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Faster Metrics with Data Marts (overcoming Data Warehouses challenges)
We explain what data marts are and how they help business leaders in managing their company performance by having faster and better metrics. We start by giving a short history of data warehousing and the typical challenges, and then move over to explain data marts and how they can help companies get better metrics today.
What As a Data Mart? And What Challenges Does It Solve?
Data marts simplify access to meaningful business metrics, helping leaders drive performance improvements with clarity and precision. This article breaks down what a data mart is, how it functions, and what makes a metric truly effective. Explore how data marts consolidate information from multiple sources to provide actionable insights, avoid common data pitfalls, and enable smarter decision-making.
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.
Driving Sustainability with Data: Improving CO₂ Emissions Reporting Across Supply Chains
Accurate CO₂ emissions reporting is vital for meeting sustainability goals and regulatory requirements. This article delves into the challenges of Scope 3 emissions, the importance of clean data, and how structured data systems like sustainability marts can improve reporting, ensure compliance, and support better decision-making for businesses.
Flat Tables vs. Snowflake Semantic Models: The Ultimate BI Data Debate
Structuring data for BI is a key decision that impacts performance, scalability, and data consistency. This article compares flat tables and semantic models, highlighting the strengths and trade-offs of each. Learn how a hybrid approach can offer the best of both worlds—combining consistency, flexibility, and efficient analytics across tools and teams.
What the Claude Code Leak Reveals About Enterprise AI
The Claude Code leak provides a practical look at AI architecture and where value is created in enterprise AI applications. This article explores why governance, workflows, and deterministic logic often matter more than AI itself when building reliable, cost-effective solutions.
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.
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.
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.
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.