Data engineering and business intelligence

Most businesses do not have a reporting problem; they have a reconciliation problem. Sales reports one revenue figure, finance reports another, and marketing reports a third, because each is counting something slightly different from a different system. Meetings are spent establishing which number is right rather than deciding what to do about it.

Data engineering is the work of making that argument unnecessary: one pipeline, one set of definitions, and a dashboard everyone reads the same way.

Why this is worth doing properly

The tooling has become genuinely accessible. Managed warehouses and transformation frameworks mean a mid-sized business can now run the kind of data stack that required a dedicated team a decade ago, and the barrier has moved from technology to definitions.

Definitions are where these projects actually fail. If "active customer" means something different in the CRM and the billing system, no amount of pipeline engineering reconciles them — somebody has to decide, write it down and have the authority to make it stick. That is a governance problem wearing a technical costume.

The other common failure is building a warehouse before there is a question. A pipeline that centralises everything without a decision it serves produces a large, expensive dataset nobody queries. Starting from the decision and working backwards to the data is slower to look impressive and much more likely to be used.

Where this work usually goes wrong

Centralising before defining

Moving every system’s data into one place does not resolve conflicting definitions; it puts them next to each other. Agree the metric definitions first, with a named owner for each.

Dashboards with fifty widgets

A dashboard nobody can read in ninety seconds is a dashboard nobody reads. Each view should serve a specific decision and omit anything that does not inform it.

No data quality checks

Pipelines fail quietly — a source changes a field, a job half-completes, and the dashboard shows a plausible wrong number. Freshness, row-count and null checks with alerting are not optional extras.

Transformation logic scattered everywhere

Business logic split between the pipeline, the warehouse and the BI tool guarantees that the same metric is computed three different ways. It belongs in one layer, in version control, under review.

Reporting on vanity metrics

Cumulative totals that only ever rise are reassuring and useless. Rates, cohorts and comparisons against a baseline are what actually indicate whether anything changed.

What this covers

Data collection systems

Reliable capture from applications, third-party APIs and manual sources, with provenance retained.

Data pipeline development

Scheduled and event-driven pipelines with retries, idempotency and failure alerting.

Data cleaning and transformation

Deduplication, normalisation and business logic applied in one place under version control.

Database design

Schemas that serve the queries you actually run rather than mirroring the source systems.

PostgreSQL implementation

Operational and analytical Postgres, tuned and indexed for the workload it carries.

Data warehouse integrations

BigQuery, Snowflake or Postgres-based warehousing, sized to your data rather than to a vendor tier.

Automated reporting

Scheduled reports assembled and distributed without anyone exporting a file.

Business intelligence dashboards

Views built around specific decisions, with definitions documented on the dashboard itself.

Marketing dashboards

Spend, pipeline contribution and cost per acquisition reconciled against CRM truth rather than platform self-reports.

Sales dashboards

Pipeline, stage conversion and forecast accuracy from the CRM.

Customer analytics

Cohort retention, lifetime value and churn indicators computed consistently.

Operational analytics

Throughput, cycle time and capacity for the operational processes that constrain the business.

KPI monitoring

A defined set of measures with owners, targets and alerting when they move materially.

Real-time reporting

Streaming or near-real-time views where the decision genuinely requires it, and batch where it does not.

Data visualisation

Charts chosen for the comparison being made, with accessible colour and labelled axes.

AI-assisted business insights

Natural-language querying over the warehouse, with generated SQL shown so results can be checked.

How the work runs

1. Start from the decision

   Which decisions are being made badly for want of data, and what would have to be visible to make them well. Everything else follows from that list, and it keeps the project from becoming a data-collection exercise.

2. Agree definitions and owners

   Written definitions for each metric, with a named person who owns each one. This is where the real disagreements surface and it is better that they surface here than in a board meeting.

3. Build pipelines that fail loudly

   Retries, idempotency, freshness checks and alerting. A pipeline that fails silently is worse than no pipeline, because people keep trusting the stale number.

4. Transform in one layer

   Business logic in version-controlled transformations, so a metric is computed once and every consumer reads the same result.

5. Build focused dashboards

   One view per decision, with definitions visible and a documented refresh time so nobody wonders how current the number is.

6. Hand over with documentation

   A data dictionary, lineage and runbooks, so your team can extend the model without reverse-engineering it.

What you receive

• Decision inventory and metric definitions with owners
• Data pipelines with retries and alerting
• Warehouse or database schema
• Version-controlled transformation layer
• Data quality checks and monitoring
• Dashboards per decision area
• Automated scheduled reporting
• Data dictionary, lineage and runbooks

You probably need this if

• Three departments report three different revenue figures.
• Somebody spends a day a month building the board pack by hand.
• Your dashboard has not been trusted since it was visibly wrong.
• Nobody can produce a cohort retention curve.
• Marketing spend is judged on ad platform numbers alone.

What we build and work with

Sized to the business. Most companies asking for a warehouse are better served by a well-designed Postgres than by a platform priced for a different scale.

PostgreSQL

Frequently sufficient as both operational store and analytical warehouse well beyond the point people assume they need something larger.

BigQuery or Snowflake

Where data volume or concurrency genuinely justifies a warehouse, chosen on cost model rather than on brand.

dbt or SQL transformations

Version-controlled, tested transformations so metric logic lives in one reviewable place.

Airflow, Dagster or n8n

Orchestration matched to complexity — n8n handles a surprising amount without a full scheduler.

