enterprise data

DataFunSummit

May 3, 2024 · Big Data

Comprehensive Guide to Enterprise Data Governance: Vision, Framework, Organization, Standards, Quality, and Security

This article presents a detailed overview of enterprise data governance, covering its vision and goals, three‑layer framework, organizational structure, institutional policies, data standards, quality management, metadata handling, security controls, lifecycle protection, and practical implementation cases.

Big DataData Securitydata governance

0 likes · 14 min read

Comprehensive Guide to Enterprise Data Governance: Vision, Framework, Organization, Standards, Quality, and Security

Architects Research Society

Sep 27, 2023 · Fundamentals

What Is the Common Data Model and Why Use It?

The Common Data Model provides a shared, standardized data language and metadata system that simplifies cross‑application data integration, reduces custom development effort, and enables consistent, extensible data structures for business and analytics scenarios across Microsoft Power Platform and Azure services.

Common Data ModelMicrosoft Power Platformdata integration

0 likes · 8 min read

What Is the Common Data Model and Why Use It?

DataFunTalk

Apr 21, 2023 · Fundamentals

Data Architecture and Data Modeling Overview, Solutions, and Enterprise Case Studies

This article explains data architecture and data modeling fundamentals, presents DAMA DMBOK concepts, outlines four practical solutions for model design, standard management, automated change control, and business mapping, and shares an enterprise manufacturing case study with Q&A on governance and efficiency.

Big DataData Modelingdata governance

0 likes · 21 min read

Data Architecture and Data Modeling Overview, Solutions, and Enterprise Case Studies

DataFunSummit

Apr 19, 2023 · Fundamentals

Data Governance Construction Path and Practice by Dipu Technology

The article presents Dipu Technology's comprehensive approach to data governance, outlining construction pathways, a typical enterprise digital platform framework, core governance concepts, implementation steps, case studies, and a Q&A session that together illustrate how to design, execute, and sustain effective data governance across business domains.

Big Datadata governancedata management

0 likes · 22 min read

Data Governance Construction Path and Practice by Dipu Technology

DataFunTalk

Jan 13, 2023 · Big Data

Data Governance Strategies and Practices: Insights from Leading Companies

The article explains the importance of data governance for organizations handling big data, distinguishes narrow and broad governance approaches, outlines strategic principles, and presents case studies from companies like Tencent, SF Tech, Huolala, and NetEase to illustrate effective governance practices.

Big Datacase studydata governance

0 likes · 8 min read

Data Governance Strategies and Practices: Insights from Leading Companies

DataFunSummit

Nov 12, 2022 · Big Data

SF Express Technology Data Governance Practice and Framework

This article details SF Express Technology’s decade‑long data governance journey, outlining its three‑phase evolution, comprehensive framework, key policies, organizational structure, and practical implementations such as master data management, data quality, metadata, data market, and security, highlighting lessons and best practices for enterprise data management.

Data SecurityMaster Data Managementdata governance

0 likes · 17 min read

SF Express Technology Data Governance Practice and Framework

Architects Research Society

Sep 6, 2022 · Fundamentals

Understanding Microsoft’s Common Data Model: Components, Benefits, and Real‑World Use

The article explains how Microsoft’s Common Data Model provides a shared metadata system and standardized, extensible data schemas that simplify integration across Power Apps, Power BI, Dynamics 365 and Azure, enabling consistent data semantics, easier app development, and scalable enterprise solutions.

Common Data ModelDynamics 365Microsoft Power Platform

0 likes · 7 min read

Understanding Microsoft’s Common Data Model: Components, Benefits, and Real‑World Use

DataFunTalk

Jun 30, 2022 · Artificial Intelligence

Building and Applying an Enterprise Relationship Knowledge Graph

This article presents a comprehensive case study on constructing an enterprise relationship knowledge graph for financial technology, detailing data preparation, ontology modeling, graph building, and three practical applications—path query, ultimate controller discovery, and group detection—demonstrating how AI-driven graph analytics can uncover hidden corporate connections.

AIenterprise datafinancial technology

0 likes · 11 min read

Building and Applying an Enterprise Relationship Knowledge Graph

DataFunSummit

Nov 30, 2021 · Fundamentals

Practical Reconstruction of a Data Management Governance Framework Based on DMBOK and Huawei Practices

This article presents a practical re‑design of the DAMA‑DMBOK data management framework, integrating Huawei's data approach and over a decade of governance experience, and details twelve knowledge domains with resources, examples, and guidance for building a comprehensive data governance system.

DMBOKKnowledge SharingPractical Framework

0 likes · 6 min read

Practical Reconstruction of a Data Management Governance Framework Based on DMBOK and Huawei Practices

Architects Research Society

Jul 22, 2021 · Big Data

Enterprise Data Strategy: Aligning Tactics, Governance, and the Experience Economy

This article explores how a clear enterprise data strategy—distinguishing strategic goals from tactical steps, emphasizing clean and governed data, and integrating analytics with business missions—drives reliable outcomes and supports the experience economy through coordinated CXM platforms and data products.

Big DataData StrategyExperience Economy

0 likes · 9 min read

Enterprise Data Strategy: Aligning Tactics, Governance, and the Experience Economy

Architects Research Society

Jan 11, 2021 · Fundamentals

Top Reasons Why MDM Implementations Fail

This article examines the common pitfalls that cause Master Data Management (MDM) projects to fail, including underestimating effort, insufficient resources, overly ambitious scope, lack of data governance, excessive rules, and inadequate executive support, offering practical insights for successful implementation.

Implementation ChallengesMDMdata governance

0 likes · 12 min read

Top Reasons Why MDM Implementations Fail

Architects Research Society

Dec 8, 2019 · Databases

Top 14 Misconceptions About Master Data Management (MDM)

This article debunks fourteen common misconceptions about Master Data Management, explaining why MDM must support enterprise‑wide needs, the importance of data quality, workflow, real‑time integration, vendor selection, and organizational change management for successful implementation.

MDMMaster Data Managementdata governance

0 likes · 8 min read

Top 14 Misconceptions About Master Data Management (MDM)

Architects Research Society

Nov 28, 2019 · Fundamentals

Four Common Master Data Management (MDM) Implementation Styles

This article explains the four primary MDM implementation styles—registry, consolidation, coexistence, and transaction/centralized—detailing their architectures, advantages, and suitability for organizations seeking a single source of truth, data quality, and governance across diverse source systems.

Implementation StylesMDMMaster Data Management

0 likes · 12 min read

Four Common Master Data Management (MDM) Implementation Styles

Architects Research Society

Jun 6, 2019 · Big Data

A Guide to Data Strategy: Key Questions and Framework for Executives

The article outlines a comprehensive data‑strategy framework for executives, highlighting business pressures, value, management, and data‑management considerations, and provides key questions to guide data discovery, business discovery, and the continual evolution of a practical, actionable data strategy.

Data Strategybusiness analyticsdata discovery

0 likes · 9 min read

A Guide to Data Strategy: Key Questions and Framework for Executives

Efficient Ops

Nov 10, 2015 · Databases

Choosing Between Commercial and Open‑Source Databases: Insights from a Veteran DBA

In this interview, seasoned database expert Lu Dongming recounts the two historic “springs” of the database industry, compares commercial and open‑source databases, and offers practical guidance on selecting the right database solution based on workload, cost, and architectural considerations.

DBMS historycommercial vs open-sourcedatabase selection

0 likes · 11 min read

Choosing Between Commercial and Open‑Source Databases: Insights from a Veteran DBA