Tag Archives: analytics

East/West vs North/South Promotion Lifecycles: How Modern Financial Services Data Platforms Support Operational Stability and Analytical Freedom Simultaneously

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This article argues that modern Financial Services (FS) data platforms must deliberately support two distinct but complementary promotion lifecycles. The well known and understood North/South lifecycle provides operational stability, governance, and regulatory safety for customer-facing and auditor-visible systems. In parallel, the East/West lifecycle enables analytical exploration, experimentation, and rapid innovation for data science and analytics teams. By mapping these lifecycles onto layered data architectures (Bronze to Platinum) and introducing clear promotion gates, FS organisations can protect operational integrity while sustaining analytical freedom and innovation.

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Consumers of a Financial Services Data Platform: Who They Are, What They Need, and How Modern Architecture Must Support Them

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This article examines who consumes a modern Financial Services data platform and why their differing needs must shape its architecture. It identifies four core consumer groups, operational systems, analytics communities, finance and reconciliation functions, and governance and regulators, alongside additional emerging consumers. By analysing how each group interacts with data, the article explains why layered architectures, dual promotion flows, and semantic alignment are essential. Ultimately, it argues that platform value is defined by consumption, not ingestion or technology choices.

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Using SCD2 in the Bronze Layer with a Non-SCD2 Silver Layer: A Modern Data Architecture Pattern for UK Financial Services

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UK Financial Services firms increasingly implement SCD2 history in the Bronze layer while providing simplified, non-SCD2 current-state views in the Silver layer. This pattern preserves full historical auditability for FCA/PRA compliance and regulatory forensics, while delivering cleaner, faster, easier-to-use datasets for analytics, BI, and data science. It separates “truth” from “insight,” improves governance, supports Data Mesh models, reduces duplicated logic, and enables deterministic rebuilds across the lakehouse. In regulated UK Financial Services today, it is the only pattern I have seen that satisfies the full, real-world constraint set with no material trade-offs.

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WTF Is SCD? A Practical Guide to Slowly Changing Dimensions

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Slowly Changing Dimensions (SCDs) are how data systems manage attributes that evolve without constantly rewriting history. They determine whether you keep only the latest value, preserve full historical versions, or maintain a limited snapshot of changes. The classic SCD types (0–3, plus hybrids) define different behaviours… from never updating values, to overwriting them, to keeping every version with timestamps. The real purpose of SCDs is to make an explicit choice about how truth should behave in your analytics: what should remain fixed, what should update, and what historical context matters. Modern data platforms make tracking changes easy, but they don’t make the design decisions for you. SCDs are ultimately the backbone of reliable, temporal, reality-preserving analytics.

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