Metabase or Looker Studio

BI chosen for who has to read it, with Metabase self-hosted where data cannot leave your infrastructure.

Data quality checks

Freshness, row-count, uniqueness and null checks running with the pipeline and alerting on failure.

Data dictionary

Definitions, ownership and lineage documented and kept next to the code rather than in a forgotten wiki.

What changes once this is in place

One number everybody uses

Metrics defined once with a named owner, which ends the recurring argument about whose figure is correct.

Reports that arrive without effort

Scheduled and distributed automatically, returning the hours currently spent exporting and reformatting.

Broken data is noticed

Freshness and quality checks with alerting, so a stale dashboard announces itself rather than misleading quietly.

Dashboards people actually read

One view per decision, short enough to absorb, with definitions visible on the page.

A model your team can extend

Documented lineage and version-controlled logic, so new questions do not require us.

How this differs by market

The work is the same craft everywhere. What changes is the law, the language and the buying culture — and those change enough to matter.

European Union

Analytical stores holding personal data remain subject to the GDPR: purpose limitation, retention periods and the ability to fulfil erasure requests across the warehouse as well as the source systems. Pseudonymisation in the analytical layer is frequently the practical answer and is easier to design in than to retrofit.

Nordics

Data residency requirements appear regularly in procurement, particularly for public-sector and regulated clients. A self-hosted Postgres and Metabase deployment inside the EU satisfies most of these without the cost of a managed platform.

United States and Canada

Sector rules drive architecture — HIPAA de-identification standards for health data, PCI scope boundaries for card data. Quebec’s Law 25 requires an assessment before transferring personal information outside the province, which affects where the warehouse can sit.

United Arab Emirates

The federal PDPL applies to mainland entities, with DIFC and ADGM operating separate regimes, and financial services regulators there set additional expectations about processing location. Multi-currency and multi-entity reporting is the norm rather than the exception for businesses operating across the Gulf.

Not legal advice. Regulatory summaries on this site describe how we scope and build, and are current to our latest review. Verify the operative text with qualified counsel in the relevant jurisdiction before relying on it.

How we know it worked

The tangible measures are pipeline reliability — success rate and freshness against target — and the time taken to produce recurring reports, compared against a baseline recorded before the work.

The measure that matters more is whether decisions changed. We ask, at the quarterly review, which decisions were made differently because of something the dashboards showed. If the answer is none, the dashboards are wrong regardless of how reliable the pipelines are.

We do not claim revenue impact from reporting work. Better information can lead to better decisions; attributing a revenue figure to a dashboard requires assumptions that cannot be tested.

Estimates are labelled as estimates. Any figure on this site that describes a range is a planning estimate with its assumptions stated, not a measured client outcome. We do not publish client results without the client's permission and a date.

Related services

Sectors where this is usually the lead engagement

These are the industries where this discipline is typically the first thing a client buys rather than something added later. The link goes to a page written for that sector specifically, with a paragraph on this service and on every other one.

• Manufacturing & Industrial — Excellent products, world-class production, and commercial systems that have not been touched in a decade. That gap is the opportunity.
• Logistics & Freight — One of the fastest-moving sectors for AI adoption, and one where most of the value is still locked in email attachments.
• Construction & Engineering — The fastest-accelerating sector for AI adoption, running on drawings, variations and payment applications that arrive as PDFs.
• E-commerce & Retail — The third-largest sector for digital transformation spending, and the one where a business can grow revenue while losing money on every order.
• SaaS & Software — Buyers who evaluate suppliers professionally, sell to people who do the same, and measure everything — which raises the bar on both sides.
• Automotive & Mobility — A sector where the manufacturer sets the rules, the margin is in aftersales, and lead response time decides who sells the car.
• Energy & Utilities — Enormous operational data volumes, heavy regulatory reporting, and customer systems that were built for a market that no longer exists.
• Wholesale & Distribution — Thin margins, enormous SKU counts and customer-specific pricing that lives in a system nobody wants to touch.
• Food & Beverage Production — Traceability obligations that must survive a recall, retailer requirements that exceed them, and a direct channel most producers have never built.
• Fintech & Payments — Startup growth expectations inside a supervised institution, where onboarding conversion and financial crime obligations pull in opposite directions.
• Pharma & Life Sciences — Where data integrity is a regulatory obligation, promotion to the public is largely prohibited, and every system that touches quality must be validated.
• Nonprofits & Associations — Retention economics identical to a subscription business, budgets that must be defended to a board, and systems that have to work for volunteers.
• Agriculture & Agritech — A sector where subsidy compliance, buyer requirements and new supply chain regulation all demand data that farms have never systematically collected.
• Medical Devices — Where the documentation is the product as far as a regulator is concerned, and post-market surveillance never ends.
• Marine & Shipping — Assets moving between jurisdictions, crews whose certificates expire, and emissions reporting that has become a financial obligation.

It appears on all thirty sector pages, because every one of them carries a paragraph on all twenty-two services. This list names only the sectors where it tends to lead.

Where we deliver this

This service is delivered across the European Union, the Nordic countries, North America and the United Arab Emirates. The craft does not change; the law, the language and the buying culture do. Consent regimes, invoicing mandates and payment conventions differ enough between markets that a campaign or a system built for one frequently cannot be used unchanged in another.

Each country page sets out what actually differs there and what it means for scope — all 32 countries and 10 cities are listed here. A few of the markets we work in most:

• Germany
• Netherlands
• France
• Sweden
• United States
• United Arab Emirates