How can you use data storytelling to communicate uncertainty and risk effectively?

As a data engineer, you know how complex and uncertain data can be. You also know how important it is to communicate your findings and recommendations to stakeholders, clients, and decision-makers. But how can you do that effectively without losing their trust or attention? The answer is data storytelling. Data storytelling is the art and science of using data, visuals, and narratives to convey a message, persuade an audience, and inspire action. In this article, you will learn how to use data storytelling to communicate uncertainty and risk effectively in four steps.

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1 Define your goal

The first step in data storytelling is to define your goal. What do you want your audience to know, feel, or do after reading or listening to your story? Your goal should be clear, specific, and measurable. For example, your goal could be to explain the sources and impacts of uncertainty in your data, to convince your audience to adopt a certain solution or strategy, or to warn them about the potential risks and challenges of ignoring your data. Your goal will guide your choice of data, visuals, and narratives.

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Pratik Vaidya

Market Intelligence : Business & Data Analytics : MBA|MIS
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The 4 steps outlined in the article are iterative. Especially when communicating stories of risk and uncertainty, we must be comfortable with going back to "defining the goal" after first "narrating the data". Sometimes the data shows the need to reevaluate the process and findings: - critical insights to be investigated further in the same line of questioning - context that was first missed and needs to be added to the analysis for proper depth and relevance - a generally accepted pivotal hypothesis is disproven or proven - a more direct way of framing the message emerges

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2 Choose your data

The second step in data storytelling is to choose your data. You should select the most relevant, accurate, and reliable data that supports your goal and message. You should also consider the level of detail, complexity, and uncertainty of your data. You don't want to overwhelm or confuse your audience with too much or too vague data. You want to provide enough context, evidence, and explanation to make your data meaningful and trustworthy. You should also check and clean your data for any errors, outliers, or biases.

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🧭 Omar Awad

Data Analytics @ Microsoft
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Navigating the landscape of data selection in data storytelling requires careful consideration to steer clear of common pitfalls. One frequent mistake is succumbing to the allure of excessive data, inundating your audience with information overload. To avoid this, prioritize relevance and coherence, choosing data points that directly contribute to your narrative and align with your communication goal. Conversely, the pitfall of vagueness arises when data lacks specificity, leaving room for misinterpretation. Striking the right balance involves providing sufficient context and detail to elucidate your story without drowning stakeholders in ambiguity.

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3 Visualize your data

The third step in data storytelling is to visualize your data. You should use appropriate and effective charts, graphs, maps, or other visuals to display your data in a way that highlights your key points, patterns, and trends. You should also use visual cues, such as colors, shapes, sizes, or icons, to emphasize or contrast your data. However, you should also be careful not to mislead or distort your data with misleading scales, axes, or labels. You should also use visual techniques, such as error bars, confidence intervals, or ranges, to show the uncertainty and variability of your data.

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4 Narrate your data

The fourth and final step in data storytelling is to narrate your data. You should use clear and concise language, tone, and style to explain and interpret your data for your audience. You should also use storytelling elements, such as hooks, characters, plots, conflicts, and resolutions, to engage and persuade your audience. You should also use rhetorical devices, such as metaphors, analogies, or anecdotes, to make your data relatable and memorable. However, you should also be honest and transparent about the limitations, assumptions, and implications of your data. You should also acknowledge and address any counterarguments, objections, or questions that your audience might have.

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5 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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How can you use data storytelling to communicate uncertainty and risk effectively?

1

2

3

4

5

1 Define your goal

2 Choose your data

3 Visualize your data

4 Narrate your data

5 Here’s what else to consider

Data Engineering

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How can you use data storytelling to communicate uncertainty and risk effectively?

1

2

3

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5

1 Define your goal

2 Choose your data

3 Visualize your data

4 Narrate your data

5 Here’s what else to consider

Data Engineering

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