What is Data Storytelling?

Definition

Data Storytelling is a method for presenting data using a combination of visual and verbal techniques that are presented as a storyline where the story explains the context of the data, highlights key insights and may also present implications and recommendations.

Purpose

The purpose of Data Storytelling is to improve data insights awareness, recognition and cognition, including providing additional context to the data, highlighting key insights and potentially offering additional narrative regarding indications, implications and recommendations. Importantly, data storytelling needs to include the data! And it should also include a narrative / storyline, visuals and where relevant, voice, video and animation.

Principles to Consider when Implementing Data Storytelling

Every data tale should follow the basic principles of good storytelling: to have a beginning, middle and ending. The purpose is to be concise, eliminate confusion and bias in order to improve engagement, cognition and when appropriate, pursue a call to action.  Basic principles of storytelling are as follows:

• Determine the key insights / point that you intend to make: simple and compelling
• Understand your audience and their familiarity with the data / content 
• Provide context: purpose, background, description and exploration
• Tell the complete story – focus on key message: why it’s important and what it means, using method such as purpose, context, key message, implication and call to action
• Reveal the data at several levels of detail, from a broad overview to the fine structure
• Closely integrate the presentation elements – data, visuals, voice, video / animation
• Consider motivations necessary to induce actions – what data / visual / storyline will cause the audience to act? What key fact(s) will the audience respond to the most?
• Consider providing a summary or recap to ensure cognition

Remember, data storytelling is about going beyond the facts, to engaging the audience regarding what understanding / “take-away” or emotion or action they should take. Data storytelling should lead to better understanding and action.

Primary Uses of Data Storytelling

Data Storytelling has many uses and there are many methods for viewing and presenting data in story form. Data storytelling is best used for the following situations:

• Audience needs to clearly understand what the data is telling
• Audience may not be aware of the the data or the implications indicated 
• Data or insights may be new / unfamiliar to the user 
• Data may be complicated or confusing without further explanation
• Decision and / or action needs to be taken / is recommended
• Audience may not agree to the recommended action to be taken
• Audience may take an adverse action in the absence of viewing the key information  

Types of Data Storytelling Storylines 

• Changing /Trending
• Drilling Down
• Drilling Up
• Comparisons: Similarities and Contrasts
• Intersections
• Patterns (e.g. 80/20 rule)
• Segment / Dissect the elements
• Aggregate the elements
• Anomalies / Outliers

Key Business Benefits of Data Storytelling

The main benefit of Data Storytelling is improved understanding of what the data is indicating in terms of context, core message / importance and implication – what the data is telling the user to understand, decide / plan and act.

Data Storytelling Trends

Data storytelling is evolving beyond static charts into an immersive, AI-driven discipline that bridges analytics and human decision-making. As organizations strive to make data accessible and actionable, these trends are reshaping how insights are communicated:

• AI Automation and Narrative Generation – Artificial intelligence now crafts data-driven narratives by analyzing datasets, identifying patterns, and suggesting visualization formats. Tools auto-generate executive summaries and highlight anomalies, reducing manual analysis while maintaining contextual relevance. This shift enables teams to focus on strategic interpretation rather than repetitive tasks.
• Democratization Through Self-Service Tools – Low-code platforms empower non-technical users to build and share data stories without IT dependency. Unified metrics layers ensure consistency across departments, preventing fragmented interpretations of KPIs like revenue growth or customer retention.
• Ethical Storytelling and Transparency – With rising skepticism toward data sources, organizations prioritize clear sourcing and bias mitigation. Automated lineage tracking documents data origins, while plain-language explanations accompany visualizations to build trust with stakeholders.
• Personalized and Context-Aware Insights – Machine learning tailors data stories to audience roles, industries, or geographic contexts. Sales teams receive region-specific performance analyses, while executives see high-level strategic summaries, all derived from the same underlying datasets.
• Immersive AR/VR Experiences – Augmented and virtual reality transform data into interactive 3D environments. Users explore spatial representations of supply chains or climate models, improving comprehension of complex relationships. These immersive tools are particularly impactful for training simulations and real-time operational dashboards.

A semantic layer accelerates these trends by providing governed, real-time access to metrics across platforms like Tableau and Power BI. Its unified definitions ensure storytelling consistency, while AI-ready data pipelines enable predictive insights.

“Moreover, with self-service BI capabilities, teams can easily access the insights they need to build narratives that drive business impact,” says Dave Mariani, AtScale Co-Founder and CTO. “When combined with strong data literacy, this approach can transform how organizations leverage data, turning insights into actions that drive measurable ROI,” he adds.

AtScale and Data Storytelling

The AtScale semantic layer platform improves data storytelling and visualization by enabling data queries and the results, including visualizations to be rendered faster via automated query optimization. The semantic layer enables the development of a unified business-driven data model that defines what data can be used, including supporting specific queries that generate data for visualization. This enables ease of tracking and auditing, and ensures that all aspects of how data are defined, queried and rendered across multiple dimensions, entities, attributes and metrics, including the source data and queries made to develop output for reporting, analysis and analytics are known and tracked.