Definition of Insights

Insight = Information / Knowledge + Context
An insight is validated information that changes understanding and enables decisions or actions.

Introduction

Dashboards and data visualization in general, have a clear goal:
to translate numbers and data into visual representations so that users can easily compare information, quickly recognize patterns, trends, or anomalies, and ultimately derive insights.

To achieve this, charts and key figures are used, while content is designed according to principles of perception psychology through the use of colors, shapes, axes, and interactions.

However, visualization is only one part of the process.
Equally important are a clear understanding of the questions to be answered, the quality of the data, the way information is presented, and the expertise of the viewer in order to successfully generate meaning.

Expressed as a formula, this means:

Note

With the increasing use of artificial intelligence, the way we interact with and design traditional data visualization scenarios will change in the future.
AI will take over tasks such as pattern recognition, deriving meaning, summarizing findings in natural language, and suggesting actions, tasks that have traditionally been the responsibility of the end user.

AI can generate analyses at a scale and speed that would not be possible manually. However, the quality of the results still depends on the quality of every individual step in the insight-generation process. AI cannot compensate for weaknesses, it amplifies what is already there, including both quality and errors.

01 Questions

Every effective visualization begins with clarity of purpose. This requires a precise definition of the target audience and a clear understanding of the specific questions the dashboard is intended to answer.

If clear questions are missing, dashboards risk displaying interesting but non-actionable information or, in the worst case, providing answers to the wrong questions.

A visualization is therefore only as effective as the questions it was designed to address. The quality of these questions determines the focus, content, and overall value of the visualization.

As this topic becomes increasingly important – especially with the growing use of artificial intelligence – it is explored in more detail in a dedicated section of this guideline.

02 Data

In dashboard design, the principle “garbage in, garbage out” applies without exception.
Poor data quality inevitably leads to unreliable results, misleading insights, and ultimately poor decisions.

Data quality is not a technical footnote, but the business foundation of any analysis. Reliable data is relevant, representative, and traceable.
It has a known and documented level of quality, along with a shared understanding of what it measures and what it does not measure. Only on this basis can dashboards build trust and serve as reliable decision-making tools.

What AI Changes Here

Artificial intelligence will increasingly support tasks related to data governance, data quality, and plausibility checks. AI can identify patterns, highlight anomalies, and make quality risks visible.

However, AI cannot create quality that is not present in the source data. On the contrary, existing errors and structural weaknesses are amplified when AI is applied to unreliable data. As a result, analyses may appear professional and convincing, even when they are incomplete or incorrect.

For example, a fragmented data model without clear business context will not produce reliable insights, even with AI support, but rather confident-looking noise.

Therefore, the principle remains unchanged: the care taken in building and maintaining a reliable data foundation is not reduced in the AI context, it becomes more important than ever.

03 Visualizations

Clear visualizations prioritize understanding over completeness. The goal is not to present all available information, but to show what is relevant for comparison, categorization, and decision-making. Consistent terminology, clear labeling, and the intentional omission of unnecessary details reduce cognitive load and make information easier to understand quickly.

Comparisons are central: deviations, trends over time, and differences should be visualized so they are immediately apparent and require no additional interpretation.

A clear visual hierarchy, targeted highlighting, and sufficient white space guide attention toward what matters most and support orientation.

→ Clear visualizations always serve a purpose.

They make visible where action is needed and guide the user from perception to decision.

What AI Changes Here

The ability of artificial intelligence to generate text introduces, for the first time, an explicit verbal interpretation layer to dashboards.

While visualizations primarily present facts, AI-generated text can help reconstruct meaning, identify assumptions, and suggest potential causal relationships or formulate them as hypotheses.

In addition, AI will increasingly be able to recommend the most appropriate visualization type based on recognized data structures and the analytical goal.

04 Validated Interpretation

Once visualizations are used for analysis, the final step remains:
Interpretation means evaluating the relevance and reliability of the displayed information within the business context.

This involves domain experts qualitatively assessing the results, including awareness of potential limitations, such as a small data basis.

The process includes:
Identifying anomalies → examining and evaluating them (remember: “statistically significant” is not the same as “business relevant”) → narrowing down causes where necessary → deriving actions

What AI Changes Here

This step will increasingly become dialogue-driven and exploratory.

At the same time, the principle of “human in the loop” remains essential.

Users contribute their expertise in a targeted way to contextualize, validate, and prioritize the machine’s suggestions and conclusions through interactive exchange.
AI does not replace expert domain interpretation, it supports it by highlighting relevant anomalies, patterns, and potential relationships, thereby reducing analytical complexity.

Conclusion

AI will define a new mode of working between humans and data in the process of generating insights.
Users ask targeted and clearly formulated questions → receive answers in the form of text, key figures, and interpretations → understand relationships → and make informed decisions.

AI can identify patterns, highlight relevant aspects, enable comparisons, outline consequences and assess uncertainties.
The resulting information is condensed and prioritized in textual form.

In this context, visualizations increasingly lose their leading role: they become secondary, situational, and supportive, used not first but afterward. Not as the primary driver, but as confirmation.

As a result, the traditional dashboard evolves into a powerful insight system, with a clear focus on understanding, prioritization, and decision-making.