Our library of practical wisdom from the front lines of data and risk
This page is our knowledge centre of field-tested insights, frameworks, and technical guides, drawn from our experience building and scaling production-grade decisioning systems. We believe in sharing our expertise openly.
Below are the core methodologies and best practices that inform every client engagement.
Data Strategy & Governance
A successful data programme is built on a solid strategic foundation. Technology is only an enabler; the real work lies in defining the business case, establishing robust governance, and ensuring the organisation can trust its data.
The 90-Day Data Transformation Roadmap
We deliver value incrementally to build momentum and ensure alignment. Our standard engagement follows a proven, week-by-week pattern.
Assessment & Quick Wins
(Weeks 1-2)
Activities
We conduct stakeholder interviews across business, risk, and technology to map critical decision pathways and pain points. This is followed by a rapid assessment of your current data landscape and architecture.
Deliverable
A high-impact dashboard (e.g., automating a critical Excel report) is delivered to prove value immediately. A prioritised 90-day execution roadmap is presented, outlining the path to the target state.
Foundation Building
(Weeks 3-6)
Assessment & Quick Wins
We implement the core data warehouse schema (e.g., star schema), build robust data pipelines for 2-3 critical sources, and deploy an automated data quality rules engine.
Activities
A single source of truth for the initial scope is established. The core semantic layer is live, providing a governed foundation for business intelligence.
Scale & Adoption
(Weeks 7-12)
Assessment & Quick Wins
We accelerate the onboarding of more data sources, enable self-service analytics for pilot user groups with comprehensive training, and execute a change management programme to drive adoption.
Activities
The platform is scaled, key users are trained and empowered, and the solution is demonstrably adopted by the business to make decisions.
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The Business Case for Data Quality
Poor data quality is not an IT problem; it is a business problem with a direct financial impact. Based on our analysis across over 50 implementations, every $1 invested in proactive data quality prevention saves an average of $10 in remediation costs, opportunity loss, and regulatory fines down the line. A robust DQ framework is not a cost centre; it is a value driver that unlocks reliable analytics, AI, and regulatory compliance. Our approach focuses on automated, preventative controls built directly into your data pipelines, not manual, reactive clean-up.
Practical Guide to Data Privacy Compliance
Data privacy regulations (like GDPR, PDPL, NDMO) are a baseline requirement for managing data. Our practical compliance framework is built on four key pillars that we embed into your data architecture:
Lawful Basis
Establishing and documenting a lawful basis for all data processing activities.
Data Subject Rights
Building automated, auditable workflows to handle data access, deletion, and portability requests within regulatory timelines.
Privacy by Design
Embedding principles like data minimisation and purpose limitation directly into your data models and pipelines.
Robust Security
Implementing technical measures such as encryption, dynamic data masking, and attribute-based access controls.
Modern Data Engineering
Strategy is meaningless without execution. We build robust, scalable data platforms using best-in-class tools and methodologies from the modern data stack.
Best Practices and Project Structure
The principle of separating data transformation logic is key to building trust and maintainability. In our dbt projects, we enforce a clear, three-tiered structure.
models/
├── staging/
# 1:1 with source, for light cleaning, renaming, and type casting. Provides a durable, idempotent base.
├── intermediate/
# Complex business logic is modularised here. Not exposed to end users.
└── marts/
# Final, aggregated models for consumption by BI tools. These are the "products" of the data team.
Our key principles for dbt:
Test Everything
We combine generic tests (e.g., `not_null`, `unique`) with singular, custom tests that encode critical business logi (e.g.,`assert_total_revenue_is_positive`), ensuring data is not just present, but correct.
Document Everything
We enforce 100% documentation coverage for all models and columns, which feeds a living, searchable data catalogue for the entire organisation.
Favour Modularity
We break down complex business logic into smaller, reusable intermediate models. This improves readability, speeds up development, and makes testing more granular and effective.
Power BI Performance Optimisation
The principle that fast dashboards are a sign of a well-designed data model is central to our BI work. Our approach to building high-performance Power BI solutions follows ten core commandments.
1. Use measures, not calculated columns
Measures calculate at query time on aggregated data; calculated columns bloat model size by storing values for every row, slowing down performance.
2. Embrace the star schema
It is the most performant structure for BI tools, avoiding the complex, multi-hop joins of normalised schemas.
3. Use incremental refresh
Configure incremental refresh for large fact tables to minimise refresh times and resource consumption.
4. Enforce query folding
Where possible, push transformations back to the source database during Power Query steps.
5. Create aggregation tables
Dramatically speed up high-level dashboards by pre-calculating summary-level data.
6. Implement RLS via roles
Use the built-in Row-Level Security engine, which is more secure and performant than applying complex DAX filters in each measure.
