Data Governance Practices and Implementation Path at Dipu Technology
This article presents Dipu Technology's comprehensive data governance methodology, covering construction paths, a typical enterprise digital platform framework, core governance components, practical case studies, and a Q&A session that together illustrate how businesses can design, implement, and sustain effective data governance across the organization.
The article introduces Dipu Technology's data governance experience, outlining two main topics: the construction path for data governance and practical sharing of governance practices.
It explains that business digitization aims to integrate business, information, and data flows, and describes the challenges of aligning disparate systems and data models, emphasizing the need for governance to reduce information loss.
The core governance actions are divided into business data governance (creating true data representations) and analysis system governance (designing analytical structures).
A typical enterprise digital platform framework is presented, consisting of business systems, a data middle platform with layered data (source, detail, summary, application), self‑service data consumption, and intelligent decision‑making components.
Key governance components are detailed: governance system design, deep business data governance (including data asset cataloging, data modeling, standards, distribution, and quality improvement), and analysis data system design (indicator management, performance metric design, and data capability supply).
The article then outlines a step‑by‑step data governance rollout, from asset inventory and mapping to standardization, quality checks, and external empowerment through organizational roles and processes.
Practical case studies illustrate how a food processing company and a manufacturing enterprise applied these methods, covering governance system design, indicator design, data catalog creation, and stakeholder alignment.
A Q&A section addresses common concerns about master data, data standards, new databases, governance value measurement, and long‑term implementation strategies.
The piece concludes with a thank‑you note and references to the DataFun community and upcoming data intelligence events.
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