Data Analytics
Your systems already record the answers. We define the metrics, wire the data, and build dashboards that end the “whose number is right” meeting.
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Trusted by teams across education, retail, and services
{ 01 } — Analytics process
Define once. Measure honestly. Review on cadence.
Analytics fails on definitions, not tools. So we start with a metric dictionary everyone signs — formula, source, owner — and only then automate its measurement. The dashboards come last, because a chart of a disputed number is just a prettier argument.
Define
- Metric dictionary — every KPI defined once, in writing
- Source-of-truth decision per metric
- A named owner per metric
- Targets, thresholds & alert levels
- The awkward conversations about whose number wins
Wire
- Source integrations — ERP, CRM, events, spreadsheets
- Transformations & models in version control
- Quality checks that block bad loads
- Refresh schedules with freshness visible
- Reconciliation back to the source systems
Use
- Role-based dashboards at the right altitude
- Scheduled reports that send themselves
- Alert thresholds that notify a person
- Self-serve access on modeled data
- Monthly metric review — definitions evolve deliberately
Every org has the meeting where finance and sales bring different revenue numbers, and the first half hour goes to arguing about whose export is right. The cure is structural, not diplomatic: one defined source per metric, transformations in version control, and changes reviewed like code.
Definitions are the real product. “Revenue” has to mean one thing — booked or collected, gross or net, before or after refunds — and that decision gets made once, written down, and owned by a named person. Everything downstream of the dictionary is plumbing.
Dashboards then become trustworthy by construction, and decisions speed up because nobody relitigates the data. The measure of success is quiet: the number appears, everyone accepts it, and the meeting moves on to what to do about it.
{ 03 } — What we deliver
Answers on a schedule.
Executive dashboards
The ten numbers that run the business — live, agreed, and drillable to the transactions underneath.
Operational analytics
Team-level views of pipeline, throughput, quality, and cost — refreshed on a cadence each team actually works to.
Self-serve exploration
Modeled data that analysts can query safely — new questions answered without a ticket, and without breaking a definition.
Automated reporting
The Monday report that writes and sends itself — same format, same sources, no copy-paste week.
Data quality & reconciliation
Checks that block bad loads and reconcile warehouse to source — drift surfaces in an alert, not in a board meeting.
Forecasting & deeper analysis
Demand forecasts, cohorts, funnels, and segmentation — with honest error bars and a baseline every model has to beat.
{ 04 } — Analytics stack
The analytics toolchain we deploy.
Sized to your data volume — a growing business rarely needs the tooling of a bank, and should not pay for it. Every layer is mainstream, documented, and hireable-for.
{ 05 } — Ways to engage
Three ways to start, matched to where you are.
Analytics foundation
A fixed-scope start: metric dictionary, warehouse, and the first live dashboards in 4–6 weeks — one metric domain done end to end, properly.
- Metric dictionary signed off first
- Production sources, real numbers
- One domain finished, not ten started
Build + handover
We build the full stack — sources, models, dashboards, alerts — document and test it, train your team, and hand over the keys.
- Your warehouse, your keys
- Models documented and tested
- Analyst training before handover
Analytics retainer
A monthly cadence for teams whose questions keep evolving — new metrics modeled, definitions reviewed, dashboards pruned when unused.
- Monthly capacity, no re-scoping
- Metric review on cadence
- Dashboards retired when nobody opens them
{ 06 } — What you receive
A system of record for your numbers.
Dashboards are the visible layer. What you actually receive is the machinery underneath that keeps them worth trusting.
Every KPI defined once — formula, source, owner, refresh — and signed by the people who used to argue about it.
A right-sized warehouse with version-controlled, tested transformations between raw data and every chart.
Executives, managers, and operators see the same truth at different altitudes — drillable, not decorative.
Tests that block bad loads, reconciliation against source, and thresholds that notify a person when a number moves.
Modeled tables analysts can query safely — questions answered without a ticket, definitions intact.
How every metric is computed and how to change one properly — so the system outlives any single analyst.
{ 07 } — The symptoms
Signs your numbers need engineering.
If these happen weekly, the fix is definitions and plumbing — not another chart tool.
{ 08 } — What changes
From gut feel to grounded calls.
Before
Metrics argued afresh in every meeting.
After
One dictionary, one source per metric — the meeting starts at the decision.
Before
Pricing set by last year’s habit.
After
Priced from margin and elasticity data, per segment.
Before
Marketing spend judged by feel at quarter end.
After
Channel ROI visible weekly; budget moves mid-quarter.
Before
Stock ordered on instinct; shortages found on Monday.
After
Demand-based ordering with alerts before stockout.
Before
Analysis requests queue behind one analyst.
After
Self-serve dashboards — the analyst does analysis again.
Where this applies
Get expert guidance on your analytics.
Book a free consultation call — a senior team member replies within one business day with real thoughts, not a sales script.
Frequently asked questions
Metabase, Looker Studio, and Power BI most often — but the modeling layer underneath matters more than the chart tool on top. Duelling numbers are almost never the chart tool's fault.
It is the normal starting point — connectors and a small warehouse consolidate it, usually within the first weeks. Spreadsheets count as sources too; we ingest them rather than pretend they will go away.
Definitions live in version-controlled transformations with named owners; changing a metric is a reviewed change with a history, not a quiet edit. When a definition does evolve, the change is announced, not discovered.
The foundation is a project; trust is a cadence — most clients keep a monthly review where new questions get modeled and definitions evolve with the business, deliberately.
The first live dashboards typically land within 4–6 weeks — one metric domain done end to end, because a narrow slice everyone trusts beats a broad one nobody does.
No — models are documented and tested, refreshes and alerts run unattended, and the monthly review keeps definitions current. When a data hire eventually makes sense, they inherit an engineered system, not tribal knowledge.