How Many Stories Can Your Data Tell?
Data ScienceHow Many Stories Can Your Data Tell?How the way we represent data can change what we think the data is sayingSara A. MetwalliSeptember 30, 20267 min readImage by RDNE Stock project from PexelsAllow me to paint a scene: you get a new dataset and need to explore it, so you don’t change a single number, but you make three different visualizations. If you showed those visualizations to three different people, they'd likely walk away with three slightly different impressions of what the data means.
- ▪Data ScienceHow Many Stories Can Your Data Tell?How the way we represent data can change what we think the data is sayingSara A.
- ▪MetwalliSeptember 30, 20267 min readImage by RDNE Stock project from PexelsAllow me to paint a scene: you get a new dataset and need to explore it, so you don’t change a single number, but you make three different visualizations.
- ▪If you showed those visualizations to three different people, they'd likely walk away with three slightly different impressions of what the data means.
2 outlets in our directory ran this story, first to last over 1 hour. All of the coverage we found sits in one bucket: lean left. That one-sidedness is itself worth noticing.
- ▪ How the Feds Get Your Data — 404 Media
Towards Data Science files mainly under ai. We currently carry 200 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Towards Data Science |
| Canonical URL | https://towardsdatascience.com/how-many-stories-can-your-data-tell/ |
| Publication time | Wed, 30 Sep 2026 15:30:02 GMT |
| Retrieval time | 2026-09-30T15:32:01.917Z |
| Last seen | 2026-09-30T15:32:01.917Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | DkvhrZOoLwJW · 2 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Data ScienceHow Many Stories Can Your Data Tell?How the way we represent data can change what we think the data is sayingSara A. MetwalliSeptember 30, 20267 min readImage by RDNE Stock project from PexelsAllow me to paint a scene: you get a new dataset and need to explore it, so you don’t change a single number, but you make three different visualizations. If you showed those visualizations to three different people, they'd likely walk away with three slightly different impressions of what the data means. That is one of the things I find most fascinating about data visualization.We often talk about visualization as if it were simply the final step in a data-analysis pipeline: collect the data, clean it, analyze it, and then make a nice chart.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Towards Data Science.
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