Data & Analytics
One set of numbers everyone can agree on.
Data platforms, warehousing, and BI built so the board, finance, and operations stop arriving at meetings with three different versions of the same figure.
60+
Platforms delivered
4x
Faster reporting cycle
100+
Sources integrated
1 day
Typical close latency
Why programmes stall
Three reasons the data nobody trusts is still the data you have
Every organisation we meet has more data than it can use and less agreement than it needs. The causes are consistent.
The same word means three things
Finance, sales and operations each compute 'revenue' in their own tool. All three are defensible. None of them reconcile, so the meeting is about the number instead of the decision.
- Logic duplicated in every BI tool
- No owner for a definition
- Reconciliation done by hand, once, then abandoned
A year of plumbing before any value
Programmes that start by ingesting everything spend four quarters building pipelines. By the time a dashboard appears, the sponsor who funded it has moved on.
- Scope defined by source systems, not decisions
- No production user until the end
- Backfills that cannot safely be re-run
Nothing fails loudly
A pipeline that runs green while loading yesterday's data is worse than one that breaks. The report is wrong, it looks fine, and someone acts on it.
- Tests cover the pipeline, not the data
- Freshness invisible to the reader
- Schema drift discovered downstream
Capabilities
The full path from source system to decision
Most organisations have plenty of data and very little agreement about it. These are the pieces that fix that.
Warehouse & lakehouse
A modelled, governed store designed for the questions you actually ask — not a lake that quietly became a swamp nobody trusts.
- Dimensional modelling
- Slowly changing dimensions
- Time-travel history
Ingestion & pipelines
Reliable ELT from ERP, CRM, operational databases, files, and third-party APIs, with backfills that are safe to re-run.
- Idempotent loads
- Change data capture
- Schema-drift handling
Semantic layer
Metrics defined once, in version control, with an owner. 'Revenue' means the same thing in every dashboard because it is computed in one place.
- Version-controlled metrics
- Certified datasets
- Lineage documented
Business intelligence
Dashboards designed around the decision being made, with distribution and alerting so insight reaches people who never open a BI tool.
- Role-based views
- Scheduled distribution
- Threshold alerting
Governance & quality
Tests on the data itself, not just the pipeline: freshness, volume, uniqueness, and referential checks that fail loudly before a report does.
- Automated data tests
- Row-level security
- Catalogue and lineage
Advanced analytics
Forecasting, segmentation, and propensity models delivered into the systems where decisions happen rather than into a notebook nobody reruns.
- Feature store
- Model monitoring
- In-product delivery
Platform anatomy
Four layers between a source system and a decision
Each layer has one job and a contract with the next. Collapsing them is what produces a lake nobody trusts.
Ingestion
Managed connectors
Fivetran or Airbyte for commodity sources, so nobody maintains a Salesforce API client.
Change data capture
Log-based CDC from operational databases without loading the primary.
Idempotent loads
Every load is safe to re-run, so a backfill is routine rather than an incident.
Schema-drift handling
Upstream column changes surface as an alert before they surface as a wrong number.
Storage and modelling
Raw landing zone
Immutable, append-only source history you can always rebuild from.
Dimensional models
Conformed dimensions and facts designed around the questions actually asked.
Slowly changing dimensions
History preserved, so last quarter's report still reproduces last quarter's answer.
Version-controlled SQL
dbt models under review, tested in CI, with lineage generated from the code.
Semantics and governance
Metric definitions
Computed once, owned by a named person, referenced everywhere else.
Data quality tests
Freshness, volume, uniqueness and referential checks that gate publication.
Row-level security
Access enforced in the warehouse, not in each dashboard's filters.
Catalogue and lineage
Every certified dataset traceable back to its system of record.
Consumption
Certified dashboards
Built around a decision, with the definition and owner visible on the page.
Scheduled distribution
Insight delivered to people who will never open a BI tool.
Reverse ETL
Modelled attributes pushed back into CRM and operational systems.
Model serving
Forecasts and scores delivered in-product, with drift monitoring attached.
A metric is only certified once it reconciles against its system of record, has a named owner, and has tests gating its publication. Anything short of that is published as uncertified and labelled as such on the dashboard.
