What are the top ways to evaluate your data storytelling skills?

📊 Top 5 Ways to Evaluate Data Storytelling Skills in Data Scientists: Clarity: Can they explain complex data simply? Narrative: Do they structure their analysis with a compelling story? Visualization: Are their visuals clear and supportive of the narrative? Audience Adaptation: Can they tailor their story for different groups? Actionable Insights: Do they provide insights that 💡drive decisions💡?

1. Clarity and Simplicity: • Communicate complex concepts clearly. 2. Audience Engagement: • Capture and maintain audience interest. 3. Relevance to Audience: • Tailor the story to audience needs and interests. 4. Use of Visuals: • Incorporate compelling visuals for better understanding. 5. Narrative Flow: • Build a logical and engaging story sequence. 6. Data Interpretation: • Provide meaningful interpretations and insights. 7. Story Impact: • Influence decision-making or understanding. 8. Story Consistency: • Ensure consistency for credibility. 9. Emotional Connection: • Create a relatable and memorable story. 10. Handling Questions: • Articulate responses clearly during discussions. 11. Feedback Solicitation: • Seek and act on audience f

Give a glimpse of what you're going to present and what they are going to get at the very beginning in an attractive manner to grab the attention of the audience quickly. This will help you to develop a narrative in your presentation and keep them engaged.

Keep it simple, practice, practice and guess what... practice. Consider your structure and cover your who, what, why, when and most importantly - your how.

The data you show is important, but it is even more important the narrative you use to explain it. Choose a story that your audience will feel familiar and go fluently through it. This will allow the audience to be engaged emotionally to your story. After all, in the world of data storytelling, it's not just about the numbers—it's about the stories we tell with them.

It is important to develop a Storytelling Framework and narrate your story around it. Representing it in a logical flow along with real world example will ensure to draw audience attention and make it more relatable.

Exemplo para Introdução: Comece apresentando o cenário da história, como uma empresa, uma comunidade, etc. Narre o problema que você está investigando - pode ser algo como uma queda nas vendas, um aumento no estresse. Exemplo para Corpo: Mostre os dados que você coletou para investigar o problema - por meio de gráficos, tabelas ou imagens - e analise-os para identificar tendências e padrões. Isso deve ajudar a explicar o problema e a identificar possíveis soluções. Exemplo para Conclusão: A conclusão deve resumir os principais pontos da narrativa. Minha recomendação é fornecer ações futuras baseadas na análise dos dados.

Line Chart/Bar Chart: Ideal for showcasing trends over time. Pie Chart/Histogram: Useful for illustrating proportions or distributions. Scatter Plot/Heat Map: Effective for revealing relationships or correlations.

Review and Self-Assessment: Review your data story against predefined goals and metrics, ensuring alignment with your narrative structure and the appropriate use of visualizations. Verify that it effectively communicates the intended message. Colleague and Peer Feedback: Seek constructive feedback from colleagues or peers. Their insights can offer diverse perspectives, enhancing the clarity and impact of your data story.

Testing your story telling is crucial. The best way to do it, is communicating your presentation to different stakeholders with different backgrounds and technical levels. Making sure your story telling brought them the information they needed out of it is the way to know it is efficient and clear.

To refine your data storytelling skills, learning from exemplary sources is paramount. Books like "Storytelling with Data" by Cole Nussbaumer Knaflic, "Data Story" by Nancy Duarte, and "The Truthful Art" by Alberto Cairo provide insightful frameworks. Explore blogs like Storytelling with Data, The Pudding, or FlowingData, offering diverse perspectives. Engage with podcasts like Data Stories, Storytelling with Data, or Data Skeptic for expert discussions and interviews. Learning from these reputable sources enriches your understanding, inspiring you to create compelling and effective data stories.

Establishing SMART Goals along with the above details would be helpful Specific: Clearly define what you want to achieve. Measurable: Identify metrics to gauge progress and success. Achievable: Set realistic and attainable goals. Relevant: Ensure goals align with the business problem. Time-Bound: Set a timeline for achieving your goals.

Gather feedback from individuals that are consider topic or subject matter experts to support the refinement of the technical aspects of the data story. In addition, gather feedback from a diverse collection of individuals that have some and no connect to the topic. This helps gauge if the story is memorable, impactful, and consumable. For me, I use my 13 year old son and then follow up with him 3 days later to see if he can recall anything I shared.