AI, Data & Emerging Tech
Data Science & Analytics
Trustworthy pipelines, a warehouse people actually query, and analysis that changes a decision. We fix the plumbing before we build the dashboard.
Overview
Numbers your leadership team will act on
Part of our AI, Data Science & Emerging Tech practice.
Dashboards get abandoned when two reports disagree and nobody can say which is right. Once trust is gone it takes far longer to rebuild than it took to lose.
So we start with definitions and lineage — what a metric means, where it comes from, when it last refreshed — and only then build the reporting layer on top.
Capabilities
What we deliver
Data platform & warehouse
Lakehouse and warehouse design on Snowflake, BigQuery, Databricks or Synapse, with dimensional modelling and a semantic layer teams can self-serve from.
Pipelines & integration
Batch and streaming ingestion from ERP, MES, CRM, telemetry and third-party APIs, orchestrated with Airflow or Dagster and transformed with dbt.
Business intelligence
Power BI, Tableau and Looker reporting designed around the decisions being made, tuned so reports load in seconds rather than minutes.
Predictive analytics
Forecasting, churn and cohort analysis, pricing and margin modelling, experiment design and causal inference.
Industrial & IoT analytics
Time-series pipelines for sensor and machine data, OEE and downtime analysis, quality correlation and predictive maintenance signals.
Governance & quality
Data contracts, automated quality tests, catalogue and lineage, access control, and GDPR-aligned retention and masking.
How we work
A predictable path from first call to handover
No two engagements are identical, but the shape is consistent — so you always know what happens next and what you get at the end of each stage.
Engagement modelsMap the questions
We start from the decisions and the questions behind them, then work backwards to the data required.
Fix the foundation
Ingestion, modelling, quality tests and lineage — the unglamorous work that makes everything after it reliable.
Deliver insight
Reports, models and analysis delivered in increments, each tied to a question from step one.
Enable the team
Documentation, metric definitions and training so your analysts extend the platform without us.
Outcomes
What changes for you
- One agreed definition per metric, documented and enforced in code.
- Pipelines that alert when they break, instead of quietly serving stale numbers.
- Reports that load fast enough to be used live in a meeting.
- Analysts who can add a model themselves rather than raising a ticket.
Tools & standards
What we work with
- Snowflake
- Databricks
- BigQuery
- Azure Synapse
- dbt
- Apache Airflow
- Dagster
- Kafka
- Spark
- Python
- pandas
- Power BI
- Tableau
- Looker
- Great Expectations
- PostgreSQL
Questions
Frequently asked
Do we need a warehouse, or can we report straight from our systems?
For a single system with modest volume, direct reporting is often fine and we will say so. A warehouse earns its cost once you need to join across systems or keep history.
Can you work with our existing BI tool?
Yes. We work in whatever your teams have already adopted rather than pushing a migration you did not ask for.
How do you handle sensitive data?
Role-based access, column masking, environment separation and retention rules, designed against GDPR and your internal policy from the start.
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ExploreLet’s scope your next build.
Tell us what you are trying to ship. We will come back with an approach, a team shape and an honest estimate — we aim to reply within two working days.