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Data Bricklaying Diary

Records practices, thoughts, and pitfalls on the data grunt-work journey, sharing content on data platforms, data analysis, data processing, data governance, knowledge graphs, and more. Less theory, more hands‑on, making complex data technologies simple.

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Latest from Data Bricklaying Diary

86 recent articles

Data Bricklaying Diary

Jul 17, 2026 · Big Data

Ontology-Driven Data Governance: Mapping Data Assets to Business Semantic Assets

This article details the third step of ontology-driven data governance: mapping existing data assets — tables, fields, documents, logs, APIs, model outputs, and human confirmations — to a business semantic model through object, attribute, state, relationship, rule, evidence, and service-action mappings, producing semantic lineage, evidence chains, and high-quality dataset candidates for semantic queries, quality validation, and agent services.

Evidence ChainOntology-Driven Data Governanceagent services

0 likes · 15 min read

Ontology-Driven Data Governance: Mapping Data Assets to Business Semantic Assets

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 AgentData GovernanceOntology-Driven Data Governance

0 likes · 16 min read

Building Business Semantic Models for Ontology-Driven Data Governance

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 agentsBPMNData Governance

0 likes · 12 min read

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

Data Bricklaying Diary

Jul 14, 2026 · Big Data

Ontology-Driven Data Governance: Start with Business Scenarios, Not Table Fields

This article explains why ontology-driven data governance should begin by selecting high-value business scenarios rather than analyzing existing table fields, detailing five selection criteria, a pain-point mapping method, and a value-opportunity formula with concrete examples like equipment monitoring and legal case review.

AI AgentOntology-Driven Data Governancebusiness scenario selection

0 likes · 11 min read

Ontology-Driven Data Governance: Start with Business Scenarios, Not Table Fields

Data Bricklaying Diary

Jul 13, 2026 · Big Data

Building Ontology-Driven Data Governance: Objects, Processes, Roles & Operating Mechanisms

This article presents a comprehensive framework for ontology-driven data governance that extends beyond semantic modeling into a continuous operating system covering three governance object categories, a six-step scenario-based process, eight cross-functional roles, seven operating mechanisms, and integration with existing data governance capabilities to support AI-ready semantic assets and Agent actionability.

AI data engineeringAgent systemsOntology-Driven Data Governance

0 likes · 16 min read

Building Ontology-Driven Data Governance: Objects, Processes, Roles & Operating Mechanisms

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 2.0Data Governance

0 likes · 14 min read

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

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 Governance

0 likes · 12 min read

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

Data Bricklaying Diary

Jul 9, 2026 · Artificial Intelligence

Industrial AI Can't Just Predict: Ontology Platforms Enable Semantic Safety Loops

This article argues that industrial AI must go beyond predictive models by integrating ontology platforms that embed equipment, process, and safety semantics, using an activated carbon box temperature warning case to show how semantic context turns raw alerts into actionable, governed safety decisions.

OPMactivated carbon boxindustrial AI

0 likes · 17 min read

Industrial AI Can't Just Predict: Ontology Platforms Enable Semantic Safety Loops

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 readinessDCMMData Governance

0 likes · 13 min read

DCMM 2.0: Data Governance Shifts from Management to Asset Operations

Data Bricklaying Diary

Jul 7, 2026 · Industry Insights

Why Police AI's First Entry Point Is Transcript Systems, Not Document Generation

The article argues that transcript systems, not document generation, should be the primary entry point for AI in policing, because transcripts capture evolving case facts, identities, evidence leads, and contradictions; with a semantic platform, they become reusable case context for evidence review, approvals, document drafting, and supervision.

AI boundariesLaw EnforcementPolice AI

0 likes · 15 min read

Why Police AI's First Entry Point Is Transcript Systems, Not Document Generation