Executive Dashboards
Board- and exec-grade dashboards in Power BI, Tableau, or Looker. Built around the four to six metrics that actually drive decisions — not vanity metrics.
Data Analytics & BI
Data analytics & BI consulting that turns raw data into decisions
Cohort and funnel analysis, executive dashboards, predictive forecasting, and self-service BI architectures — engineered to produce decisions, not just charts. Strategy-led, vendor-neutral, ROI-anchored.
Why analytics initiatives fail
Most enterprises don't have a data problem — they have a decision problem. Dashboards exist, but executives still rely on instinct because the metrics aren't trusted, the definitions disagree across teams, or the visualisation answers the wrong question.
The cost shows up everywhere: pricing decisions made on stale data, retention work prioritised on the loudest customer rather than the most valuable, marketing spend allocated to channels that look good in last-click attribution but actually destroy contribution margin.
How we close the gap
We start with the decisions. Before opening Power BI or writing a single SQL query, we map the recurring decisions your leadership team makes, the data those decisions need, and the gaps between what's available and what's required.
From there we deliver three things: a defensible single-source-of-truth metric layer, executive-grade dashboards built around real decisions, and a forecasting layer that gives leaders a forward view — not just a rear-view mirror.
What We Deliver
What we deliver
Each engagement produces a focused set of artefacts, scoped at kickoff and demo'd weekly so nothing arrives as a surprise.
Cohort & Funnel Analysis
Customer cohorting, retention curves, conversion funnels, and attribution analysis that show where customers actually drop off — and where the leverage lives.
Predictive Forecasting
Revenue, demand, headcount, and capacity forecasts with confidence intervals. Built so finance, sales, and ops plan against the same forward view.
Self-Service BI Architecture
A semantic layer (dbt, LookML, or native) plus governed datasets that let business teams answer their own questions — without breaking the underlying numbers.
Metric Definition Layer
A documented catalogue of every business metric, its definition, owner, and source. Eliminates the "your number doesn't match mine" debate that wastes leadership cycles.
Stakeholder Enablement
Workshops and runbooks for execs and analysts so the dashboards stay used after we leave. We measure adoption, not just delivery.
How We Work
A delivery process built for measurable outcomes
From scoping to handover, every engagement follows the same disciplined cadence — designed to remove ambiguity and ship results.
Discovery & Decision Mapping
One to two weeks of leadership interviews to surface the recurring decisions and the data they require. Output: a prioritised analytics backlog.
Data Modelling
We design the semantic layer, define metrics, and document lineage. The dashboards rest on a single source of truth — not direct table queries.
Build & Iterate
Weekly demos with the actual stakeholders who will use the artefacts. Feedback loops are tight; the final dashboard rarely matches the initial mockup, and that's intentional.
Enablement & Handover
Documentation, runbooks, and training. We measure dashboard adoption 30 and 90 days post-launch and tune what isn't sticking.
Tools & Stack
Production-grade stack, vendor-neutral choices
We pick the right tool for the job — and document why — so you're never locked into a stack that doesn't fit your team.
Power BI
Tableau
Looker
Metabase
Sigma
dbt
Snowflake
BigQuery
Redshift
PostgreSQL
Python (pandas, statsmodels)
SQL
Outcomes
Outcomes you can expect
Indicative ranges based on typical client engagements. Every project ties to a measurable KPI agreed at kickoff.
40–70%
faster monthly close & reporting cycles after dashboard rollout
3–5x
increase in self-service queries answered without analyst help
~15%
typical lift in marketing efficiency from cohort & attribution work
1
single source of truth — no more "your number, my number"
FAQ
Frequently asked questions
How long does a typical BI dashboard project take?
Most executive dashboard engagements ship in 6 to 10 weeks — including discovery, data modelling, dashboard build, and stakeholder enablement. Larger self-service BI rollouts typically run 12 to 16 weeks.
Which BI tools do you work with?
We are vendor-neutral and deliver in Power BI, Tableau, Looker, Metabase, and custom BI stacks. We recommend the right tool based on your data infrastructure, team skills, and licensing economics — not vendor partnerships.
Do you work with messy or fragmented source data?
Yes. Most engagements begin with fragmented data across CRM, ERP, product analytics, and spreadsheets. Our analytics work is paired with light data engineering when needed so the dashboards rest on a clean, trusted foundation.
How is the engagement priced?
Fixed-scope projects are quoted up front against an agreed deliverables list and KPI. Embedded engagements are billed monthly. We share a transparent estimate within one business day of the consultation call.
Can you train our internal analysts?
Yes — knowledge transfer is built into every engagement. We deliver runbooks, recorded walkthroughs, and live workshops so your team owns the system after we leave.
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Ready to turn dashboards into decisions?
Book a 30-minute consultation. We'll review your current analytics stack, the decisions leadership keeps stalling on, and where a focused engagement would have the highest impact.
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