In domains such as healthcare, defense, or operations, decisions often hinge on a rapid, accurate understanding of dynamic situations. Visual analytics (VA) bridges this need by transforming raw data from repositories, sensors, or machine learning models into clear, glanceable visual displays. The core idea is to consolidate essential information on a single screen (or cohesive view) so users can monitor, explore, and comprehend it quickly.

The true power of VA lies in shifting from tedious "hunting and gathering" of data to an adaptable, flowing presentation of insights. This enables faster pattern detection, deeper understanding, and enhanced situation awareness (SA), which includes the perception of environmental elements, their comprehension, and the projection of future states. Strong SA directly fuels better decision-making and performance.

Two foundational pillars that guide effective VA design are user-centered design (UCD) and situation awareness (SA).

User-Centered Design: Reducing Friction Between People and Data

UCD keeps the human user front and center throughout the process. Rather than starting with flashy visuals or tech capabilities, design flows from the inside out, rooted in the specific people who will use the system and the contexts in which they operate.

The iterative UCD cycle typically includes:

• Understanding the user's context and requirements (through interviews, observations, task analysis, etc.).
• Designing potential solutions.
• Evaluating prototypes with real users and incorporating feedback into the design.
• Refining in subsequent iterations until the usability meets expectations.

This approach minimizes friction, making the visualization feel intuitive and reducing the cognitive effort required to access and interpret data. At POMIET, this aligns closely with our Integrative Design Process, which emphasizes early and ongoing user involvement to create highly usable, accessible tools.

Situation Awareness: Keeping Goals and Cognitive Realities in Focus

Humans are inherently goal-oriented. Effective VA must deliver the right information to support those goals, organized in ways that align with how people think and decide. A goal-directed task analysis helps identify:

• What data users need to perceive.
• How to structure and prioritize it relative to objectives.
• Useful projections or forecasts to anticipate results or next steps.

Situation awareness supports key cognitive processes. As decision-making expert Gary Klein has noted in related work, once people have the right information framed correctly, decision-making itself becomes much easier - the real challenge is achieving accurate perception and comprehension in the first place.

A simple yet powerful model of SA (inspired by established frameworks like Mica Endsley's) captures this flow:

1. Perception of the current environment state (presented via the visualization).
2. Comprehension leading to understanding.
3. Projection of near-future status.
4. Decision, including "no action" as a valid choice.
5. An action that may change the environment and update the display.

To make this model work in practice, VA designers should keep three key principles in mind:

• Recognition is easy; recall is hard: As Jeff Johnson explains in Designing with the Mind in Mind, human memory favors recognizing familiar patterns over recalling details from scratch. Shift cognitive load toward recognition: deliver timely, accurate, contextually relevant information so users spot what's important rather than straining to remember or search for it.
• Standardization is important: Consistent visual languages (colors, icons, layouts, templates) reduce confusion, eliminate learning curves across tools, save authoring time, and reinforce branding. Users can scan multiple dashboards effortlessly, focusing on content rather than page layout.
• Limited focus is detrimental to decision making: In complex displays, fixation on one area can blind users to others, leading to outdated or incomplete awareness. Support global scanning with subtle cues (e.g., gentle animations, highlights, or peripheral indicators) to maintain broad, up-to-date SA without overwhelming attention.

VA differs from many applications in that, while task-oriented software often drives toward a specific goal, VA users are curious. A VA app should prioritize exploration and awareness to inform decisions. It should enable user control through filters, sorting, searching, historical views, and manual overrides, and encourage users not simply be passive recipients of auto-updating data. They should be able to revisit information, drill down, or reframe as needed.

At POMIET, we see visual analytics as a prime opportunity for human-machine teaming. Machines excel at processing vast data and surfacing patterns; humans bring context, goals, intuition, and ethical judgment. By including UCD in SA systems design, we design visualizations that augment rather than overload cognition, turning data into insight while respecting human constraints. This leads to faster, more confident decisions in complex, dynamic environments.