Tag Archives: Regulatory Compliance

Aligning the Data Platform to Enterprise Data & AI Strategy

This article establishes the data platform as the execution engine of Enterprise Data & AI Strategy in Financial Services. It bridges executive strategy and technical delivery by showing how layered architecture (Bronze, Silver, Gold, Platinum), embedded governance, dual promotion lifecycles (North/South and East/West), and domain-aligned operating models turn strategic pillars, architecture & quality, governance, security & privacy, process & tools, and people & culture, into repeatable, regulator-ready outcomes. The result is a platform that delivers control, velocity, semantic alignment, and safe AI enablement at scale.

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Foundational Architecture Decisions in a Financial Services Data Platform

This article defines a comprehensive architectural doctrine for modern Financial Services data platforms, separating precursor decisions (what must be true for trust and scale) from foundational decisions (how the platform behaves under regulation, time, and organisational pressure). It explains why ingestion maximalism, streaming-first eventual consistency, transactional processing at the edge, domain-first design, and freshness as a business contract are non-negotiable in FS. Through detailed narrative and explicit anti-patterns, it shows how these decisions preserve optionality, enable regulatory defensibility, support diverse communities, and prevent the systemic failure modes that quietly undermine large-scale financial data platforms.

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

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

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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Managing a Rapidly Growing SCD2 Bronze Layer on Snowflake: Best Practices and Architectural Guidance

Slowly Changing Dimension Type 2 (SCD2) patterns are widely used in Snowflake-based Financial Services platforms to preserve full historical change for regulatory, analytical, and audit purposes. However, Snowflake’s architecture differs fundamentally from file-oriented lakehouse systems, requiring distinct design and operational choices. This article provides practical, production-focused guidance for operating large-scale SCD2 Bronze layers on Snowflake. It explains how to use Streams, Tasks, micro-partition behaviour, batching strategies, and cost-aware configuration to ensure predictable performance, controlled spend, and long-term readiness for analytics and AI workloads in regulated environments.

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Governance & Metadata That Actually Survive a PRA Review

Turning catalogues, glossaries, and lineage tools into something regulators will actually trust. Most Financial Services organisations invest heavily in data catalogues, glossaries, and lineage tools: yet still struggle to satisfy regulators when asked to explain where data comes from, what it means, and how decisions were made over time. This article focuses on governance and metadata that actually survive Prudential Regulation Authority’s (PRA) and Financial Conduct Authority (FCA) scrutiny. It sets out a practical model for connecting semantics, lineage, rules, and accountability, so regulated firms can move beyond decorative governance and confidently explain historical outcomes with evidence, not anecdotes.

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Production-Grade Testing for SCD2 & Temporal Pipelines

The testing discipline that prevents regulatory failure, data corruption, and sleepless nights in Financial Services. Slowly Changing Dimension Type 2 pipelines underpin regulatory reporting, remediation, risk models, and point-in-time evidence across Financial Services — yet most are effectively untested. As data platforms adopt CDC, hybrid SCD2 patterns, and large-scale reprocessing, silent temporal defects become both more likely and harder to detect. This article sets out a production-grade testing discipline for SCD2 and temporal pipelines, focused on determinism, late data, precedence, replay, and PIT reconstruction. The goal is simple: prevent silent corruption and ensure SCD2 outputs remain defensible under regulatory scrutiny.

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Event-Driven CDC to Correct SCD2 Bronze in 2025–2026

Broken history often stays hidden until remediation or skilled-person reviews. Why? Event-driven Change Data Capture fundamentally changes how history behaves in a data platform. When Financial Services organisations move from batch ingestion to streaming CDC, long-standing SCD2 assumptions quietly break — often without immediate symptoms. Late, duplicated, partial, or out-of-order events can silently corrupt Bronze history and undermine regulatory confidence. This article sets out what “correct” SCD2 means in a streaming world, why most implementations fail, and how to design Bronze pipelines that remain temporally accurate, replayable, and defensible under PRA/FCA scrutiny in 2025–2026.

