Data strategy & metric design
Working backwards from the decisions you need to make to the small set of measures that actually inform them.
Capability
Data & Analytics
One number, one definition, one source.
Pipelines, a governed model and dashboards people actually open on Monday.
The brief
Most reporting problems are definition problems
When finance and sales bring different revenue figures to the same meeting, the tool is rarely at fault. Two teams are calculating the same word differently, from two extracts, on two days. We fix the layer underneath first — reliable pipelines and a governed model where every metric has one written definition and traceable lineage — and only then build the dashboards. That is why the dashboards survive past the launch demo.
Signals you may recognise
- Two departments arrive at a meeting with two different numbers for the same thing.
- The monthly pack is assembled by hand and takes someone the better part of a week.
- Dashboards were built, presented once, and nobody has opened them since.
What we deliver
Data & Analytics services in detail
Each item below is scoped, priced and delivered on its own — take one, or take the set.
Warehouse & lakehouse builds
A modelled central store on BigQuery, Snowflake, Databricks or Fabric, structured for querying rather than storage alone.
Pipelines & integration
Scheduled, monitored ingestion from ERP, CRM, spreadsheets and operational systems, with failures that alert instead of going quiet.
Semantic & governance layer
Every metric defined once, with an owner, a formula and lineage back to source — the end of duelling spreadsheets.
Dashboards & self-service BI
Power BI, Tableau or Looker builds designed around a role’s daily question, not around every field available.
Operational & embedded reporting
Reporting placed inside the workflow — in the app, the inbox or the chat channel where the decision is taken.
Forecasting & advanced analytics
Demand, churn and capacity models, deployed only where a decision genuinely changes as a result.
AI-assisted analysis
Natural-language querying and summarisation grounded in your governed model, so answers stay traceable.
What it produces
Outcomes we hold ourselves to
Metrics agreed
A published dictionary that ends the argument about whose number is right.
Reporting time returned
Manual monthly assembly replaced by pipelines that run overnight.
Dashboards that get used
Built around real decisions, so people return to them without being reminded.
Trustworthy data
Freshness and quality checks that raise an alert before a user notices.
How the work runs
Start at the decision, not the data
We begin with the meeting you are trying to improve and work backwards. It keeps scope honest and delivery short.
Frame
Name the decisions
Interviews with the people who act on numbers, producing a shortlist of questions worth answering well.
Model
Define and structure
Source audit, metric definitions written and signed off, and a warehouse model built to serve them.
Build
Pipelines and views
Ingestion, transformation and testing automated, with the first dashboard shipped inside weeks rather than quarters.
Embed
Drive adoption
Training in your own data, feedback loops and iteration. A dashboard nobody opens is a failed project regardless of build quality.
Deliverables
What lands on your side
- Metric dictionary with owners and written formulas
- Warehouse schema and documented data lineage
- Version-controlled transformation code in your repository
- Role-based dashboard set with usage tracking
- Data quality monitoring and alerting rules
Platforms and tooling
What this is built on
Power BITableauQlikLookerBigQuerySnowflakeDatabricksdbtAlteryx
When clients call us
Situations this work is built for
Board reporting that consumes a week
Automating the pack so the finance team spends its time on the commentary instead of the copy-paste.
ERP data nobody can reach
Unlocking the numbers trapped inside a system that only three people know how to query.
Preparing for a raise or diligence
Clean, defensible metrics with lineage an investor’s analyst can follow without hand-holding.
The difference
How we keep analytics honest
Three habits that decide whether this becomes an asset or a graveyard.
Definitions before dashboards
We will not build a chart for a metric nobody has agreed. It feels slow for two weeks and saves two years of argument.
Adoption is the success measure
We track who opens what. If usage is low we treat it as our problem to solve, not evidence that users are resistant.
Fewer, better views
A tight set of dashboards someone reads daily beats a portal of two hundred nobody trusts.
Questions
Answered before you ask
Not always. If your data lives in one or two systems and volumes are modest, direct connections can carry you a long way. We recommend a warehouse when the joins across systems become the bottleneck.
Usually the one your team is closest to and already licensed for. Tool choice matters far less than the model beneath it — a good model works in any of them.
The first working dashboard on real data typically lands in four to six weeks. We deliberately scope a narrow first slice so value arrives before enthusiasm fades.
Yes, and most engagements start there. Spreadsheets are usually where the real business logic is hiding — we extract it, formalise it and retire the file.
Continue
Capabilities that pair with this
Talk to us
Tell us which number is disputed
Every reporting problem starts with two people quoting different figures. Name the metric and we will show you how the definition gets settled.