This article (Evolution of GenAI as the Data Storyteller In 2025) is attributed to Mr. Anurag Sanghai, Principal Solutions Architect, Intellicus (From Kyvos)
In his book “21 Lessons for the 21st Century”, Yuval Noah Harari, the bestselling author, historian and philosopher, asserts that “humans think in stories rather than in numbers and graphs…. we want a story that will explain what reality is all about.”
Data scientists and business analysts have always known this and have applied it by turning raw data into visual narratives that are easy to follow. Storytelling bridges the gap between data and executive action, transforming abstract numbers into relatable insights.
BI tools have done well with building data stories using the power of visualization, statistical analysis and semantics. As organization data grows exponentially, and its nature became more complex, AI is stepping up into the role of assisting analysts in creating these storylines, and even becoming the storyteller itself. This is made possible with the advancements in natural language processing (NLP), machine learning, and generative AI.
Evolution of GenAI and Data Storytelling
Data storytelling is a process that begins with collection and integration of raw data, running it through analytical models, insight generation and culminates in narrative development, visualization and communication. AI, and more specifically Gen AI, lends itself as a valuable asset at each stage. Let us explore some of the ways GenAI augments the progression of data to a context-rich and persuasive story:
Data Preparation: Even though this is the first stage, the quality and integrity of data significantly impacts the insights and the resulting narrative. This stage is extremely resource, time and effort intensive, involving tasks like integrating data from disparate sources, identifying gaps, correcting errors and normalization.
GenAI streamlines data preparation in a number of ways. For example, it can help in data cleaning by identifying inconsistencies in datasets like duplicate entries, missing values or incorrect formats. It can even learn from historical patterns to intelligently fill out missing values.
It can unify datasets by harmonizing different structures, terminologies or schema mismatches. AI can automatically add metadata, highlighting key attributes, timeframes and relationships between variables, making the data easier to utilize downstream.
Data Visualization: Insights are effectively communicated though visualizations. It is the essential bridge to comprehending the story within the data.
Evolution of GenAI: Data Visualization
Good visualizations require both technical skills and a deep contextual knowledge. GenAI is increasingly playing a role in data visualization by simplifying and enhancing the creation process. It is automating the design of dashboards, choosing the most appropriate and impactful chart types and graphical representation for insights.
Moreover, GenAI can be used to dynamically update the visualization in real time as new data arrives. Users can also interact with these dynamic dashboards, drill down into specific data points or explore “what-if” scenarios – all guided by AI generated insights.
Several tools are also innovating with AR/VR integration with visualization that further enhances the data story, making it come alive with high degree of user interactivity built-in.
Natural Language Summaries: GenAI excels in writing natural language summaries, translating complex analytical insights into comprehensible stories.
For instance, after evaluating sales data Gen AI may produce a summary like- “Revenue increased by 12% in Q3, driven by a 20% rise in online sales and the greatest growth seen in electronics category.” These summaries can also be personalized for different audiences adapting to their needs, outlook and role. For business leaders they may deliver strategic insights, while for operational heads granular details and variances. Additionally, GenAI is also capable of delivering briefs in multiple languages, highly useful for multi-national organizations.
While dashboards just present data, GenAI can deliver narratives that are contextual and not just descriptive. For example, along with reporting a drop in sales, it may add the underlying causes like competitor actions or increased product returns.
Hyper-Personalization in BI: Going Beyond Dashboards
Taking advantage of GenAI capabilities, BI tools have innovated to provide hyper-personalized insights tailored to individual context and usage patterns, and more importantly, their personal data literacy.
Hyper personalization reduces the cognitive load and improves engagement with data. Resulting in heighted trust on the insights, it improves business value that BI tools deliver by catalyzing faster, data backed decisions.
In order to deliver hyper-personalization, AI tools are trained to understand the user roles and their individual KRAs. Further, they identify historical usage of each individual, like their preferences, frequently accessed metrics and decision-making patterns. These inputs are leveraged to customize the reports with insights that provide advice that is relevant to them.
Moreover, the recommendations from BI tools are designed to be highly context-aware, taking advantage of complex data that map seasonal trends, market conditions and even recent organizational policy changes.
When delivered in natural language, as discussed earlier, hyper-personalization notches up the efficacy of data storytelling further. It can also be trained to not just narrate the story but provide actionable recommendations. For instance, it can notify a marketing manager to say “Your email campaign’s click-through rate dropped by 15% compared to last month. Consider testing a new subject line or adjusting your target audience” or alert a purchase manager with “Inventory levels for Product X are critically low. Based on current sales velocity, restock immediately to avoid revenue loss.”
Evolution of GenAI: Trends to Become the Norm
Considering its transformative promise, integration of GenAI into BI tools is rapidly turning from being a trend to being the norm. These AI assistants are designed to understand users, anticipate their needs, be interactive and deliver insights and recommendations that drive strategic success. They are poised to evolve from just being the means for analyzing and reporting data to becoming personal data assistants to business leaders.
About Anurag Sanghai
Anurag is a dynamic and seasoned IT professional with over 14 years of experience in consulting, designing, and delivering cutting-edge data analytics and business intelligence solutions to clients across the globe. Throughout his career, he has excelled in diverse roles such as Full Stack Engineer, Data Architect, Engineering Manager, and Solutions Architect. His expertise spans the development of enterprise data warehouses, advanced analytics, trend and forecasting systems, and data visualization projects. A trailblazer in innovation, Anurag holds a US patent for his groundbreaking work. He earned his Bachelor of Engineering with honors from the University of Technology, Madhya Pradesh.
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