They Requested It. I Built It. Nobody Ever Used It.
The article discusses the challenges faced by data professionals when delivering predictive models that go unused. It highlights the importance of explainability in models, especially in healthcare, where stakeholders prefer trusted clinical processes over complex algorithms. Additionally, it emphasizes the need for timely delivery to prevent stakeholders from seeking alternative solutions.
- ▪Data professionals often face the issue of delivering models that are ultimately ignored by stakeholders.
- ▪Explainability is crucial in healthcare, as clinicians prefer trusted methods over complex, opaque models.
- ▪Timely delivery of models is essential, as delays can lead stakeholders to abandon the project in favor of other solutions.
Towards Data Science files mainly under ai. We currently carry 104 of its stories.
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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/they-requested-it-i-built-it-nobody-ever-used-it/ |
| Publication time | Wed, 27 May 2026 12:00:00 +0000 |
| Retrieval time | 2026-05-27T12:07:59.258Z |
| Last seen | 2026-05-27T12:07:59.258Z |
| 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 | BKlkzHHCje-9 |
| 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 Science They Requested It. I Built It. Nobody Ever Used It. Why good data work gets ignored after delivery. Hayden Kastens May 27, 2026 6 min read Share Illustration by Alghozy on Unsplash Stakeholders came to us asking for a model. We built a proof of concept. Got the green light. Delivered the model. Weeks of work…all to hear nothing. It’s a tale as old as time, and one that plagues data professionals everywhere, from analysts to ML engineers. So, what happened? