By Pete Stiglich Semantic Web technologies can strengthen data governance and stewardship by improving how organizations understand and connect their data. Rather than focusing only…
Data Governance and Stewardship Organizations
By Pete Stiglich Provides an example organizational structure for implementing governance and stewardship in phases, tailored to culture, size, and data complexity. It focuses on…
Enterprise Data Management Foundations and Principles
By Anne Marie Smith, Ph.D., CBIP, ARM, FIDM, FIIM Enterprise Data Management facilitates the management of data as a valuable asset of an enterprise. Using an…
The Importance of Teaching and Implementing Ethical Data Use in Organizations
By Anne Marie Smith, Ph.D., CBIP, ARM, FIDM, FIIM In today’s data-driven world, organizations rely heavily on data to make strategic decisions, improve customer experiences,…
The Unfinished Business of Generative AI Governance
By Dr. Tejasvi Addagada Generative AI is no longer a siloed experiment. It is shaping customer interactions, redefining knowledge work, and altering the very nature…
Examining and Refuting Some Common Data Governance Myths
By Anne Marie Smith, Ph.D., FIDM, ADGP Many organizations are hesitant to develop a data management/data governance program since they believe common myths about the…
Data Integrity in a New Light – A New View of Data Management and Ethics
By Anne Marie Smith, Ph.D., FIDM, ADGP A new definition of data integrity focuses on an ethical approach to managing and using data, providing an…
Business Glossary – Sooner Rather than Later
By Pete Stiglich Argues that implementing an enterprise business glossary early is one of the best investments for major data initiatives like MDM, EDW, CRM,…
Disruption caused by Data Governance?
By Pete Stiglich Addresses the concern that governance might slow teams down or feel like a ‘roadblock.’ The post argues that data governance is inherently…
Data Governance and Data Stewardship – keys to successful enterprise data initiatives
By Pete Stiglich Explains that enterprise data initiatives succeed when business involvement is strong, and it distinguishes three kinds of involvement: stakeholders, data governance, and…
