The Impact Of AI And Other Innovations On Data Storytelling

Last updated on

Storytelling remains a fundamental aspect of human experience, with people sharing insights and information for millennia using persuasive techniques that endure today.

Yet, the landscape of generating insights and crafting narratives has evolved significantly, driven by advancing technology. This trend is set to continue, empowering us to gather, process, and derive meaning from vast pools of information.

So, what lies ahead for data storytelling?

It’s widely acknowledged that data fuels critical business decisions. The influence of AI and data in shaping this future is undeniable.

Therefore, enhancing one’s data literacy and awareness becomes crucial for making well-informed, evidence-based decisions across various disciplines. This progressive approach defines the future we are collectively striving for and embracing.

Ultimately, it’s about staying relevant and leveraging cutting-edge technology to lead the way.

Sanica Menezes, Head of Customer Analytics, Aviva

The Near Future Scenario

Imagine leveraging a cutting-edge generative AI tool to transform your marketing data dashboards into polished, audience-ready copy. This tool seamlessly synthesizes relevant datasets into a coherent narrative structure, delivering actionable insights tailored to your target audience.

Unlike simplistic AI outputs prone to inaccuracies and generic content, this tool operates at a sophisticated level. It enables collaboration with human authors to create technically robust and compelling content infused with human insights.

By automating the storytelling process from complex datasets, you not only boost efficiency and save time but also empower human co-authors to focus on creative delivery strategies. This approach ensures your message resonates, drives recommendations, and influences decisions effectively.

While the AI tool handles data synthesis and initial content creation, human co-authors play a crucial role in refining prompts, providing expert interpretation, adding nuanced language, and tailoring messages for diverse audiences.

With the implementation of AI tools, human co-authors are liberated from the arduous tasks of data aggregation and analysis. Instead, they can dedicate their efforts to synthesizing insights, refining interpretations, and honing their skills in communication and judgment.

In discussions with industry experts, the consensus is clear: AI profoundly transforms data storytelling by streamlining processes, yet it does not diminish the essential role of human input.

This future of enhanced storytelling capabilities is nearly within reach. Existing tools continue to evolve, promising further advancements and broader availability as I document these developments in my book.

However, the truth is that the skills required to effectively utilize these tools are fundamentally the same as those needed to currently develop, generate, and present compelling data narratives. If anything, the absence of human collaborators underscores the increasing value of acquiring the skills covered in this book.

In the AI storytelling exercise conducted by WIN, the tool identified “80 percent of people are healthy” as its central point. Frankly, this fact isn’t particularly engaging.

On the other hand, humans examining the same data recognized a pattern of rising stress levels, which is far more compelling as a narrative. AI can analyze data in seconds, yet it often requires thorough and insightful prompting to significantly contribute to the storytelling process.

I am more optimistic about AI’s capability to generate 100 slides from the data, which could facilitate identifying the core narrative.

Richard Colwell, CEO, Red C Research & Marketing Group

We recently conducted an experiment using the Inspirient AI platform with an extensive dataset. In just three minutes, it generated 1,000 slides with well-crafted titles and designs.

Moreover, you can pose any question to it, and it can produce tailored presentations of varying lengths—whether 110 slides, 30 slides, or any other quantity. This eliminates the need for people to spend time manually sifting through data in such a manner.

AI is poised to bring about a significant transformation. However, human expertise in contextualization, storytelling, and strategic relevance remains crucial—areas where computers cannot yet compete.

Lucy Davison, Founder And CEO, Keen As Mustard Marketing

Other Innovations Impacting On Data Storytelling

Apart from AI, several other significant trends are expected to influence our approach to data storytelling in the future:

Synthetic Data

Synthetic data refers to data artificially generated through computer simulation, designed to replicate real-world data. It is increasingly utilized in data models either as a supplement to real-world data or as a substitute when real-world data is unavailable. The prevalence of synthetic data is expected to rise in the near future.

According to Gartner (2023), by 2024, approximately 60 percent of the data used to train AI models will be synthetically generated.

Mark Ritson, speaking in Marketing Week (2023), notes that academic studies have demonstrated around 90 percent accuracy for AI-generated consumer data when compared with data sourced directly from human subjects.

This indicates significant potential for synthetic data in crafting data-driven narratives that inform strategies and plans.

