Tag: Data Strategy
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.
Data Platform Cost Optimization: Practical Strategies for Query Performance, Storage, and Cloud Resource Management
Explore how you can dramatically reduce data platform costs without sacrificing performance. This guide breaks down actionable techniques across query tuning, incremental data loading, cloud resource management, and storage lifecycle design.
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.
Roles in the Context of the Analytics Workflow
Code-based analytics workflows offer a unique advantage - the ability to combine robust collaboration with strict governance. While technically straightforward to implement, this approach only reaches its full potential when aligned with well-adapted business and analytics processes.
What is a Modular Data Platform?
Learn why modularity is crucial for building scalable, efficient data architectures. This article covers the core components of modern data platforms, from ingestion to governance, and shares best practices for flexibility, interoperability, and security.
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.