Catalog
method#Product#Observability#Analytics#Delivery

Dashboard Design

Method for systematic dashboard design focusing on information hierarchy, visualization choices, and data quality for different user roles.

Dashboard design is a structured method for planning and crafting dashboards that combine metrics, visualizations and context tailored to target audiences.
Established
Medium

Classification

  • Medium
  • Business
  • Design
  • Intermediate

Technical context

Grafana or Looker as visualization toolDatabase and data warehouse connectionsAlerting and incident management systems

Principles & goals

Audience orientation: Dashboards must be tailored to the information needs of the target audience.Information hierarchy: Key metrics first, supporting details via drilldowns.Measurability and transparency: Sources, definitions and freshness must be disclosed.
Build
Team, Domain

Use cases & scenarios

Compromises

  • Wrong decisions due to misinterpreted or poorly designed visualizations.
  • Information overload leads to ignoring important signals.
  • Stale data creates false confidence in metrics.
  • Use standardized metric definition templates.
  • Place primary KPIs prominently, secondary info via drilldowns.
  • Conduct regular reviews to validate relevance.

I/O & resources

  • Audience analysis and user requirements
  • Available data sources and schemas
  • Product or operational goals to measure
  • Prototypes and wireframes
  • Implemented dashboards and documentation
  • Governance policies and metric definition documents

Description

Dashboard design is a structured method for planning and crafting dashboards that combine metrics, visualizations and context tailored to target audiences. It defines information hierarchy, interactions and data sources as well as governance for freshness and ownership. It considers user needs, visualization types and performance trade-offs to speed decision-making.

✔

  • Faster decision-making through clear visualizations.
  • Improved alignment between product, engineering and business teams.
  • Reduced analysis effort through standardized dashboards and interpretation guides.

✖

  • Low informativeness with poor data quality or missing context notes.
  • Overhead in maintenance and governance of large dashboard portfolios.
  • Not suitable for deep exploratory analysis without interactive tools.

  • Adoption rate

    Percentage of target users who use the dashboard regularly.

  • Time-to-insight

    Average time for a user to find relevant information.

  • Incorrect metrics

    Count of reported cases where metric definitions or data were incorrect.

On-call dashboard of a SaaS product

A dashboard focused on availability, error rates and user impact for fast incident diagnosis.

Executive KPI overview

Consolidated metrics for management reporting with trend analysis and target indicators.

Conversion analysis dashboard

Interactive dashboard for analyzing funnel data and optimization opportunities.

1

Identify goals and stakeholders, define metrics.

2

Design wireframes and information architecture, gather feedback.

3

Implement prototypes, test, plan governance and rollout.

⚠️ Technical debt & bottlenecks

  • Ad-hoc queries instead of reproducible data pipelines.
  • Hardcoded visualizations without parameterization.
  • Missing tests for metric calculations and data ETL.
Data latencyMetric ownershipRendering performance
  • Using a dashboard as a substitute for deep analysis.
  • Providing management with raw data instead of interpreted KPIs.
  • Exposing sensitive data on dashboards without access control.
  • Standardizing too early before validating user needs.
  • Ignoring data latency for real-time requirements.
  • No clear ownership leads to stale or incorrect dashboards.
Information design and visualization skillsBasic data analysis and metric definition skillsFamiliarity with the chosen dashboard tools
Data quality and consistencyLatency and freshness of dashboardsScalability of data pipelines
  • • Limited query performance with large datasets.
  • • Compliance requirements for data access and PII.
  • • Tool-specific visualization limits.