 12 min read
High-Quality AI Datasets: Why Business Judgment Beats Data Volume
TechVision Expert Circle
TechVision Expert Circle
Jul 16, 2026 · Artificial Intelligence

Enterprise AI Trends for H2 2026: Key Priorities for Tech Leaders

In the second half of 2026, enterprise AI shifts from adoption to reliable, cost‑effective deployment, with six key trends—including multi‑agent orchestration, GraphRAG retrieval, MoE model clusters, AI observability, built‑in data governance, and reorganized AI engineering roles—guiding tech leaders toward trustworthy AI systems.

AI AgentAI ObservabilityAI Team Structure
0 likes · 13 min read
Enterprise AI Trends for H2 2026: Key Priorities for Tech Leaders
Data Bricklaying Diary
Data Bricklaying Diary
Jul 16, 2026 · Big Data

Building Business Semantic Models for Ontology-Driven Data Governance

The article explains how to transform business models into machine-understandable business semantic models for ontology-driven data governance, covering eight key content types including concepts, relationships, states, processes, rules, metrics, evidence, and action contracts, plus transformation steps, granularity control, and deliverables such as semantic glossaries and relationship models.

AI AgentOntology-Driven Data GovernanceSemantic Assets
0 likes · 16 min read
Building Business Semantic Models for Ontology-Driven Data Governance
Yunqi AI+
Yunqi AI+
Jul 15, 2026 · Artificial Intelligence

How to Build Enterprise AI Management Agents: Path, Design, and Governance

The article analyzes how to construct enterprise AI management agents by distinguishing them from personal efficiency agents, defining data semantics and governance, designing two capability chains for query and analysis, and outlining a step‑by‑step implementation roadmap with security, evaluation, and ownership practices.

AIAgentEnterprise Management
0 likes · 18 min read
How to Build Enterprise AI Management Agents: Path, Design, and Governance
Data Bricklaying Diary
Data Bricklaying Diary
Jul 15, 2026 · Big Data

Ontology-Driven Data Governance: Business Modeling from Research to Symbolic Models

This article explains how ontology-driven data governance requires thorough business research and modeling before database design, detailing a six-element framework (objects, processes, states, rules, data sources, Agent opportunities) and visual modeling techniques like OPM and BPMN to create stable semantic foundations for data mapping and AI agents.

AI agentsBPMNOPM
0 likes · 12 min read
Ontology-Driven Data Governance: Business Modeling from Research to Symbolic Models
dbaplus Community
dbaplus Community
Jul 14, 2026 · Artificial Intelligence

Achieving 85%+ Accuracy: Qunar’s SQL Agent for Intelligent Data Retrieval and Efficiency Gains

The article details Qunar’s AI‑driven SQL Agent project, describing how data‑governance, multi‑agent architecture, prompt design, and RAG techniques were combined to reduce data‑access latency, raise query accuracy above 85%, and streamline the end‑to‑end data‑service workflow for business users.

AI operationsRAGSQL Agent
0 likes · 24 min read
Achieving 85%+ Accuracy: Qunar’s SQL Agent for Intelligent Data Retrieval and Efficiency Gains
TechVision Expert Circle
TechVision Expert Circle
Jul 13, 2026 · Industry Insights

Why the $515 B AI Services Market Won’t Be Won by Models Alone

A $515 billion AI services market forecast for 2030 highlights that enterprises face data readiness, unclear use cases, and a shortage of AI‑business translators, making engineering and vertical solutions the real profit drivers rather than the models themselves.

AI EngineeringAI Product Managerai-services
0 likes · 8 min read
Why the $515 B AI Services Market Won’t Be Won by Models Alone
Architect's Alchemy Furnace
Architect's Alchemy Furnace
Jul 12, 2026 · Product Management

Grounded Flight: A Practical Blueprint for Evolving AI Product Managers

The article outlines how future software will serve AI agents instead of humans, describes three essential cognitive shifts for AI product managers, poses four critical questions, presents a detailed capability map covering business understanding, technical principles, data handling, evaluation, prompt design, product design, and ethics, and concludes with actionable advice for thriving in the fast‑moving AI product landscape.

AI Product ManagementEthicsRAG
0 likes · 27 min read
Grounded Flight: A Practical Blueprint for Evolving AI Product Managers
CTO Full-Stack Academy
CTO Full-Stack Academy
Jul 11, 2026 · Industry Insights

Designing an Enterprise Master Data Management (MDM) Platform: A Complete Guide

This article presents a comprehensive, step‑by‑step design and implementation guide for an enterprise‑wide Master Data Management (MDM) platform, covering core principles, five implementation phases, detailed four‑level functional modules, integration patterns, data quality governance, RBAC3 permission control, and solutions to common deployment challenges.

