Storytelling with data is the practice of transforming raw numbers into engaging narratives that drive understanding and action.
Storytelling with data has evolved from static dashboards to dynamic, AI-enhanced narratives that inform, persuade, and inspire action. This article explains the concept, the 2025 trends shaping it, and how to apply it effectively.
Storytelling with data combines the analytical rigor of data analysis with the emotional resonance of narrative. It takes structured or unstructured data, extracts insights, and presents them in a story arc—context, conflict, climax, and conclusion—using visuals and narrative techniques. The goal is not simply to show numbers but to communicate meaning so stakeholders understand what the data says, why it matters, and what to do next.
In an era of information overload, numbers without narrative are quickly forgotten. Organizations that excel at data storytelling:
AI copilots, like Galaxy’s SQL copilot, auto-generate narrative annotations, suggest visualizations, and even point out anomalies in real time. These assistants learn from organizational context—metadata, prior queries, and domain lexicons—to tailor stories for each audience.
Stakeholders expect to change parameters on the fly and see the story update instantly. WebGL-powered graphics and progressive web apps deliver high-fidelity experiences previously reserved for native tools.
Reusable metrics and semantic layers ensure that different teams tell consistent stories with the same definitions (e.g., "active user" or "net revenue retention"). Tools such as dbt Metrics and open-source semantic layers standardize business logic.
WCAG-2.2-compliant color palettes, alt text, and screen-reader-optimized structures are becoming baseline requirements. Storytelling platforms embed accessibility checks in their CI/CD pipelines.
With AI creating narratives, organizations must audit for bias and ensure transparency. Expect increased adoption of model cards and data provenance diagrams embedded within stories.
Imagine a SaaS startup wants to explain why revenue plateaued last quarter. Analysts run the query below in Galaxy, use the AI copilot to generate commentary, and embed the resulting chart in a Notion page.
-- Monthly cohort revenue analysis
WITH cohorts AS (
SELECT DATE_TRUNC('month', signup_date) AS cohort_month,
DATE_TRUNC('month', paid_at) AS revenue_month,
SUM(amount) AS mrr
FROM fact_payments
WHERE paid_at >= DATE_TRUNC('year', CURRENT_DATE) - INTERVAL '1 year'
GROUP BY 1, 2
)
SELECT cohort_month,
revenue_month,
mrr,
ROUND(
100.0 * mrr / SUM(mrr) OVER (PARTITION BY cohort_month ORDER BY revenue_month)
,2) AS retention_rate
FROM cohorts
ORDER BY cohort_month, revenue_month;
The AI copilot then suggests:
"Retention slipped for 2023-10 cohorts from 71% to 58% by month 3. Upsell campaigns improved 2023-11 retention to 77%."
Stakeholders immediately see the issue and green-light deeper customer research.
Why it’s wrong: Viewers face cognitive overload.
Fix: Limit each slide/dashboard to one insight; annotate key takeaways.
Why it’s wrong: Erodes trust when stakeholders discover omitted context.
Fix: Show selection criteria and offer links to raw data for auditability.
Why it’s wrong: Without source lineage, reproducibility suffers.
Fix: Store queries in Galaxy, reference commit SHAs, and embed lineage diagrams.
Modern stacks combine ELT tools (Fivetran), warehouses (Snowflake, BigQuery), transformation layers (dbt), and visualization (Mode, Hex). Galaxy sits at the querying layer, acting as an IDE where analysts craft the SQL that underpins every data story. Key advantages:
Storytelling with data in 2025 is a blend of robust data engineering, thoughtful narrative design, and AI-driven assistance. By grounding your stories in clean, well-modeled data and leveraging tools like Galaxy, you can turn raw numbers into compelling calls to action that drive real business results.
Data storytelling converts complex analytics into clear, actionable narratives that drive informed decisions and align diverse stakeholders. As AI and real-time analytics expand, mastering this skill ensures organizations translate data investments into measurable impact.
Traditional reports list numbers, whereas storytelling with data weaves insights into a narrative arc that highlights context, conflict, and resolution, making information memorable and actionable.
A modern stack might include a warehouse (Snowflake), transformation layer (dbt), a SQL editor like Galaxy for querying, and a visualization platform (Mode, Power BI) for presentation.
Galaxy’s context-aware AI copilot speeds up query creation, while Collections let teams endorse canonical queries, ensuring consistent metrics and reliable narratives.
Start by clarifying the business question. From there, gather clean data, draft a storyboard, and iteratively refine both visuals and narrative with stakeholder feedback.