Our approach
Why most data programmes stall
Scope
Start from a decision, not a source system
Programmes that begin by ingesting everything spend a year building plumbing before anyone sees value. We start from a handful of decisions the business cares about and pull only the data those need.
- Two to three decision areas chosen with the people who own them
- First governed dashboard in production within eight weeks
- Sources onboarded in the order the decisions require
- Scope expands only after the previous slice is in daily use
Trust
Numbers people will actually defend
A dashboard is only useful if the finance director will put it in a board pack. That means reconciliation against source, a documented definition, and a named owner for every metric.
- Every certified metric reconciled against its system of record
- Definitions in version control with review history
- Freshness and quality tests gating publication
- A visible owner per dataset, not a shared mailbox
Adoption
Built to be maintained by your team
We use tooling your analysts can learn, with patterns documented as they are built. The measure of success is your team shipping a new model without calling us.
- SQL-first modelling that analysts can read and review
- Pair-building sessions rather than a handover document at the end
- Internal contribution guide written during the engagement
- Optional retained support that steps down over two quarters
Maturity
Where you are now, and what the next step actually costs
Most organisations sit at stage two and try to buy stage five. The jump that pays is always the next one.
| Stage | What it looks like | Typical symptom | Next step | Time to move |
|---|---|---|---|---|
| 1 · Spreadsheets | Extracts pulled by hand, mailed around, versioned in filenames | Nobody knows which file is current | Central warehouse and one automated load | 4–6 weeks |
| 2 · Tool sprawlMost common | Several BI tools, each with its own logic and its own answer | Meetings spent reconciling numbers | A semantic layer and one certified domain | 8–10 weeks |
| 3 · Governed core | One warehouse, modelled, with tests and named owners | Trusted, but slow to extend | Repeatable domain pattern and self-serve | One quarter |
| 4 · Self-serve | Analysts ship their own models against a reviewed pattern | Governance keeping pace is the constraint | Contracts, SLAs and automated certification | Two quarters |
| 5 · Data as product | Domains publish versioned datasets with SLAs and consumers | Platform investment needs a business case | Federated ownership and cost attribution | Ongoing |
1 · Spreadsheets
- What it looks like
- Extracts pulled by hand, mailed around, versioned in filenames
- Typical symptom
- Nobody knows which file is current
- Next step
- Central warehouse and one automated load
- Time to move
- 4–6 weeks
2 · Tool sprawl
Most common- What it looks like
- Several BI tools, each with its own logic and its own answer
- Typical symptom
- Meetings spent reconciling numbers
- Next step
- A semantic layer and one certified domain
- Time to move
- 8–10 weeks
3 · Governed core
- What it looks like
- One warehouse, modelled, with tests and named owners
- Typical symptom
- Trusted, but slow to extend
- Next step
- Repeatable domain pattern and self-serve
- Time to move
- One quarter
4 · Self-serve
- What it looks like
- Analysts ship their own models against a reviewed pattern
- Typical symptom
- Governance keeping pace is the constraint
- Next step
- Contracts, SLAs and automated certification
- Time to move
- Two quarters
5 · Data as product
- What it looks like
- Domains publish versioned datasets with SLAs and consumers
- Typical symptom
- Platform investment needs a business case
- Next step
- Federated ownership and cost attribution
- Time to move
- Ongoing
Delivery
A data engagement, phase by phase
Value lands early and compounds, rather than arriving all at once at the end.
- 01
Weeks 1–2
Decision mapping
We interview the people who make the decisions, agree the metrics that matter, and trace each one back to its source systems.
- 02
Weeks 3–6
Platform foundation
Warehouse, ingestion framework, transformation project, and CI set up with the first source loaded end to end.
- 03
Weeks 7–10
First certified domain
One business domain modelled, reconciled, tested, and published — in daily use before the second domain starts.
- 04
Ongoing
Domain expansion
Additional domains added on a repeatable pattern, each with the same reconciliation and quality bar as the first.
- 05
Handover
Enablement
Your analysts take ownership with documented patterns, a contribution guide, and a support taper rather than a cliff edge.
