Tag Archives: Data Strategy

Measuring Value in a Modern FS Data Platform: Framework for Understanding, Quantifying, and Communicating Data Value in FS

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Measuring Value in a Modern FS Data Platform reframes how Financial Services organisations should evaluate data platforms. Rather than measuring pipelines, volumes, or dashboards, true value emerges from consumption, velocity, optionality, semantic alignment, and control. By landing raw data, accelerating delivery through reuse, organising around business domains, and unifying meaning in a layered Bronze–Silver–Gold–Platinum architecture, modern platforms enable faster decisions, richer analytics, regulatory confidence, and long-term adaptability. This article provides a practical, consumption-driven framework for CDOs and CIOs to quantify and communicate real data value.

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Gold & Platinum Layer Architecture After Silver

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Modern Financial Services data platforms require more than Bronze, Silver, and Gold layers to manage complexity, meaning, and governance. While Silver provides current-state truth and Gold delivers consumption-driven business meaning, neither resolves enterprise-wide semantics. This article introduces the Platinum layer as the conceptual truth layer, reconciling how different domains, systems, and analytical communities understand the same data. Together, Gold and Platinum bridge operational use, analytical insight, and long-lived domain semantics, enabling clarity, velocity, and governed understanding at scale.

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Databricks vs Snowflake vs Microsoft Fabric: Positioning the Future of Enterprise Data Platforms

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This article extends the Databricks vs Snowflake comparison to include Microsoft Fabric, exploring the platforms’ philosophical roots, architectural approaches, and strategic trade-offs. It positions Fabric not as a direct competitor but as a consolidation play for Microsoft-centric organisations, and introduces Microsoft Purview as the governance layer that unifies divergent estates. Drawing on real enterprise patterns where Databricks underpins engineering, Fabric drives BI adoption, and functional teams risk fragmentation, the piece outlines the “Build–Consume–Govern” model and a phased transition plan. The conclusion emphasises orchestration across platforms, not choosing a single winner, as the path to a governed, AI-ready data estate.

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Databricks vs Snowflake: A Critical Comparison of Modern Data Platforms

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This article provides a critical, side-by-side comparison of Databricks and Snowflake, drawing on real-world experience leading enterprise data platform teams. It covers their origins, architecture, programming language support, workload fit, operational complexity, governance, AI capabilities, and ecosystem maturity. The guide helps architects and data leaders understand the philosophical and technical trade-offs, whether prioritising AI-native flexibility and open-source alignment with Databricks or streamlined governance and SQL-first simplicity with Snowflake. Practical recommendations, strategic considerations, and guidance by team persona equip readers to choose or combine these platforms to align with their data strategy and talent strengths.

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