Data IntegrationData QualityMDM
0 likes · 28 min read
Designing an Enterprise Master Data Management (MDM) Platform: A Complete Guide
Data Bricklaying Diary
Data Bricklaying Diary
Jul 11, 2026 · Industry Insights

Why AI-Era Data Governance Requires Ontology-Driven High-Quality Datasets

This article traces data governance evolution from master data consistency and metadata visibility to ontology-driven business semantic modeling, arguing that AI-era governance must produce high-quality datasets that are AI-usable, trustworthy, evaluable, and continuously optimized through explicit business object, process, rule, and evidence modeling.

AI data supplyDCMM 2.0Master Data Management
0 likes · 14 min read
Why AI-Era Data Governance Requires Ontology-Driven High-Quality Datasets
Frontline Investigation
Frontline Investigation
Jul 10, 2026 · Industry Insights

Government Data Sharing: The Hard Part Isn't Collection, It's Accountable Backflow

The article analyzes why government data sharing in China often stalls at upward aggregation, arguing that true sharing requires closed-loop mechanisms — clear responsibilities, conditional access, timed responses, and data backflow to frontline units — as illustrated by Sichuan's new implementation rules.

Digital GovernmentSichuan policyclosed-loop sharing
0 likes · 13 min read
Government Data Sharing: The Hard Part Isn't Collection, It's Accountable Backflow
Data Bricklaying Diary
Data Bricklaying Diary
Jul 10, 2026 · Big Data

High-Quality Datasets: The New Data Governance Battlefield After DCMM 2.0

The article argues that post-DCMM 2.0, data governance must evolve from asset management to building high-quality datasets—trustworthy, semantically clear, quality-measurable, version-traceable, and compliant—to reliably support AI training, evaluation, knowledge augmentation, and agent workflows, requiring semantic foundations and AI data engineering.

AI data engineeringDCMM 2.0Data Quality
0 likes · 12 min read
High-Quality Datasets: The New Data Governance Battlefield After DCMM 2.0
Data Integration and Governance
Data Integration and Governance
Jul 8, 2026 · Big Data

How to Evaluate Data Asset Quality: Focus on Completeness, Accuracy, Consistency, and Timeliness

The article explains why data quality is critical for business value, defines the four core dimensions—completeness, accuracy, consistency, timeliness—details metrics and evaluation methods for each, presents case studies, outlines a weighted scoring model, and describes practical implementation steps and tool support for systematic data‑asset quality assessment.

Data AssetData Qualityaccuracy
0 likes · 19 min read
How to Evaluate Data Asset Quality: Focus on Completeness, Accuracy, Consistency, and Timeliness
Data Bricklaying Diary
Data Bricklaying Diary
Jul 8, 2026 · Industry Insights

DCMM 2.0: Data Governance Shifts from Management to Asset Operations

DCMM 2.0 (GB/T 36073-2025), effective July 1, 2026, expands from 8 to 9 capability domains and 29 to 33 items, adding a Data Asset domain with ownership, valuation, and operations items, renaming Data Application to Data Application Circulation with external data management, and shifting security to compliance-focused protection, signaling a move from data management to asset operations.

AI data readinessDCMMGB/T 36073
0 likes · 13 min read
DCMM 2.0: Data Governance Shifts from Management to Asset Operations
Data Integration and Governance
Data Integration and Governance
Jul 6, 2026 · Fundamentals

Why Messy Data Demands Immediate Cleaning: A Complete Data‑Cleaning Workflow

Many analysts rush to build dashboards on raw data, only to discover mismatched numbers and business push‑back, because the data was never properly cleaned; this article outlines a structured, rule‑based, verifiable and reusable data‑cleaning process that starts with clear goals, proceeds through inventory, rule definition, standardization, mapping, validation, and ends with documented deliverables.

AnalyticsData QualityETL
0 likes · 13 min read
Why Messy Data Demands Immediate Cleaning: A Complete Data‑Cleaning Workflow
Data Bricklaying Diary
Data Bricklaying Diary
Jul 6, 2026 · Big Data

Ontology-Driven Data Governance: From Metadata to Business Semantics for AI Agents

The article argues that traditional master data and metadata management only achieve data visibility and traceability, while ontology-driven governance adds a computable business semantic layer—modeling objects, processes, states, rules, and actions via OPM and ontology—to enable AI agents to execute tasks reliably within real business contexts.

