Short answer
Data visualization turns measures and uncertainty into a visual form that helps a defined audience see the intended comparison without being misled.
About Data visualization
Data visualization presents data visually so the intended audience can interpret patterns, comparisons, and uncertainty. It matches visual encoding and context to the decision rather than decorating the data.
Use this competency for
- Roles that create charts, dashboards, or visual explanations used for decisions.
- Work where readers must compare values, see change, or understand uncertainty quickly.
Do not use this competency for
- Use data analysis when the main evidence is how the conclusion was derived rather than how it is represented visually.
Important distinctions
Data analysis
Data analysis develops the conclusion, while data visualization designs how readers perceive and interpret the supporting evidence.
Communication
Communication covers information sharing broadly, while data visualization focuses on accurate visual encoding of data.
Expectations by level
IC1
IC1: Clear charts
Creates straightforward visualizations for a known audience and question with guidance. Uses appropriate scales, labels, and comparisons and checks the rendered values.
Observable behaviors
- Selects a chart type that matches the requested comparison.
- Labels measures, units, time windows, and filters in the visual.
- Checks plotted values against the source result before sharing.
Examples
- Replaces a crowded pie chart with sorted bars so category differences can be compared.
- Adds the reporting period and sample count to a trend chart before it is used in a team review.
IC2
IC2: Decision-focused views
Independently designs dashboards and visual narratives for ambiguous team decisions. Prioritizes relevant context and tests whether the audience interprets the display correctly.
Observable behaviors
- Chooses visual hierarchy based on the decision and audience needs.
- Shows uncertainty, targets, or baselines when they change interpretation.
- Removes or revises encodings that create misleading comparisons.
Examples
- Builds an operations view that separates backlog size, age, and inflow instead of combining them into one score.
- For a forecast review, displays ranges and prior accuracy so readers do not treat a point estimate as certain.
IC3
IC3: Visualization systems
Sets visualization direction for products or reporting used across teams. Resolves competing audience needs and reviews shared patterns for accurate interpretation.
Observable behaviors
- Defines reusable standards for scales, color, annotation, and uncertainty.
- Evaluates shared dashboards with representative users and corrects recurring misinterpretations.
- Reviews high-impact visuals for omitted context and distorted comparisons.
Examples
- Standardizes metric cards and time comparisons across executive and team dashboards while preserving audience-specific detail.
- Redesigns a public-facing result display after testing shows readers confuse percentage points with percent change.