Data platforms that make the numbers agree with each other.
Warehouses, streaming pipelines and governed models — so every dashboard, report and AI system draws from the same truth.
- Source & truth audit
- Platform foundation
- Build-out
- Operate & govern
Data quality is an engineering property.
Most enterprises don’t lack data — they lack agreement. Three dashboards, three revenue numbers, and a steering meeting that turns into an argument about definitions instead of a decision. The cause is rarely the warehouse. It is that the same calculation lives in six places — a BI tool, a stored procedure, an export someone fixes by hand every Monday — and none of them is authoritative. We build data platforms where lineage is explicit, quality is tested like code, and “where did this number come from” has a clickable answer.
The method is ordinary engineering applied to data. Transformations are version-controlled and reviewed by a second person. Every model has a named owner, a written contract and tests that run before it merges. Pipelines carry freshness SLAs, and a breach pages someone rather than surfacing three days later in a board pack. It is the same discipline that runs under our own platforms: reporting on the points ledger in Loyalty & Retention and on rated sales in Sales Performance reads governed models, never the operational tables directly.
Data quality is an engineering property: tested in CI, monitored in production, alerted on breach — exactly like uptime. If the 06:00 load misses its freshness window, the on-call engineer finds out before the CFO does, and the dashboard says so on its face rather than showing yesterday as though it were today.
Four things a data platform has to do.
Storage is the easy part. What makes a platform trustworthy is the modelling layer above it, the tests around it and the ownership recorded next to every dataset.
Warehouse & lakehouse
Snowflake, BigQuery or Databricks platforms with dbt-modelled, documented, tested transformations. We pick the engine from your workload mix and the cloud you already run on — see AWS, Azure and Google Cloud — not from preference. Staging, intermediate and mart layers stay separated, so changing one metric does not ripple silently through forty reports.
Streaming pipelines
Kafka and CDC-based flows for the cases where yesterday’s batch is too late: fraud checks, operational dashboards, personalisation at the point of contact. Change data capture with Debezium turns a system nobody wants to modify into an event source without touching its code. Consumers are idempotent, so a replay corrects the record rather than doubling it — the patterns are set out on our Kafka page.
Data quality & lineage
Contracts and tests on every model, lineage graphs end to end, freshness SLAs with alerting attached. Tests run in CI on the pull request and again in production after each load, so a renamed upstream column fails the build instead of the quarter-end pack. Lineage is generated from the code that actually runs, which is why it cannot drift from reality the way a diagram does.
Analytics & BI enablement
Semantic layers and Power BI or Looker models — one definition of revenue, used everywhere. We move business logic out of individual reports into the modelled layer, so a metric is defined once and every tool reads the same figure. Analysts get documented marts, a data dictionary and a review path for adding new measures without forking the definition.
How the work runs, day to day.
Every number has an address.
The first question in any disputed report is “where does this come from”, and in most estates the honest answer takes a long time. On our platforms it takes a click. Each mart column resolves back through the models that produced it to the source table and the system that owns it, with the transformation visible as reviewed code rather than as a screenshot of a query. Ownership is recorded next to the dataset, not in a wiki page that went stale two reorganisations ago, so a question about the number has a person attached to it as well as a path.
- Lineage generated from the compiled models, so the graph and the pipeline cannot disagree
- A named owner and a written contract for every published dataset — schema, grain, refresh and meaning
- Column-level impact analysis before a change merges: who breaks if this field changes shape
Quality tested where it breaks, not where it shows.
A data defect is cheapest at the load and most expensive in a decision. So the tests sit at the boundary: uniqueness and referential checks on keys, accepted-value checks on codes, volume and distribution checks against a rolling baseline, freshness checks against the SLA the business agreed. A failing test does not quietly log a warning. It holds the downstream models, keeps the last good snapshot visible, and raises an alert with the offending rows attached, so the platform degrades to “yesterday, and it says so” rather than to “today, and it is wrong”.
- Tests defined next to the model in code and run in CI before a change can merge
- Production assertions after every load, with quarantine tables instead of silent drops
- Freshness and volume SLAs per dataset, alerting to the team that owns the source
Streaming only where it earns its cost.
Real time is a requirement, not a badge. We map each use case to the moment a decision changes: if nobody acts before tomorrow morning, a nightly batch is cheaper to run and calmer to operate. Where the decision is inside the hour — a fraud hold, a dispatch reassignment, a stock figure a customer can see — we build the streaming path properly: change data capture off the source’s write-ahead log, an event backbone with schemas in a registry, idempotent consumers keyed on a business identifier, and a replay procedure that has been rehearsed rather than described.
- Debezium CDC from Oracle, SQL Server, MySQL or PostgreSQL without changing the source system
- One modelled definition shared by the streaming and batch paths, so the fast number and the settled number reconcile
- Dead-letter topics, lag alerts and a written replay runbook for every operational flow
The bill is part of the design.
