What are some of the common challenges and pitfalls of data storytelling and how do you overcome them?

1 Lack of clarity and focus

One of the biggest challenges of data storytelling is to have a clear and focused message that aligns with your goal and your audience's needs. A common pitfall is to include too much data, too many details, or too many topics that confuse or distract your audience from the main point. To avoid this, you should start with a clear question or problem statement, and then select and prioritize the most relevant and impactful data points that support your answer or solution. You should also use a logical and coherent structure that guides your audience through your data story, and avoid jumping from one idea to another without a clear connection.

2 Lack of context and relevance

Another challenge of data storytelling is to provide enough context and relevance for your data, so that your audience can understand and relate to it. A common pitfall is to assume that your audience has the same background, knowledge, or perspective as you, and to present your data without explaining its meaning, significance, or implications. To avoid this, you should know your audience and their expectations, and tailor your data story to their level of understanding and interest. You should also use appropriate data visualizations, metaphors, analogies, or examples that help your audience grasp and remember your data.

3 Lack of emotion and engagement

A third challenge of data storytelling is to elicit emotion and engagement from your audience, so that they care about your data and your message. A common pitfall is to rely solely on facts and figures, and to neglect the human or emotional aspects of your data story. To avoid this, you should use storytelling techniques such as narratives, characters, conflicts, resolutions, or surprises that appeal to your audience's emotions and curiosity. You should also use a conversational and personal tone, and invite your audience to participate or interact with your data story.

4 Lack of credibility and trust

A fourth challenge of data storytelling is to establish credibility and trust with your audience, so that they believe in your data and your message. A common pitfall is to present your data without acknowledging its limitations, uncertainties, or biases, or to make claims or recommendations that are not supported by evidence. To avoid this, you should be transparent and honest about your data sources, methods, assumptions, and results, and use data quality checks, validation, or verification techniques to ensure accuracy and reliability. You should also use ethical and responsible practices when collecting, analyzing, and presenting your data, and respect your audience's privacy and confidentiality.

5 Lack of action and impact

A fifth challenge of data storytelling is to inspire action and impact from your audience, so that they act on your data and your message. A common pitfall is to end your data story without a clear call to action, or to leave your audience wondering what to do next or why they should care. To avoid this, you should have a clear purpose and objective for your data story, and communicate the benefits and value of your data and your message for your audience. You should also use persuasive techniques such as urgency, scarcity, social proof, or authority to motivate your audience to take action.

6 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?