As of today, analysts devote considerable time crafting stories with visualizations and narratives in PowerPoint. Some data visualization tools offer features that help create data stories manually within the visualization tools. Whether the data stories reside in PowerPoint or data visualization tools, the story-creation process is still manual and time-consuming. Storytelling will always be essential to influencing business stakeholders, but the question remains: Can AI automate the generation of data stories?
With progress in generative AI (GenAI), it’s possible to create indonesia whatsapp number data complex visualizations from data [1] automatically. Also, NLG data-to-text is a prime use case of NLG [2] [3]. Looking ahead, I expect AI-generated analyses to be communicated through data stories consisting of GenAI-created visualizations and NLG-generated narratives. In the early stages of AI-driven analytics, human analysts must vet, refine, and curate data stories before they can be sent to business stakeholders. However, as AI models are trained better, the entire business intelligence workflow from monitoring, analysis, and storytelling can be automated, allowing humans to consume data stories and take appropriate actions.
In my work at an investment bank, I could achieve analysis user experience (UX), where algorithmically done analysis is integrated with a scorecard-based visual analytics solution. So, the machine learning algorithm automatically does the analysis and alerts users. Users can click on the alert to see the underlying visualization and analyze it further. This paradigm of enabling analysis can be revolutionary for organizations as analysts do not have to analyze themselves; they can review the analysis done by the algorithm. While automatically generating a compelling data story is still a work in progress, I see a lot of promise in this direction with the rapid pace of AI/ML innovation.