Tag: Data Governance
AI Access Management: Three Governance Layers
AI introduces a new access management challenge: what the user can see, what the agent can see, and what the LLM can see are not the same thing. This article explores how AI governance can be integrated into an existing hub-and-domain data architecture through identity groups, secured schemas, and governed AI harnesses.
Hub & Domains: A Practical Data Operating Model
Domains bring data ownership closer to the business, but governance, access management, and shared standards remain difficult to decentralize. This article explores a Hub & Domains operating model that combines business ownership with centralized governance and platform controls.
Certified Metrics: From Fact to Dashboard
Certified metrics require more than documented formulas. Learn how facts, measures, dimensions, aggregate metrics, and dashboards work together to create trusted and reusable business metrics.
Distributing Facts and Dimensions: Governance, Access & Ownership
Building facts and dimensions is only part of the challenge. This article explores how certified data should be distributed across the organization through controlled access paths, ownership models, governance processes, and support structures.
Scaling Secure Data Access: A Systematic RBAC Approach Using Entra ID
Establish scalable, secure access controls for your data platform with a systematic RBAC strategy built on Microsoft Entra ID. This article outlines a five-phase implementation—from user persona mapping to automated auditing—designed to balance flexibility, compliance, and operational efficiency.
A Simple Approach to Master Data Management to Unify Metrics and Insights
Discover the role of master data management (MDM) in achieving consistent and accurate business metrics. This article explains the concept of master data, outlines key challenges organizations face, and introduces two accessible approaches to MDM. By focusing on practical steps and avoiding common pitfalls, we show how businesses can enhance data quality without large budgets or complex systems.