Virtual And Augmented Reality

Virtual and augmented reality (VR/AR) are poised to revolutionize data storytelling by offering more immersive and interactive experiences. This technology will enable audiences to enter into story worlds, engage with data, and influence narrative outcomes.

Already prevalent in entertainment, VR/AR blurs the boundaries between traditional linear television and interactive video games, creating a novel form of content consumption.

In data storytelling, envision scenarios where simulated customer interactions occur while navigating websites or retail spaces. Rather than static charts and visualizations, audiences can overlay data onto their physical surroundings and seamlessly integrate data from various sources with a simple touch or gesture.

Transmedia Storytelling

Transmedia storytelling is advancing, with narratives expanding across multiple platforms and media types. Data storytellers are increasingly expected to craft interconnected storylines that traverse various channels, allowing audiences to engage with data stories in diverse ways.

In data journalism, these tools are already at play, integrating embedded audio and video, firsthand eyewitness content, live data streams, data visualizations, and photography alongside traditional editorial commentary and narrative storytelling.

A standout example is the Pulitzer Prize-winning piece “Snow Fall: The Avalanche at Tunnel Creek” (Branch, 2012) from The New York Times, which set a new standard for data storytelling.

In marketing, forward-thinking teams are investing in sophisticated knowledge-sharing portals and embedding tools within their intranet and internet platforms. This approach consolidates multiple media formats into cohesive narratives, enhancing the impact of data storytelling initiatives.

User-Generated Content

User-generated content will wield more influence in data storytelling as social media and online communities continue to expand. Audiences actively engage by creating and sharing stories. Emerging platforms will facilitate collaboration between storytellers and audiences, allowing narratives to be co-created and fostering community around storytelling.

Customizing narratives to cater to individual audience members based on their preferences and emotional state will heighten expectations for personalized data storytelling, thereby boosting engagement and impact.

Shifting from traditional customer communication (“You said, so we did”) to showcasing how feedback is implemented, user-generated content empowers customers to share their experiences and expectations more actively.

These advanced tools complement rather than replace the human creativity and critical thinking essential for effective data storytelling. When used thoughtfully, they can augment data storytelling but cannot replace the need for human input and expertise.

Whether you’re working with Microsoft Excel or utilizing more advanced business intelligence tools like Microsoft Power BI, Tableau, Looker Studio, or Qlik, the next step involves leveraging your skills as a data storyteller to transform these outputs into meaningful insights for your audience.

There are numerous knowledge-sharing platforms available that facilitate the integration of outputs from existing data storytelling tools, streamlining content curation. Some platforms can seamlessly integrate into your existing business environments, such as Confluence.

For more tailored needs, external tools can be used to create customized solutions, such as developing a micro-site for your data story using WordPress. Alternatively, solutions can be scaled to integrate smoothly with established Microsoft or Google tools.

The range of available options is extensive and largely hinges on the IT infrastructure and resources within your organization.

The Continuing Role Of The Human In Data Storytelling

In our ever-changing world, the role of the data storyteller remains indispensable and increasingly vital.

The human data storyteller continues to fulfill crucial roles, especially in influencing and engaging skeptical, discerning, and inundated audiences. The skills required for this task become even more valuable in light of the rapid generation of white papers, marketing copy, internal presentations, and digital content—tasks that exceed human capacity alone. Without a skilled storyteller to curate this flood of information, the risk of overwhelming audiences with information overload becomes inevitable.

Today, the human data storyteller plays a crucial role in several key areas:

  1. Ensuring Relevance and Context: They ensure that stories told are not arbitrary but directly relevant to the business context and requirements.
  2. Ethical and Accurate Use of Data: They understand the data inputs used by tools, addressing limitations, biases, and ensuring ethical use, accuracy, reliability, and proper permissions.
  3. Framing Queries Effectively: They formulate queries that incorporate appropriate context, issues, and meet the needs of the target audience, enriching the knowledge base.
  4. Integrating AI Insights with Human Expertise: They synthesize AI-generated insights or synthetic data with human expertise and domain knowledge, validating the accuracy and relevance of recommendations.
  5. Utilizing Technology Tools: They leverage VR, AR, and transmedia tools judiciously, selecting the most suitable tools for the task at hand.

Original news from SearchEngineJournal