Data Science, Analytics & Cloud
From Raw Data to Confident Decisions: Building Data Systems That Earn Trust
Most enterprises are not data-poor. They are insight-poor. The data exists, but it is fragmented, inconsistent, ungoverned, and invisible to the people who need it. The solution is not more data — it is better architecture.
The Modern Data Stack: What Each Layer Does
The modern data stack has four layers, and understanding each one prevents the most common architectural mistakes.
Ingestion: data arrives from source systems — SaaS products, databases, event streams, APIs, files. Tools like Fivetran, Airbyte, and Kafka handle this layer. The key design decision is latency: batch (acceptable 24-hour lag) vs. streaming (minutes or seconds). Most analytics use cases run happily on batch; real-time fraud detection, dynamic pricing, and live dashboards require streaming.
Storage and transformation: data lands in a cloud data warehouse — Snowflake, BigQuery, or Databricks — and is transformed into analytics-ready models. dbt has become the standard transformation layer, turning SQL scripts into a governed, tested, documented pipeline. The warehouse is not a dumping ground; it is a product.
Serving: transformed data is exposed through dashboards (Tableau, Power BI, Looker), APIs, reverse ETL back to operational systems (Census, Hightouch), or directly consumed by ML models. The serving layer is where business value is realised.
Governance: lineage, cataloguing, access control, and data quality checks. This layer is the least glamorous and the most often skipped — until a compliance audit, a data breach, or a wrong number in a board presentation makes it urgent.
AI/ML in Production: What the Tutorials Skip
Training a model is the easy part. Deploying it, monitoring it, and keeping it accurate over time is where most ML projects stall.
Feature engineering determines model quality more than algorithm choice. Good feature engineering requires domain expertise: knowing which signals are causal vs. correlated, which features will be available at inference time (not just at training time), and which features will drift as the world changes.
Model serving requires infrastructure decisions: batch prediction (cheaper, higher latency), online prediction (expensive, low latency), or streaming prediction. Most use cases that claim to need real-time inference are actually fine with batch — with significant cost savings.
Model monitoring is non-negotiable. Models degrade silently. Data drift, concept drift, and infrastructure changes can all cause a model to quietly underperform without surfacing an obvious error. A monitoring pipeline that tracks prediction distributions, feature distributions, and business outcome metrics is as important as the model itself.
Cloud Platforms: Azure, AWS, GCP — How to Choose
The honest answer is that for most analytics and AI workloads, all three major platforms are technically capable. The decision is driven by existing enterprise relationships, data gravity, and team expertise.
Azure wins in Microsoft-centric enterprises: tight integration with M365, Active Directory, Dynamics, and a strong enterprise sales relationship. Azure Synapse, Azure ML, and Fabric are mature enterprise offerings.
AWS offers the broadest service catalogue and the deepest open-source integration. If your team already runs infrastructure on AWS, keeping analytics on the same platform eliminates cross-cloud data egress costs and simplifies IAM.
Google Cloud / BigQuery is the strongest pure-analytics platform at scale. BigQuery's serverless model, native ML integration (BQML), and Vertex AI pipeline make it uniquely competitive for data-heavy organisations that are not already locked into another cloud.
For greenfield data platforms, we recommend BigQuery or Snowflake on the warehouse layer — both have excellent cost models at scale and strong ecosystem tooling.
