Information hierarchy
Prioritize the measures and comparisons a user needs first, then design drill paths for investigating exceptions.
Data Visualization
Data Visualization
Design dashboards and visual analysis that make metrics, trends, exceptions, and decisions easier to understand without hiding the underlying definitions.
Inside the delivery
Design a dashboard around the next user decision
Identify who uses the report, what they compare and which actions follow an exception. The flow below shows the main delivery stages and the evidence produced at each step.
- 01
Audience and questions
Identify who uses the report, what they compare and which actions follow an exception.
Output
Dashboard brief and decision hierarchy
- 02
Metric and visual design
Agree definitions, chart choices, filters and drill paths without hiding important context.
Output
Metric specification and report prototype
- 03
Connected implementation
Bind governed data, implement interactions and account for permissions and refresh behavior.
Output
Dashboard implementation and data bindings
- 04
Usability and accuracy
Reconcile displayed values and test navigation, accessibility and performance with representative users.
Output
Validation findings and report maintenance notes
Controls across the workflow
- Metric definitions
- Accessible encoding
- Filter context
- Source reconciliation
Decisions that shape the scope
- Does a more interactive dashboard make the analysis better?
- Only when interaction helps the decision. A focused chart or table may be clearer; filter state, comparison periods and metric definitions must remain visible to avoid misleading interpretation.
- Power BI or Tableau?
- Ecosystem usually decides it. Heavy Microsoft estates tend toward Power BI for licensing and integration reasons; organisations wanting platform independence often prefer Tableau. Both are capable, and neither will fix disagreeing numbers, which is a modelling problem.
Before you commit
Is this the right engagement?
- What we need from you
- Audience roles, decision questions, metric definitions, data access, report examples and required refresh frequency.
- How you accept the work
- Reconcile displayed values and test navigation, accessibility and performance with representative users. Acceptance records the tested scope, unresolved issues and the owner's decision.
- Scope & alternatives
- Only when interaction helps the decision. A focused chart or table may be clearer; filter state, comparison periods and metric definitions must remain visible to avoid misleading interpretation.
Capabilities
Engineering scope and deliverables
Select the work that addresses your constraint. Responsibilities and acceptance criteria are agreed before delivery.
Chart and interaction design
Choose visual encodings, comparison periods and filters that preserve context. Include empty, missing-data and partial-refresh states.
Accessible reporting
Use readable labels, sufficient contrast and alternatives to color-only meaning; evaluate keyboard interaction where the reporting platform supports it.
Metric validation
Reconcile displayed values to agreed definitions and document refresh behavior, data limitations and the owner of each report.
Integrations
Selected for your environment
Tools are chosen around your existing systems, access requirements and operating constraints.
Microsoft Azure
AWS
Databricks
Snowflake
dbt
Power BI and Tableau
Frequently asked questions
Questions to resolve before starting
01Does a more interactive dashboard make the analysis better?
Does a more interactive dashboard make the analysis better?
Only when interaction helps the decision. A focused chart or table may be clearer; filter state, comparison periods and metric definitions must remain visible to avoid misleading interpretation.
02Power BI or Tableau?
Power BI or Tableau?
Ecosystem usually decides it. Heavy Microsoft estates tend toward Power BI for licensing and integration reasons; organisations wanting platform independence often prefer Tableau. Both are capable, and neither will fix disagreeing numbers, which is a modelling problem.
03Why are our dashboards so slow?
Why are our dashboards so slow?
Almost always the model rather than the tool: queries hitting raw tables, calculations done at render time, or no aggregation. Reporting performance is fixed in the layer underneath, which is why we treat modelling as part of this work.
04How do we stop dashboard sprawl?
How do we stop dashboard sprawl?
By retiring as you publish and by giving each report an owner and a review date. Sprawl comes from adding without ever removing, until nobody knows which of the four similar reports is authoritative.
05What should we prepare for the first technical discussion?
What should we prepare for the first technical discussion?
Audience roles, decision questions, metric definitions, data access, report examples and required refresh frequency.
06What evidence is available at handover?
What evidence is available at handover?
The agreed delivery includes validation findings and report maintenance notes. Reconcile displayed values and test navigation, accessibility and performance with representative users.
07How is the engagement estimated?
How is the engagement estimated?
We review the available inputs before estimating: Audience roles, decision questions, metric definitions, data access, report examples and required refresh frequency. The proposal identifies dependencies, review milestones and excluded work; the scope determines the schedule.
Start a conversation
Discuss your next technical step
Share your current situation and the constraint you need to resolve. We will use the discovery inputs above to define a practical scope for Data Visualization.