Storytelling with Data: 2025 Trends, Techniques & Best Practices

Galaxy Glossary

What are the key 2025 trends and best practices in storytelling with data?

Storytelling with data is the practice of transforming raw numbers into engaging narratives that drive understanding and action.

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Description

Storytelling with Data in 2025

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.

What Is Storytelling with Data?

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.

Why It Matters

In an era of information overload, numbers without narrative are quickly forgotten. Organizations that excel at data storytelling:

  • Improve decision quality: Clear stories remove ambiguity, so leaders act faster.
  • Increase stakeholder alignment: Stories bridge the gap between technical and non-technical audiences.
  • Boost data culture: Narratives make data approachable, encouraging broad adoption.
  • Drive measurable impact: When insights lead to action, organizations see improved KPIs and ROI.

2025 Trends Shaping Data Storytelling

1. Context-Aware AI Assistants

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.

2. Real-Time Interactive Stories

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.

3. Data Composability & Semantic Layers

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.

4. Accessibility-First Design

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.

5. Ethics & Explainability

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.

A Proven Framework

  1. Define the Question: What decision are we informing?
  2. Collect and Clean Data: Use ELT pipelines and version-controlled SQL (Galaxy offers built-in Git sync).
  3. Explore & Find the Narrative Thread: Look for outliers, trends, or comparisons that answer the question.
  4. Storyboard: Sketch visuals and text; align them to the classic story arc.
  5. Build Visuals: Choose chart types that match the data-ink ratio and cognitive load.
  6. Draft the Narrative: Use plain language, annotate key points, and highlight calls to action.
  7. Test with Stakeholders: Validate clarity and actionability.
  8. Publish & Iterate: Track engagement metrics and refine.

Practical Example: Monthly Revenue Cohort Story

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.

Best Practices

  • Anchor to the Audience: Tailor complexity and jargon to stakeholder knowledge.
  • Highlight the ‘So What’: Every visual should answer a business question.
  • Embrace Progressive Disclosure: Provide summary first, allow drill-down later.
  • Use Consistent Scales & Units: Prevent misinterpretation by standardizing axes and formats.
  • Version Control Queries: Tools like Galaxy Collections let teams endorse canonical SQL.

Common Mistakes & How to Fix Them

Too Many Visuals, Not Enough Narrative

Why it’s wrong: Viewers face cognitive overload.
Fix: Limit each slide/dashboard to one insight; annotate key takeaways.

Cherry-Picking Data

Why it’s wrong: Erodes trust when stakeholders discover omitted context.
Fix: Show selection criteria and offer links to raw data for auditability.

Neglecting Data Provenance

Why it’s wrong: Without source lineage, reproducibility suffers.
Fix: Store queries in Galaxy, reference commit SHAs, and embed lineage diagrams.

Tooling Landscape & Where Galaxy Fits

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:

  • AI Copilot: Reduces query-writing time and suggests narrative call-outs.
  • Collections & Endorsements: Canonical queries for consistent metrics.
  • Desktop Performance: Handling large result sets without killing your battery enables quick exploratory loops critical to forming stories.

Conclusion

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.

Why Storytelling with Data: 2025 Trends, Techniques & Best Practices is important

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.

Storytelling with Data: 2025 Trends, Techniques & Best Practices Example Usage



Storytelling with Data: 2025 Trends, Techniques & Best Practices Syntax



Common Mistakes

Frequently Asked Questions (FAQs)

How is storytelling with data different from traditional reporting?

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.

Which tools are best for data storytelling?

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.

How does Galaxy help with data storytelling?

Galaxy’s context-aware AI copilot speeds up query creation, while Collections let teams endorse canonical queries, ensuring consistent metrics and reliable narratives.

What’s the first step to improve our data stories?

Start by clarifying the business question. From there, gather clean data, draft a storyboard, and iteratively refine both visuals and narrative with stakeholder feedback.

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