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Entity Resolution & Matching at Scale on the Bronze Layer

Entity resolution has become one of the hardest unsolved problems in modern UK Financial Services data platforms. This article sets out a Bronze-layer–anchored approach to resolving customers, accounts, and parties at scale using SCD2 as the temporal backbone. It explains how deterministic, fuzzy, and probabilistic matching techniques combine with blocking, clustering, and survivorship to produce persistent, auditable entity identities. By treating entity resolution as platform infrastructure rather than an application feature, firms can build defensible Customer 360 views, support point-in-time reconstruction, and meet growing FCA and PRA expectations.

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Handling Embedded XML/JSON Blobs to Audit-Grade SCD2 Bronze

Financial Services platforms routinely ingest XML and JSON embedded in opaque fields, creating tension between audit fidelity and analytical usability. This article presents a regulator-defensible approach to handling such payloads in the Bronze layer: landing raw data immutably, extracting only high-value attributes, applying attribute-level SCD2, and managing schema drift without data loss. Using hybrid flattening, temporal compaction, and disciplined lineage, banks can transform messy blobs into audit-grade Bronze assets while preserving point-in-time reconstruction and regulatory confidence.

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Operationalising SCD2 at Scale: Monitoring, Cost Controls, and Governance for a Healthy Bronze Layer

This article explains how to operationalise Slowly Changing Dimension Type 2 (SCD2) at scale in the Bronze layer of a medallion architecture, with a focus on highly regulated Financial Services environments. It outlines three critical pillars: monitoring, cost control, and governance, needed to keep historical data trustworthy, performant, and compliant. By tracking growth patterns, preventing meaningless updates, controlling storage and compute costs, and enforcing clear governance, organisations can ensure their Bronze layer remains a reliable audit-grade historical asset rather than an unmanaged data swamp.

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Advanced SCD2 Optimisation Techniques for Mature Data Platforms

Advanced SCD2 optimisation techniques are essential for mature Financial Services data platforms, where historical accuracy, regulatory traceability, and scale demands exceed the limits of basic SCD2 patterns. Attribute-level SCD2 significantly reduces storage and computation by tracking changes per column rather than per row. Hybrid SCD2 pipelines, combining lightweight delta logs with periodic MERGEs into the main Bronze table, minimise write amplification and improve reliability. Hash-based and probabilistic change detection eliminate unnecessary updates and accelerate temporal comparison at scale. Together, these techniques enable high-performance, audit-grade SCD2 in platforms such as Databricks, Snowflake, BigQuery, Iceberg, and Hudi, supporting the long-term data lineage and reconstruction needs of regulated UK Financial Services institutions.

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Scaling the SCD2 Bronze Layer: Practical Strategies for Financial Services

A rapidly growing SCD2 Bronze layer is an expected outcome of implementing full historical tracking across financial services data platforms, where high-frequency attribute changes, noisy upstream systems, and long regulatory retention periods contribute to rapid data growth. This article outlines practical engineering strategies to keep SCD2 Bronze efficient and cost-effective, including effective partitioning, change-suppression logic, hot–warm–cold tiering, compaction and lifecycle optimisation, windowed processing, metadata offloading, and upstream data contracts. Advanced approaches, such as attribute-level SCD2, hybrid SCD2/delta-merge patterns, and hash-based change detection, further enhance scalability for mature platforms. The piece concludes that SCD2 growth is not a failure but a natural result of robust governance. With the right architecture and operational discipline, organisations can maintain an auditable, scalable, and regulator-aligned Bronze layer ready for analytics, AI, and Data Mesh.

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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

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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Supply Chains: Still Vulnerable – According To Latest McKinsey Research

This article delves into McKinsey’s latest research, which paints a stark picture of the vulnerabilities still plaguing global supply chains in 2024. Despite progress since the pandemic, companies face persistent risks, from geopolitical tensions to talent shortages. By critiquing McKinsey’s findings and offering actionable insights, this piece explores how organizations can enhance resilience through digitization, talent development, and boardroom advocacy.

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