7. Limit visuals per page
Each visual fires at least one query. We aim for fewer than 8 visuals to ensure a responsive user experience.
8. Default to Import mode
Use DirectQuery sparingly and only when absolutely necessary for near real-time data needs, as it is significantly less performant.
9. Use variables in complex DAX
Store intermediate calculations in variables to improve readability, debugging, and query performance.
10. Profile performance with DAX Studio
We don't guess where bottlenecks are; we use external tools to analyse query plans and identify the root cause of slow performance.
Architecting for Real-Time Decisions with Event Sourcing
The principle of capturing every business event allows for both real-time insights and perfect auditability. For use cases like fraud detection or instant credit approvals, we implement event sourcing.
Key architectural components we implement:
Immutable Event Logs
We design immutable events for core business domains (e.g., `TransactionOccurred`, `CreditApplicationSubmitted`), creating a permanent, auditable record of every action.
Scalable Stream Processing
We use tools like Kafka or Kinesis with appropriate stream processing patterns (e.g., Flink, Spark Streaming) to handle thousands of events per second, feeding real-time feature stores and decision engines.
Time-Travel Queries
We enable simplified auditing and debugging by building the capability to reconstruct the state of any entity (e.g., a customer's risk profile) at any point in time—a critical requirement for regulatory inquiries.
Machine Learning & MLOps
A model in a notebook provides zero business value. Our focus is on MLOps — the discipline of reliably and efficiently getting models into production and keeping them there.
Playbook for Shipping Challenger Models to Production
Our 8-week framework is designed to get high-performing, compliant models into production quickly and safely.
Data & Feature Foundation
(Weeks 1-2)
A systematic process to develop hundreds of predictive features from traditional and alternative data sources, creating the rich signal needed for modern modelling.
Rigorous Model Development & Validation
(Weeks 3-4)
We train multiple algorithms (from logistic regression for baseline explainability to XGBoost for performance) and conduct rigorous statistical, business, and regulatory validation.
Compliant Deployment & A/B Testing
(Weeks 5-6)
We deploy models as containerised APIs within a robust champion/challenger framework that allows for live A/B testing to prove performance uplift before a full rollout.
Continuous Monitoring & Governance
(Weeks 7-8)
We implement a critical monitoring setup for data drift, concept drift, and silent model failures to ensure performance never degrades unnoticed and results are auditable.
Our Feature Store Implementation Checklist
The principle behind a feature store is to decouple feature generation from model training, ensuring consistency between training and serving. Our implementation checklist ensures it gets used.
Pre-Implementation
We ensure use cases are prioritised, features are defined in a shared registry, and the team is trained on core concepts before any technology is chosen.
Technical Setup
We configure the online store (e.g., Redis) for low-latency serving and the offline store (e.g., Parquet/Delta Lake) for model training, along with monitoring and CI/CD pipelines for feature backfills and updates.
Production Readiness
The system must pass performance benchmarks (e.g., P99 latency <50ms) and automated failover procedure tests before going live.
Credit Risk & Decisioning
This is our deepest area of domain expertise. We apply the principles of data engineering and MLOps to the specific, high-stakes challenges of credit and financial risk management.
Playbook for Scaling a Modern Credit Bureau
Scaling a credit bureau is not merely a technical challenge — it's an ecosystem transformation. Success hinges on navigating the complex interplay of regulation, data sharing incentives, and commercial strategy.
Our key principles for success:
It's a data-sharing problem, not a tech problem
The foundation of a successful bureau is a fair and valuable data reciprocity model that incentivises all participants to contribute high-quality data.
Data quality is non-negotiable
We implement an automated DQ framework with over 200 validation rules from day one to achieve and maintain a 95%+ data quality index. Trust in the data is paramount.
Iterate from compliance to prediction
The journey starts with delivering essential regulatory and compliance reporting. Once the data foundation is trusted, we progress to simpler predictive models, and finally to high-performance ML scores (Gini >0.85).
The Future of Credit Decisioning is Dynamic
Credit scoring is evolving from static, periodic models to dynamic, real-time contextual decisions.
Key trends we help clients implement include:
Continuous Learning Models
Building the MLOps pipelines required to safely and automatically retrain models on new data, allowing them to adapt to changing market conditions.
Alternative Data Integration
Moving beyond traditional bureau data to incorporate signals from behavioural biometrics, network graph features, and real-time transaction patterns.
Explainable AI (XAI)
Implementing techniques like SHAP (SHapley Additive exPlanations) to ensure that even complex models can be explained to customers and regulators, a growing legal requirement.
Put our expertise to work
These frameworks and insights are the starting point for our client engagements. They represent the depth of expertise we bring to every project.
If you are facing challenges in data, risk, or AI/ML, the next step is a complimentary discovery call to discuss how these principles can be applied to solve your specific problems.
Explore our solutions in depth