Cloud warehouses charge for carelessness. Most of the spend we find on arrival comes from a handful of habits: full refreshes where an incremental model would do, dashboards querying raw tables directly, transformations repeated in five reports because nobody trusted the fourth one, and a scheduler running every fifteen minutes for data that changes daily. Modelling the layer properly removes most of that work rather than optimising it. We report warehouse spend by model and by consumer from the start, so the conversation is about specific queries rather than a total.
- Incremental models, partitioning and clustering chosen from real query patterns
- Spend attributed per model, per dashboard and per team — visible to the people who can change it
- Scheduler cadence set by the freshness the business agreed to, not by habit
One platform, four different jobs.
A metric defined once, read everywhere.
Analysts stop rebuilding logic per report. The semantic layer holds the definition of revenue, margin, churn and active customer; the BI tool reads it. New measures go through review, so the dictionary stays a description of what exists rather than a wish list.
- Documented marts with grain, keys and meaning written down
- Certified dashboards separated from ad-hoc exploration
- A review path for new measures, with the owner named
Numbers that survive the shift handover.
Operational reporting has a different bar from monthly analysis: it must be current, it must say when it is not, and it must reconcile with the system people work in. Operational marts carry a freshness stamp on every screen and fall back to the last good snapshot rather than showing a partial load.
- Freshness visible on the report, not buried in a footnote
- Streaming path where the decision is inside the hour, batch elsewhere
- Reconciliation against the source of record on a fixed schedule
A codebase your engineers can read.
Everything we build is code: dbt models, orchestration, infrastructure, tests, alert policies. Your team reviews our pull requests from the start and goes on to merge their own. Enablement is part of delivery, not a workshop at the end — the goal is a platform they operate without us.
- dbt project with a written style guide and naming rules
- Orchestration and infrastructure as code, reviewed like application code
- Runbooks for late loads, schema drift and backfills, rehearsed with your on-call
The prerequisite nobody wants to fund first.
Retrieval systems and models fail on the same thing that breaks dashboards: unclear grain, missing history, no lineage. Governed marts and event history are what make a feature store, a retrieval index or a forecast reproducible. Our AI & Machine Learning work builds on exactly this layer, and MLOps reuses its tests and monitoring.
- Point-in-time correct history, so training data matches what was knowable then
- Feature definitions shared between training and serving paths
- Document and event sources indexed with the same contracts as reporting
What is standard, and what is shaped per client.
Four phases, and the most-argued metric first.
Source & truth audit
Systems, owners, and where the numbers disagree today — the platform’s job description. We trace the two or three most-argued figures back through every path that produces them, write down which definition each stakeholder is actually using, and record what each source system can and cannot answer. The output is a short document that usually settles more arguments than it starts.
Platform foundation
Warehouse, ingestion and the first governed models — the metrics everyone fights about most, settled. Environments, CI, orchestration and access control are stood up as code in the same phase. Nothing is published until it has tests, an owner and a freshness SLA.
Build-out
Domain by domain: models, tests, lineage, dashboards — with owners named for every dataset. Each domain lands with its own reconciliation against the system of record, and legacy reports are retired deliberately rather than left running beside the new ones. Your engineers review from the start and go on to lead domains.
Operate & govern
Freshness SLAs, cost monitoring, access governance — and a platform team (yours) running it. We move to a support-and-review role: on-call rota handed over, runbooks rehearsed, spend and quality reported regularly, at a rhythm agreed with you. If you want us to keep operating it, that is a managed service decision, not a dependency you fell into.
What is on the table at handover.
- Deployed warehouse/lakehouse with governed models
- dbt codebase: tested, documented, lineage-complete
- Streaming pipelines where latency matters
- Semantic layer and BI models with single-definition metrics
- Data quality dashboards with freshness SLAs
- Orchestration and infrastructure as code
- Runbooks for backfills, schema drift and late loads — rehearsed with your team
- A cost report by model and consumer, with the reduction actions ranked
When this is the right call.
Choose this when reports disagree and nobody can say which is right; when finance, operations and product each maintain a private version of the same number; when a pipeline breaks quietly and is found days later; when warehouse spend is rising faster than usage; or when an AI initiative has stalled because the data underneath it cannot be trusted or reproduced. It is also the right first move before an AI programme, before a modernisation that will change where data comes from, and after an acquisition leaves you with two of everything.
Look elsewhere when the real problem is that one source system records the wrong thing — fix the process or the application first, because a warehouse will only model the error faithfully. Look elsewhere when a single team needs one report from one database; that is a small job, not a platform. And be cautious about starting when no one on your side will own datasets after handover: governance without owners decays, and we would rather scope a smaller platform with real owners than a complete one with none.
Frequently asked questions
Name the number your teams argue about.
Tell us the metric and the systems behind it. An engineer who has settled that argument before reads it and replies in writing with a view of what it would take.