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Solution · Data Modernisation

A Modern Data Foundation for Analytics and AI.

Consolidate data from across the business into a governed cloud platform with reliable pipelines, shared metrics and the quality needed for machine learning and AI.
Discuss Data ModernisationOur approach
  • Unified, trusted data
  • Self-service analytics
  • Real-time where it matters
  • AI-ready foundation
CRMERPProduct eventsSaaS appsWAREHOUSE / LAKEHOUSEBI dashboardsML featuresAI retrieval

The business challenge

Why this is harder than it looks.

Legacy warehouses, nightly batch jobs and spreadsheet-based reporting slow decisions and block AI initiatives. Modernisation is as much about governance and trust as technology.

  • Siloed sources

    Data spread across ERP, CRM, product and finance systems.

  • Conflicting metrics

    Teams calculate the same KPI in different ways.

  • Legacy limits

    On-premises warehouses struggle with scale and new data types.

  • Limited governance

    Unclear ownership, lineage and access rules.

Technical approach

How we approach it.

  1. 1

    Data strategy

    Align data priorities to business decisions and AI ambitions.

  2. 2

    Platform design

    Choose warehouse or lakehouse architecture with open formats.

  3. 3

    Pipeline engineering

    Tested, version-controlled ingestion and transformation.

  4. 4

    Semantic layer

    Shared, documented business metrics.

  5. 5

    Governance

    Ownership, cataloguing, lineage, access control and quality monitoring.

Architecture concepts

A layered reference architecture.

Every engagement is tailored, but these layers form the backbone of a well-engineered solution.
  1. SourcesOperational databases, SaaS applications, files and event streams.
  2. IngestionBatch connectors and change data capture or streaming.
  3. StorageCloud warehouse or lakehouse with open table formats.
  4. TransformationLayered models with dbt or Spark and automated tests.
  5. ConsumptionBI dashboards, data products, ML features and AI retrieval.

Relevant services

The capabilities behind this solution.

AI & Data

Data Engineering

Reliable pipelines, modern warehouses and real-time streaming that turn raw data into trusted decisions.

  • BigQuery
  • Snowflake
  • Amazon Redshift

AI & Data

Artificial Intelligence

Generative AI, intelligent agents and machine learning engineered into real business workflows — securely and measurably.

  • OpenAI API
  • Anthropic Claude
  • Google Gemini

Cloud & Infrastructure

Cloud Computing

Design, migrate and operate secure, cost-efficient cloud environments across AWS, Google Cloud and Microsoft Azure.

  • AWS
  • Google Cloud
  • Microsoft Azure

Business benefits

The outcomes you can expect.

  • Trusted reporting

    Consistent metrics across every team and dashboard.

  • Faster insight

    Fresh data available when decisions are made.

  • Lower cost

    Managed, elastic platforms replace legacy hardware and licences.

  • AI enablement

    Clean, governed data accelerates ML and generative AI.

Delivery methodology

A phased, low-risk path.

Indicative durations — actual timelines are agreed after discovery.

Related technologies

  • BigQuery
  • Snowflake
  • Databricks
  • dbt
  • Apache Airflow
  • Apache Kafka
  • Looker
  • Power BI
  1. 01

    Assess

    2–4 weeks

    Current state, priority use cases and target architecture.

  2. 02

    Foundation

    6–10 weeks

    Platform, governance and first priority data domain.

  3. 03

    Expand

    Iterative

    Additional domains, migration of legacy reports.

  4. 04

    Optimise

    Ongoing

    Performance, cost and self-service enablement.

FAQs

Common questions.

Relevant industries

Yes. We run old and new platforms in parallel and reconcile results before decommissioning legacy reports.

Many organisations benefit from a lakehouse that combines both. We recommend an architecture based on your data types, users and workloads.

Let's plan your data modernisation.

A short consultation is the fastest way to understand scope, risks and a realistic path forward.

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