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Does data storytelling still matter today? Is the concept even relevant in a world where a quick prompt can generate a thousand charts, and AI agents are powerful enough to produce detailed reports in seconds?
The articles we’re highlighting this week answer these questions with a resounding “Yes.” Data-driven stories remain an evergreen tool because they exemplify a distinctly human way of thinking and reasoning. They blend facts, opinion, trends, and intuition into a coherent narrative that empowers teams and organizations to make better decisions and to think in more creative and innovative ways.
Beyond Numbers: How to Humanize Your Data & Analysis
With endless streams of data and an ever-growing arsenal of tools to process and analyze it, practitioners are increasingly aware of the wide gap between the availability of data and its usefulness. Michal Szudejko argues that the way to get back on the right path is through data humanization: a new way of thinking designed “to transform data from a passive spreadsheet into a compelling narrative that moves stakeholders to action.”
What Building My First Dashboard Taught Me About Data Storytelling
The sad dashboard that nobody uses has long been a data science trope. Benjamin Nweke looks beyond the clichés, and unpacks the perennial struggle of making others see what you first saw in in the data. The key? Remembering that empathy for your audience comes first.
Why Storytelling With Data Matters for Business and Data Analysts
For an upbeat roundup of helpful insights, don’t miss Rashi Desai’s reflection on how to “move beyond spreadsheets and charts to frame data in a way that builds stronger business cases, unlocks agility, and drives alignment across teams.”
This Week’s Most-Read Stories
From NumPy to multimodal RAG, don’t miss the articles that made the biggest splash in the past week.
NumPy for Absolute Beginners: A Project-Based Approach to Data Analysis, by Ibrahim Salami
Building a Multimodal RAG That Responds with Text, Images, and Tables from Sources, by Partha Sarkar
How to Evaluate Retrieval Quality in RAG Pipelines (part 2): Mean Reciprocal Rank (MRR) and Average Precision (AP), by Maria Mouschoutzi
Other Recommended Reads
Why not branch out into a few other topics this week? These articles are some of our strongest recent additions.
- Multi-Agent SQL Assistant, Part 2: Building a RAG Manager, by Alle Sravani
- Train a Humanoid Robot with AI and Python, by Mauro Di Pietro
- AI Papers to Read in 2025, by Ygor Serpa
- The Reinforcement Learning Handbook: A Guide to Foundational Questions, by Avishek Biswas
- LLM-Powered Time-Series Analysis, by Sara Nobrega
Meet Our New Authors
We hope you take the time to explore the excellent work from the latest cohort of TDS contributors:
- Mohannad Elhamod challenges the conventional wisdom that more data necessarily equates to better model performance.
- Sam Arrington launched a new statistics-focused series on power analysis in the context of marketing.
We love publishing articles from new authors, so if you’ve recently written an interesting project walkthrough, tutorial, or theoretical reflection on any of our core topics, why not share it with us?
We’d Love Your Feedback, Authors!
Are you an existing TDS author? We invite you to fill out a 5-minute survey so we can improve the publishing process for all contributors.
