Data Standards and Data Quality: Concepts, Frameworks, Tools, and Case Studies

Introduction – The session focuses on data standards and data quality, outlining three main parts: related concepts and frameworks, tools and technologies, and typical case studies.

01 Related Concepts and Framework – Explains the definition of data standards, differences between international and domestic frameworks (e.g., DCMM, DAMA), and how terms such as glossary, data dictionary, and data elements map to business terminology. It discusses the hierarchy of data standards from business terms to technical attributes, provides examples of master data and reference data standards, and introduces a maturity assessment model for data standards.

Data Quality – Defines data quality as the degree to which data meets business, management, and decision‑making needs. Highlights key dimensions such as authenticity, accuracy, uniqueness, completeness, consistency, relevance, and timeliness, and explains common causes of quality issues from technical and business perspectives.

02 Tools and Techniques – Describes how to capture data‑standard information models, convert them into management system content, and generate quality‑check rules. Shows a demo of metadata management, discusses handling structured versus unstructured data, and outlines four methods for governing unstructured data, including business‑driven governance, transformation to structured form, metadata mapping, and building an unstructured‑data asset system.

03 Typical Cases – Presents two real‑world projects: (1) building a data‑governance platform that enables self‑service data models, master‑data integration, and standardized data assets; (2) a data‑quality improvement initiative that implements monitoring rules for batch tables, streaming data (Kafka), multi‑table comparisons, indicator analysis, and low‑code rule customization.

Q&A Session – Answers ten questions covering evaluation standards for data quality, the relationship between data models and standards, classification of data standards, approaches to standardizing historical models and indicators, organizational guarantees for governance projects, low‑code maintenance of data definitions, scope definition and value demonstration, and automation of metadata extraction.