Data Management & AI Governance
Data Management & AI Governance
Data is the core asset of modern digital transformation, and in the AI era, data quality and governance are the source of competitive advantage.
This section covers 19 topics.
Introduction to this section and its core frameworks.
A management framework that systematizes data management principles, governance organization, and operating processes, and clearly defines authority and…
A data management body of knowledge published by DAMA International that provides 11 Knowledge Areas and practical guidelines for organizations to effectively manage…
A data management capability assessment framework developed by the EDM Council (Enterprise Data Management Council) that structures an organization’s data management…
A data management system that centrally standardizes an enterprise’s core shared data (master data) — such as customers, products, suppliers, and organizations —…
A framework that defines big data through five attributes — Volume, Velocity, Variety, Veracity, and Value — and designs collection, storage, processing, and analysis…
A scalable data platform architecture that centrally loads structured (DB/DW), semi-structured (JSON/XML/logs), and unstructured (image/video/text) data in raw format…
A decentralized data architecture in which, instead of a single central team managing data, each business domain team directly owns and manages its own data, serving…
NoSQL is a general term for non-relational databases that overcome the schema constraints and vertical-scaling limits of relational databases (RDBMS), while the CAP…
A data management system that measures and diagnoses the quality of an organization’s structured and unstructured data against criteria such as completeness,…
A standard process for data mining and analytics projects, applicable across industries, that iterates through six stages — from business understanding to deployment…
A standard data mining process proposed by SAS Institute that cycles through five stages — Sample, Explore, Modify, Model, and Assess — on large datasets to develop…
A theoretical framework that represents entities as nodes and the relationships between them as edges, mathematically analyzing complex connection structures. It…
A formal, explicit system of knowledge representation that defines the concepts (classes) of a specific domain and the relationships between them (properties,…
A privacy approach proposed by Dr. Ann Cavoukian that embeds privacy protection as the default from the design stage of systems, services, and business processes,…
A methodology and system that integrates machine-learning development (ML) and operations (Ops), automating the entire process of model training, deployment, and…
An integrated management framework that systematizes AI principles, governance organization, and operating processes to ensure ethics, fairness, transparency, and…
A governance system that embeds ethical principles such as Reliability, Transparency, and Fairness from the design stage onward, throughout the full lifecycle of AI…
The world’s first comprehensive AI regulation, enacted by the European Union in 2024, which systematically classifies the risk that AI systems can pose and imposes…