AI agentsBusiness Semantic GovernanceMaster Data Management
0 likes · 15 min read
Ontology-Driven Data Governance: From Metadata to Business Semantics for AI Agents
Frontline Investigation
Frontline Investigation
Jul 5, 2026 · Information Security

Data Cross-Border Compliance: The Real Risk Isn't the Path—It's the Business Purpose

The article argues that data cross-border compliance misjudgments stem from focusing on technical paths rather than business purposes, outlines regulatory shifts toward purpose-based assessment, identifies three common misconceptions, provides a four-question framework for evaluation, and emphasizes building systems that enable explainable data flows.

Chinese regulationsbusiness purposedata classification
0 likes · 16 min read
Data Cross-Border Compliance: The Real Risk Isn't the Path—It's the Business Purpose
Data Integration and Governance
Data Integration and Governance
Jul 3, 2026 · Fundamentals

Data Governance Explained: Standards, Quality, Security, and Metadata Management

The article breaks down data governance into four essential pillars—data standards, data quality, data security, and metadata management—illustrating why each is critical, how they interrelate, and practical steps enterprises can take to embed them into data pipelines for trustworthy, secure, and discoverable analytics.

Data Qualitydata architecturedata governance
0 likes · 14 min read
Data Governance Explained: Standards, Quality, Security, and Metadata Management
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Jun 29, 2026 · Big Data

How DataWorks Data Agent Evolved Across Three Stages and Its Cloud‑Native Engineering Practices

The article systematically outlines DataWorks Data Agent’s progression from a Copilot‑assisted tool to human‑AI collaboration and finally AI‑driven autonomy, details its four‑agent product matrix covering data development, operations diagnostics, autonomous governance and ChatBI, describes three architecture iterations (Dify, AgentScope, QwenCode/OpenClaw) and a cloud‑managed deployment, and cites real‑world efficiency gains such as cutting development cycles from hours to minutes.

AI AgentData AgentDataWorks
0 likes · 15 min read
How DataWorks Data Agent Evolved Across Three Stages and Its Cloud‑Native Engineering Practices
Data Integration and Governance
Data Integration and Governance
Jun 29, 2026 · Industry Insights

What Is a Data Asset? Distinguish Data Resources, Data Elements, Digital Assets, and Accounting Entry

The article explains that data resources are usable data collections, data elements are data that actively participates in business processes, data assets are controllable, measurable resources that generate economic value, digital assets encompass a broader range of digital value, and outlines the necessary data management and governance steps to turn raw data into recognized assets.

Data AssetData ElementData Resource
0 likes · 14 min read
What Is a Data Asset? Distinguish Data Resources, Data Elements, Digital Assets, and Accounting Entry
Frontline Investigation
Frontline Investigation
Jun 28, 2026 · Information Security

Automated Data Collection: The Real Challenge Is Explainable Boundaries, Not Technical Feasibility

China's new draft national standard for automated network data collection tools shifts focus from technical feasibility to explainable compliance, requiring organizations to define collection boundaries, purpose, impact, and audit trails across the entire data lifecycle, especially for AI training data.

AI training dataTC260 standardWeb Scraping
0 likes · 15 min read
Automated Data Collection: The Real Challenge Is Explainable Boundaries, Not Technical Feasibility
TechVision Expert Circle
TechVision Expert Circle
Jun 27, 2026 · Industry Insights

How CIOs Can Navigate the Deep‑Water Phase of Digital Transformation

The article examines why many enterprises stall in the deep‑water stage of digital transformation, detailing three common pitfalls—legacy‑system debt, unusable data, and AI demo traps—and offers a step‑by‑step architecture evolution, AI Agent rollout, pragmatic data‑governance, and organizational tactics for CIOs to break the deadlock.

AI AgentCIOarchitecture
0 likes · 14 min read
How CIOs Can Navigate the Deep‑Water Phase of Digital Transformation
DataFunSummit
DataFunSummit
Jun 25, 2026 · Big Data

Evolution and Engineering Practices of DataWorks Data Agent

The article systematically outlines DataWorks Data Agent’s three‑stage evolution—from Copilot assistance to human‑AI collaboration and finally AI‑driven autonomy—details its four‑agent product matrix covering the full data lifecycle, describes the cloud‑managed engineering rollout, and presents a Taobao flash‑sale case where development cycles shrank from hours to minutes, highlighting efficiency gains, security measures, and architectural iterations.

AI AgentCloud ManagedData Agent
0 likes · 13 min read
Evolution and Engineering Practices of DataWorks Data Agent