Data

Our vision of data

Data is the fuel of artificial intelligence and the foundation of informed decision-making. We treat it not as a by-product of applications but as a first-order strategic asset, designed, governed and operated with the same rigour as application code.

Our conviction: without a Digital Twin — a faithful, up-to-date digital representation of the organisation — AI agents are blind. Structured data, rich metadata and semantic models are absolute prerequisites for the Agentic Enterprise. The paradox of rigour applies in full: AI automation demands more human discipline in data management, not less.

Data engineering and Data Mesh

Our data engineers build robust, scalable, observable pipelines across every paradigm — batch, micro-batch, real-time streaming and event-driven — on Airflow, Spark, dbt, BigQuery, Kafka and Dataflow. We establish formal data contracts between producers and consumers, with versioned schemas, freshness and quality SLAs, and automated conformance tests.

Where it fits, we help organisations adopt Data Mesh pragmatically — domain ownership, data as a product, a self-serve platform and federated governance — through a progressive, domain-by-domain migration rather than a big-bang rollout. The goal is not architectural purity but operational efficiency: reliable, accessible, usable data, faster.

Digital Twin and lakehouse

We build the Digital Twin of the organisation — its processes, data, relationships and business rules — through a structured method: Reverse Conway Maneuver, data modelling, Data Shift Left and a metadata catalogue (DataHub, Collibra). On top sits a semantic layer that lets AI agents and business users query data in natural language with consistent definitions.

Our lakehouse architectures combine the flexibility of the data lake with the governance of the warehouse, deployed on Databricks, BigQuery/BigLake or Snowflake. We stay format-agnostic — favouring open formats (Iceberg, Delta Lake, Hudi) to avoid lock-in — and organise data through the Medallion pattern (Bronze, Silver, Gold) with automated quality gates at every transition.

Governance, quality and analytics

Data governance is a continuous discipline, not a one-off project: data ownership, codified policies, automated lineage and regulatory compliance (GDPR, HDS, DORA). We run data-quality programmes across the six fundamental dimensions — accuracy, completeness, consistency, freshness, uniqueness and validity — measured continuously with Data Quality Scores that trigger automatic investigation when they fall below threshold.

On the analytics side, we design self-service BI platforms (Looker, Tableau, Power BI, Metabase), generative BI that queries data in natural language, and real-time streaming analytics for fraud detection, operational monitoring and live personalisation.

Ready to make data a strategic asset? Get in touch.