Data Visualisation
Dashboards and reports people actually read: built around a decision, honest about uncertainty, and readable at a glance without a legend of caveats.
Overview
Most dashboards are built by listing available metrics and arranging them on a grid. They get opened twice, then people go back to asking someone for a number. A dashboard is only useful when it was designed around a specific decision made on a specific rhythm.
The other common fault is presentation. Truncated axes, dual axes chosen to force a correlation, and pie charts with eleven segments all mislead, usually unintentionally. Clear visualisation is mostly a matter of not doing those things.
Who this is for
- Companies with dashboards nobody opens
- Teams whose reporting is a monthly manual spreadsheet
- Businesses where leadership asks for numbers rather than looking them up
How we approach data visualisation
The specific pieces of work a typical engagement covers. Scope is agreed up front — nothing here is a surprise line item later.
Decision-led design
Each dashboard built for a named decision and audience. Dashboards for everybody serve nobody.
Metric hierarchy
The two or three numbers that matter, prominent, with supporting detail available rather than competing for attention.
Honest chart choice
Axes from zero where the comparison demands it, no dual axes implying relationships that are not there, and chart types that suit the data.
Uncertainty shown
Comparison periods, ranges and sample sizes visible, so a small movement is not read as a trend.
Automated data pipelines
Refreshed automatically from source, since a dashboard that needs manual updating stops being updated.
Annotation
Recording what happened on the dates things changed, which is what turns a chart into an explanation.
From first call to measured result
The same sequence every time, so you always know what happens next.
Find the decision
Identify who decides what, how often, and what would change their mind. That defines the dashboard.
Prototype
A rough version reviewed with the actual audience before anything is connected to live data.
Build
Connect the pipelines and build the dashboard, with refresh reliability treated as part of the work.
Review usage
Check after a month whether it is being opened. If not, we change it or retire it rather than leaving it.
Outcomes, not deliverables
A pile of artefacts isn't progress. These are the changes the work is meant to produce — and what we report against.
Reports that get opened
Designing around a decision is the difference between a dashboard in use and a link nobody clicks.
Time back each month
Automated pipelines remove the manual assembly that consumes days in most marketing teams.
Fewer misread charts
Honest presentation prevents confident decisions based on a truncated axis.
Self-service answers
Leadership looking things up directly reduces the constant flow of number requests.
Common questions about Data Visualisation
The things people ask before they get in touch. If yours is not here, ask us directly.
Which dashboard tool should we use?
Whatever your data already lives near and your team can maintain. Looker Studio is free and adequate for most marketing reporting; a warehouse-backed tool becomes worthwhile when you are joining marketing to sales or product data. The tool matters far less than the design.
Why does nobody use our dashboard?
Almost always because it was built from available metrics rather than from a decision, so nothing on it changes what anyone does. Occasionally it is trust: if the numbers were wrong once, people stop looking and never come back.
How many dashboards should we have?
Few, and each with a named owner and audience. Proliferation is what kills a reporting culture; when there are twenty dashboards, none of them is the one people check.
Do we need a data warehouse?
Only when you need to join data across systems, or when the volume makes direct connections slow. Plenty of good marketing reporting runs without one, and we will say when you do not need the extra complexity.
Thinking about Data Visualisation?
Tell us what you are trying to change. If we are not the right fit we will say so, and point you somewhere better.
Looking at the wider picture?
Data Visualisation usually sits alongside other work in Data, Analytics & Measurement. Browse the full area to see what it connects to.