How do you show risk in data visualizations?

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Risk is an inherent part of decision making, especially in uncertain and complex situations. Data visualization can help you communicate and understand the potential outcomes, probabilities, and trade-offs of different choices. But how do you show risk in data visualizations effectively? Here are some tips and examples to guide you.

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Interactive scenarios:

Utilize dynamic tools like sliders or animations to simulate risk scenarios. This allows decision-makers to visually explore and understand the impact of different choices over time.
User testing feedback:

Before finalizing a visualization, conduct user tests and gather feedback to ensure the representation effectively communicates risk and meets audience needs.

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Vitor David B.

Data Analyst
Tobi Oladimeji

Building @ Data Fellows || On a mission…

1 Use appropriate charts

Not all charts are suitable for showing risk. Some common pitfalls are using pie charts, 3D effects, or misleading scales that distort the proportions or comparisons of data. Instead, opt for charts that can display distributions, ranges, or confidence intervals, such as histograms, box plots, or error bars. These charts can help you show the variability and uncertainty of data, as well as the central tendency and outliers.

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2 Use colors and shapes wisely

Colors and shapes can enhance or hinder your data visualization, depending on how you use them. For showing risk, you want to use colors and shapes that can convey the magnitude, direction, and impact of risk. For example, you can use red and green to indicate negative and positive outcomes, or use darker shades to indicate higher probabilities. You can also use shapes such as arrows, circles, or icons to indicate the direction, size, or type of risk.

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3 Use scenarios and simulations

Sometimes, showing risk in data visualization requires more than just static charts. You may want to use scenarios and simulations to show how risk changes over time, or how different actions affect the outcomes. Scenarios and simulations can help you explore the possible futures, test the assumptions, and compare the alternatives. You can use tools such as sliders, buttons, or animations to create interactive and dynamic data visualizations that show risk.

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4 Use annotations and narratives

Data visualization alone may not be enough to show risk clearly and convincingly. You may need to use annotations and narratives to provide context, explanation, and guidance for your audience. Annotations and narratives can help you highlight the key points, clarify the assumptions, and suggest the implications of your data visualization. You can use text, labels, titles, legends, or captions to add annotations and narratives to your data visualization.

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5 Use ethical and honest practices

Showing risk in data visualization is not only a technical but also an ethical challenge. You have a responsibility to present the data accurately, fairly, and transparently, without manipulating or misleading your audience. You should use ethical and honest practices such as citing the sources, disclosing the limitations, and avoiding cherry-picking or exaggerating the data. You should also respect the privacy and confidentiality of the data subjects, and avoid causing harm or distress to your audience.

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6 Use feedback and evaluation

Finally, showing risk in data visualization is not a one-time process, but a continuous improvement cycle. You should use feedback and evaluation to assess the effectiveness, usability, and impact of your data visualization. You should seek input from your audience, stakeholders, and experts, and test your data visualization in different settings and scenarios. You should also measure the outcomes, such as the decisions made, the actions taken, or the changes achieved, based on your data visualization.

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Tobi Oladimeji

Building @ Data Fellows || On a mission to make data-driven decision-making accessible to small businesses || Python || SQL || PowerBI
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In my view, gauging the success of a data visualization lies in subjecting the prototype to user testing. This step is vital to confirm that the visualization effectively serves its intended purpose. Both prior to and subsequent to this, valuable feedback can be collected through various means such as questionnaires, surveys, and interviews. Personally, when developing a Power BI dashboard for a client, I'd prepare a comprehensive questionnaire to solicit insights about user requirements. Once the dashboard is complete, I'd actively seek and incorporate user input to ensure alignment with their needs and expectations. This iterative process, guided by user feedback, is instrumental in refining and enhancing the data visualization's impact.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Vitor David B.

Data Analyst
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I feel that it is sometimes difficult to quantify the risks. For example, how likely is a failure to communicate to the public to trigger a lawsuit against the company? In this case, a presentational approach is to lay it out plainly, with a follow-up recommendation by the legal department. Other cases are where the risk is measurable, such as the likelihood that a project will be late. And here it is possible to graph this probability with the bottlenecks that may be responsible for this delay. A decision tree can be used if the intention is to point out the roots that can branch into different possible risks. But in all cases the risk must be clear to the decision maker.

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Ranjan Segu

I am a passionate market researcher with more than 10 years of experience in analyzing data and providing actionable insights
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Data visualization depends on the kind of risks that needs to be presented. Can it be quantified? Also what would be the risk apetite, the lower and upper limit. Also many times risk is a perception. Is the risk qualitative or quantitative? Risk of loosing sales/customers/share is quantitative. Risk of loosing reputation of a brand can be qualitative.

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How do you show risk in data visualizations?

1

2

3

4

5

6

7

1 Use appropriate charts

2 Use colors and shapes wisely

3 Use scenarios and simulations

4 Use annotations and narratives

5 Use ethical and honest practices

6 Use feedback and evaluation

7 Here’s what else to consider

Data Visualization

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How do you show risk in data visualizations?

1

2

3

4

5

6

7

1 Use appropriate charts

2 Use colors and shapes wisely

3 Use scenarios and simulations

4 Use annotations and narratives

5 Use ethical and honest practices

6 Use feedback and evaluation

7 Here’s what else to consider

Data Visualization

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