Data strategy that turns dashboards into decisions.
Having lots of data is not a data strategy. A data strategy is a plan for which data to collect, how to model it, where to store it, and how to surface it to the people who need to act. We architect measurement, warehousing, BI and activation as one connected programme — not five disconnected tools.
How we earn your confidence
Four signals that show how we demonstrate Experience, Expertise, Authoritativeness and Trustworthiness on this service — visible to both your team and the search engines that rank us.
Warehouse + activation fluent
We have shipped end-to-end BigQuery, Snowflake and Redshift programmes — from raw connector through dbt models to activated audiences in Meta, Google and HubSpot.
First-party-data ready
Consent-aware schemas, server-side tagging, CDP rulebooks and identity stitching — the data plumbing the cookieless future requires, designed once and reused everywhere.
BI rigour, not dashboard theatre
Looker, Looker Studio and Power BI dashboards governed by a metric layer — every chart traces back to a named owner, a definition and a query.
Privacy-aware by default
DPDPA and GDPR consent capture, region-aware retention and PII minimisation baked into the warehouse model — not bolted on after the regulator asks.
We've rebuilt fragmented analytics stacks, replaced 18-tab manual reports with one governed warehouse, and shrunk time-to-insight from three weeks to under a day — without throwing more tools at the problem.
From business question to governed, activated data — in five disciplined steps.
Five steps we run in lockstep so every metric in the warehouse ladders back to a real decision someone in your business needs to make.
Business question intake
We start with the decisions you need to make — not the data you happen to have. Channel mix, payback period, churn risk, retention cohorts. Every later choice is judged against this list.
Measurement model
We design the KPI tree, attribution approach and metric definitions before any pipeline is built. Named owners, agreed formulae and a north-star metric that survives finance scrutiny.
Architecture & warehouse
BigQuery, Snowflake or Redshift selection, source connectors, dbt modelling and a canonical mart layer. The warehouse is the single source of truth — every dashboard reads from it, nothing else.
BI & activation
Looker, Looker Studio or Power BI dashboards plus reverse-ETL pipes pushing audiences and conversions back to ad platforms, CRM and lifecycle tools. Insight becomes action, not a PDF.
Governance & iteration
Data dictionary, freshness SLAs, access controls, consent rules and a monthly review where we retire dashboards nobody opens and promote the ones that move decisions.
Six disciplines. One connected data programme.
Modern data strategy is no longer one tool or one team. We run the six disciplines below as a coordinated programme — not six disconnected projects competing for budget.
Measurement frameworks
KPI trees, attribution models and metric definitions agreed across marketing, sales and finance — the contract every dashboard ultimately reports against.
Data warehouses
BigQuery, Snowflake and Redshift programmes — source connectors, dbt models, mart layer, freshness SLAs and the cost discipline that keeps the bill sane.
CDP strategy
Segment, RudderStack and mParticle programmes — schema design, identity stitching, consent handling and the activation playbook that earns the licence cost back.
BI & dashboards
Looker, Looker Studio, Power BI and Tableau — governed metric layer, role-based dashboards and the regular pruning that keeps dashboards used, not just built.
First-party data
Server-side GTM, consent-aware capture, enhanced conversions and CAPI — the first-party foundation that makes paid media survive the cookieless transition.
Marketing data ops
The unsexy plumbing: pipeline monitoring, freshness alerts, cost reviews, schema change governance and a runbook for every recurring breakage.
Six artefacts your team actually uses.
The deliverables below are part of every data engagement — the artefacts that outlive the project and keep paying back long after the consultants log out.
KPI trees
The decision-to-metric map agreed across leadership — north star at the top, channel and tactic metrics below, every line owned.
Architecture diagrams
Source-to-mart-to-activation diagrams that anyone in your team can read — onboarding tool for new hires and audit artefact for finance.
BigQuery models
dbt-managed staging, intermediate and mart models with tests, docs and lineage — version-controlled and reviewable in Git.
Looker dashboards
Role-based dashboards for execs, marketing leads and operators — each tuned to the decisions that audience actually owns.
CDP rulebooks
Event tracking plans, identity rules, consent rules and audience definitions — the contract that keeps the CDP from quietly drifting into chaos.
Data dictionaries
Every metric, table and column named, defined, owned and linked to its source — the artefact that ends the "whose number is right" meeting.
Tools are inputs. Better decisions are the output.
Six KPIs we report on every month so the data programme always ladders back to actual business velocity — not vanity warehouse usage.
Data freshness
How current is the data when leaders open the dashboard. We govern against an explicit freshness SLA — not best-effort.
Dashboard adoption
Weekly active users per dashboard. Dashboards no one opens get retired; dashboards that move decisions get promoted.
Query cost
Warehouse spend per query, per model and per dashboard — the metric that keeps a BigQuery bill from quietly tripling overnight.
Data quality score
Composite of dbt test pass-rate, null-rate by field and schema drift events — tracked weekly, escalated on regression.
Time-to-insight
How long it takes from a new business question to a trustworthy answer. Most engagements start at weeks and end in hours.
Business decision velocity
Decisions taken with data per quarter — the ultimate measure of whether the data programme is earning its budget.
Where we ship data programmes that stick.
Categories we've delivered enough engagements in to know which questions matter, which sources lie and which dashboards leadership actually opens.
What you get that most data consultancies skip.
Picking the right data partner is less about polished decks and more about whether the team ships a governed warehouse, a clean metric layer and dashboards leadership actually opens — engagement after engagement.
Warehouse-first architecture — the warehouse is the source of truth, every dashboard reads from it, nothing else.
BI-rigorous metric layer — every chart traces back to a named owner, a definition and a query.
First-party-data ready — server-side tagging, consent capture, CAPI and enhanced conversions designed once and reused everywhere.
dbt-managed transformations — version-controlled, tested and reviewable in Git, not buried in someone's Looker SQL block.
Cost discipline — query cost reviewed monthly so a BigQuery bill never triples without someone noticing.
Privacy-by-default — DPDPA and GDPR consent capture, retention rules and PII minimisation baked into the model.
Dashboard pruning — dashboards nobody opens get retired so the BI estate stays small, sharp and trusted.
Knowledge transfer — your team owns the warehouse, the dbt repo and the dashboards on day one, not day 365.
Questions teams ask before they sign.
Ready to turn your data into decisions?
Book a free 30-minute consult — we'll audit your current data stack and send a custom data-strategy proposal within 48 hours.