What you get
What you keep at the end of each phase
All of it lives in your warehouse and your repository. None of it depends on our tooling or our people.
Foundation
- Warehouse provisioned with environments and role model
- Ingestion framework with the first sources loaded end to end
- dbt project, CI pipeline and review process
- Naming, layering and contribution standards documented
Certified domain
- Dimensional model for one business domain
- Reconciliation pack against the system of record
- Data quality test suite gating publication
- Production dashboard in daily use with a named owner
Enablement
- Contribution guide written during the build, not after
- Pair-building sessions recorded for your analysts
- Runbook for pipeline failure and backfill
- Support taper plan across two quarters
Toolchain
The modern data stack, chosen conservatively
We pick tools with a hiring market and a clear exit path. Nothing here locks your data in.
Warehouse
Snowflake, BigQuery, Databricks, Redshift, and Synapse — plus PostgreSQL where the volumes genuinely do not justify more.
Transformation
dbt for modelling and tests, Airflow or Dagster for orchestration, Fivetran and Airbyte for managed ingestion.
Presentation
Power BI, Looker, Tableau, and Metabase, driven from a shared semantic layer rather than per-tool logic.
Science
Python, scikit-learn, and PyTorch, with MLflow for tracking and monitoring once models reach production.
Engagement models
Ways to buy the work
Every option starts by agreeing the decisions the platform has to serve. None of them start by ingesting everything.
Reporting review
Fixed fee
Two weeks, quoted up front
Where your definitions diverge today, what it costs, and the shortest path to one source.
- Metric inventory across existing tools
- Divergence analysis with worked examples
- Target architecture and sequenced roadmap
- Findings yours regardless of next steps
Platform and first domain
Project
Roughly ten weeks, phased
Foundation plus one reconciled, certified domain in production and in daily use.
- Warehouse, ingestion and transformation stack
- One domain modelled, tested and reconciled
- Certified dashboard with a named owner
- Analysts paired in throughout the build
Embedded team
Retainer
Monthly, tapering by agreement
Named analytics engineers working inside your team while capability transfers.
- Domain expansion on the proven pattern
- Code review and standards enforcement
- On-call for pipeline incidents
- Planned step-down rather than a cliff edge
Questions
What clients ask before committing
Mostly about trust and about who owns the thing afterwards. Both are fair.
Approach
Why not just ingest everything first?
Because it postpones all value to the end of the programme and guarantees you model data nobody asked about. Starting from two or three decisions means a governed dashboard is in production inside eight weeks, and every source after that is pulled because a decision needs it. The full estate still arrives — it just arrives in a useful order.
Do we have to replace our BI tool?
No. Power BI, Looker, Tableau and Metabase all sit happily on a governed semantic layer, and the point of the layer is that the tool stops mattering. If you have three tools, we will suggest consolidating eventually, but that is a licensing and training conversation rather than a technical prerequisite.
How do you handle a metric two departments disagree on?
We publish both, name them distinctly, document who owns each and what each is for, and reconcile them against source. Forcing a single definition on a genuine business disagreement produces a number neither side uses. Making the difference explicit and traceable usually resolves it within a quarter.
Ownership and cost
Will our analysts be able to maintain this?
That is the design constraint. Modelling is SQL-first in dbt, which analysts can read and review, and the patterns are documented as they are built rather than written up at the end. Success is your team shipping a new certified model without calling us — we measure it, and the retainer tapers against it.
What does the warehouse itself cost to run?
It depends on volume and query pattern, and we model it during the review rather than quoting a number here. What we will commit to is unit-cost visibility from the first month, so the bill is attributable to a domain and a team rather than arriving as one line.
Are we locked into your tool choices?
We deliberately pick tools with a hiring market and a documented exit path — dbt, Airflow or Dagster, and a mainstream warehouse. Your models are SQL in your repository and your data is in your warehouse. Moving away from us costs you a handover conversation, not a rebuild.
Stop arguing about whose number is right.
A two-week review of your current reporting: where the definitions diverge, what it costs you, and the shortest path to one governed source.