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Services  /  02 Analytics

We build the semantic layer and metric definitions underneath your dashboards, then the dashboards and self-serve tools on top. Finance, sales and operations finally argue about decisions instead of numbers.

Discuss a projectHow an engagement runs
Typical first result
4–6 weeks
Works with
Power BI, Looker, Superset
You own
Every definition and dashboard
Q3 revenuethree dashboardsIllustrative
Financedashboard
£4.62M
revenue incl. VAT
Salesdashboard
£4.91M
bookings, not invoiced
Operationsdashboard
£4.38M
net of returns, by ship date
Semantic layernet_revenue = invoiced − returns − VATowner: Finance
Spread of £0.53M between teamsSame data, one definition

Sound familiar?

What we usually walk into.

  • Dashboards nobody trusts.

    People export to Excel and rebuild the numbers themselves, so every team has its own version.

    Governed metrics with owners and tests

  • Analysts as a ticket queue.

    Simple questions wait days, and the team never gets to the deep work.

    Self-serve on top of trusted definitions

  • Forty dashboards, five used.

    Licences and maintenance for reports nobody opens.

    A small set of decision-focused dashboards

  • The follow-up question stalls.

    A chart shows what happened, but nobody can drill into why without an analyst.

    Drill-down and natural-language questions

What we build

From definitions to decisions.

The visible part is the dashboard. The valuable part is what is underneath it.

01

Semantic layer

Metrics, dimensions and joins defined once in code, reviewed and versioned.

dbt semantic layerCubeLookML
02

BI platform

Selection, design and migration, including cost and licence clean-up.

Power BILookerSuperset
03

Executive dashboards

A few dashboards and visualisations built around real decisions, not every available chart.

Board packsKPI trees
04

Self-serve analytics

Governed exploration for business teams, with training and guardrails.

ExploresMetabaseExcel
05

Embedded analytics

Customer-facing reporting inside your own product, with row-level security.

Embedded BIAPIs
06

Forecasting

Demand, revenue and capacity forecasts your teams can question and trust.

PythonProphetscikit-learn

Metric explorer

Ask anything. Same answer everywhere.

Every metric is defined once, tested, and owned. Slice it however you like: the dashboard, the spreadsheet export and the AI assistant all calculate it the same way.

  • Metric definitions in version control, reviewed like code
  • Owners named, so questions go to the right person
  • Self-serve without a ticket queue, and without chaos
Exploregoverned metricsIllustrative data
Metric
By
£4.48MQ3 total
  • North£1.01M
  • South£1.45M
  • East£0.73M
  • West£0.57M
  • Online£0.73M
Definition
-- defined once, used everywhere
metric net_revenue:
  sum(invoice_amount)
  − sum(returns_amount)
  − sum(vat_amount)
  where status = 'posted'
OwnerFinance
Tests9 passing
Used in
Board packSales dashboardExcel exportAI assistant

Analytics process

How it runs.

We begin with the numbers people argue about, agree one definition for each, and build the dashboards on top.

  1. 01

    Audit the metrics

    1–2 weeks

    We collect the reports people actually use and find which numbers disagree, and why.

    You get

    • A list of metrics, owners and conflicting definitions
    • The reports that matter, and the ones nobody opens
    • A short list of metrics to fix first
  2. 02

    Define

    2–3 weeks

    We write each core metric once, in code, with an owner, and agree it with the teams that use it.

    You get

    • A semantic layer with the first 10–20 metrics
    • Named owners and tests for every definition
    • A sign-off from finance, sales and operations
  3. 03

    Build

    4–8 weeks

    We rebuild the few dashboards that support real decisions, and set up governed self-serve for everyone else.

    You get

    • Decision-focused dashboards on the governed layer
    • Self-serve exploration with guardrails
    • Old, unused reports retired
  4. 04

    Adopt

    Ongoing

    We train teams, run office hours, track usage and keep improving what people actually use.

    You get

    • Training for each team that uses the numbers
    • Usage tracking and a regular review
    • A handover your analysts can own

How it runs

A typical engagement.

Start with the metrics that cause the most arguments, then expand.

Metric audit

Which numbers disagree, and why.

Audit

Core metrics defined

Top 10–20 metrics in the semantic layer, with owners.

Semantic layer

Dashboards and self-serve

Rebuilt on the governed layer, old ones retired.

Dashboards

Adoption

Training, office hours and usage tracking.

Adoption
Durations are typical, in weeks
Wk 06121824

Questions

What CTOs ask us.

Usually not. The semantic layer sits underneath whichever tool you use, and many teams keep their current one. If a change makes sense, we show the licence and maintenance savings first.

Often the fastest path is to fix the few tables behind your most important metrics first. We will tell you honestly whether you need data engineering work before analytics.

We build fewer of them, around specific decisions, with the people who make those decisions. Then we track usage and retire what is not used.

Yes, and that is the point. An assistant that reads governed metrics gives the same answer as the finance dashboard. See our Applied AI work.

Other services


Contact

Book a free 15-minute call to talk through what you are working on.

Book a free 15-minute callhello@growthgear.in

How we connect

  1. 01Book a short call

    Choose a time that works for you on our calendar.

  2. 02Discovery call

    A short call to understand the problem, the people and the constraints.

  3. 03Final scope and kickoff

    We send a clear scope and cost. Once you agree, we start.

Office

393, WeWork Vijohn Tower, 3rd FloorUdhyog Vihar Phase-4, GurugramHaryana 122016

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