Data Storytelling with Altair and AI

Data storytelling combines narrative and data to engage audiences, inspire action, and make insights more understandable and memorable. [1] [2]

The DIKW pyramid guides the transformation from raw data to information, knowledge, and finally wisdom, ensuring a complete and actionable story. [1] [2]

Generative AI tools automate the creation of text, images, and code, enhancing and streamlining the process of building engaging data stories. [1] [2]

Prompt engineering tailors AI outputs by specifying roles, audiences, topics, and types, resulting in more targeted and impactful narratives. [1]

Strategies like identifying connections, coincidences, curiosity, and contradictions help uncover compelling story elements from data. [1]

Emotions, conveyed through color, size, interactivity, and images, make data stories more memorable and engaging for audiences. [1]

By adjusting the level of detail, context, and visualization style, data stories can be made accessible and relevant to each audience type. [1]

Modern tools like Streamlit, Tableau, Power BI, and Comet enable interactive, accessible, and visually appealing publication of data stories. [1] [2]

Issues like AI hallucination, bias, and copyright must be addressed, following guidelines such as those from UNESCO to ensure fairness and accuracy. [1] [2]

Case studies include decision-making in sports funding, sales trends in aquaculture, and data journalism on population growth, demonstrating practical applications. [1] [2] [3] [4]