Data strategy and analytics that survive governance scrutiny.
GA4, warehousing, attribution and AI-augmented reporting anchored to enterprise data governance, quality and architecture.
Insight programmes framed around accountable questions.
Leadership asks sharper questions than dashboards answer alone — so we pair analytics delivery with governance, lineage and controls uplift so metrics remain attributable and regulator-ready.
Before work begins, we clarify the operating context, governance expectations, and commercial pressures behind the brief. That gives the engagement a clear purpose before technical analysis starts.
The result is a more complete advisory view: what matters now, where risk may surface next, and how recommendations can be implemented without creating unnecessary hand-offs or ambiguity.
Scope
Clarify the decision, deadline, stakeholders, and evidence standard before work begins.
Delivery
Combine partner judgement, technical review, and practical implementation planning in one workstream.
Follow-through
Convert findings into owners, actions, and next steps that leadership can track after the session.
Data & analytics pillars
Data governance
Advisory-only frameworks spanning ownership, stewardship, policies and regulatory alignment — so KPI pipelines remain attributable and auditable.
Data quality & controls
Accuracy programmes coupling lineage mapping, validation cadences and controls uplift to preserve reporting integrity under pressure.
Data architecture advisory
Cloud, mesh and integration patterns sized for latency, cost and maintainability — design artefacts grounded in implementation realism.
Data strategy & operating model
Enterprise vision, funding rhythms and accountability splits aligning analytics pods with business partners — preventing resets every planning cycle.
Analytics foundation
GA4 / BigQuery warehouse, cleaned identity, event design, consent management.
Attribution
Channel and content attribution modelling with honest uncertainty disclosure.
Experimentation
A/B test design, sample sizing, statistical reporting and experimentation culture.
Dashboards
Looker / Power BI dashboards tied to specific leadership questions.
AI-augmented insight
LLM-assisted reporting with explainability and source-tracing guardrails.
Data modelling
dbt-based transformations, lineage and documentation.
Question catalogue
Identify and catalogue the 10-15 questions leadership actually asks.
Data model
Model the data needed to answer those questions, not everything available.
Reporting layer
Dashboards and narrative reports designed around answers, not tables.
Embed
Weekly / monthly cadence that puts the answers in front of the people who need them.
A good analytics platform answers questions faster than the CEO can ask them.
Analytics stack feeling ornamental?
A two-week review produces a question-led dashboard plan and prioritises data work accordingly.