Data engineering

Data Analytics & BI

When two departments arrive at a meeting with different revenue figures, the problem is rarely the spreadsheet. It is that no one agreed what the metric means or where it comes from. We fix the definition and the pipeline, then build the dashboard.

PostgreSQL BigQuery Snowflake dbt Airflow Power BI Looker Studio Metabase

Data work fails predictably: reports assembled by hand each month, definitions that differ by department, dashboards that go stale, and a single analyst who is the only person able to answer a question. The cost shows up as decisions made late, or on instinct.

We build the unglamorous layer that makes reporting trustworthy — reliable ingestion, a modelled warehouse, tested transformations and documented metric definitions — and then the dashboards on top, which are the easy part once the foundation is right.

Capabilities

What Data Analytics & BI covers

One version of the numbers, current enough to act on.

Data warehouse design

A modelled warehouse on PostgreSQL, BigQuery, Snowflake or Redshift, structured for the questions your business asks repeatedly.

ETL & ELT pipelines

Scheduled, monitored, restartable pipelines pulling from your applications, databases, SaaS tools and partner feeds.

BI dashboards

Power BI, Looker Studio, Metabase or custom dashboards designed for decisions rather than for the density of the chart grid.

Metric definitions

A governed metric layer where every number has one written definition and one calculation, agreed across departments.

Self-service analytics

Governed datasets and training so managers can answer their own questions without joining the analyst's queue.

Data quality monitoring

Automated tests on freshness, volume, uniqueness and referential integrity, with alerts before a stale number reaches a board pack.

Why it works

What you get that you might not expect.

These are the commitments clients tell us mattered most once the project was underway.

Numbers that reconcile

Warehouse figures tied back to source systems and validated, so finance and operations stop arguing about whose report is right.

Reporting time reclaimed

Manual monthly assembly replaced by pipelines — typically several days of skilled time returned every month.

Alerting, not just charts

Thresholds and anomaly alerts that reach the right person, rather than dashboards nobody remembers to open.

Documented and portable

Version-controlled transformations and documented models your own analysts can extend without reverse-engineering.

How we deliver

Our data & bi process

Every stage produces something you can look at, use or disagree with. Nothing is invisible until the end.

  1. 1

    Decision mapping

    Which decisions need better information, who makes them, how often, and what evidence would change them.

  2. 2

    Source audit

    Every system holding relevant data, its quality, its refresh cadence and how it can be accessed reliably.

  3. 3

    Model & build

    Warehouse schema, transformation layer with tests, and orchestration with retry and alerting behaviour.

  4. 4

    Visualise

    Dashboards designed to answer the mapped decisions, reviewed with the people who will actually use them.

  5. 5

    Enable & maintain

    Training, documentation and a maintenance agreement covering pipeline health and new data sources.

Technologies we use for this

Chosen per project rather than by house policy. We will explain the trade-off in plain terms before anything is decided.

PostgreSQL BigQuery Snowflake dbt Airflow Power BI Looker Studio Metabase Python Pandas

Questions

Data Analytics & BI — your questions

Not covered here?

Ask us directly

Reporting straight off a production database is fine while you have one system and light query load. You need a warehouse once you are joining across systems, need historical snapshots, or reporting queries start affecting application performance.

Power BI where Microsoft licensing already exists; Looker Studio for lightweight needs on Google Cloud; Metabase when you want self-hosted and inexpensive; a custom dashboard when it must sit inside your own product. The warehouse underneath matters more than the tool on top.

Anything from near real-time streaming to a nightly batch. Most businesses find hourly or daily is sufficient and considerably cheaper — we scope refresh frequency to actual decision cadence.

Yes. We commonly keep the reports people already trust and rebuild what feeds them, which is far less disruptive than replacing everything at once.

The first working dashboard on a real pipeline is usually live within four to six weeks, covering the highest-value decision area first.

Thinking about data analytics & bi?

Tell us the problem. You will get a rough cost, a rough timeline and an honest view on whether it is worth building — before anyone talks about a contract.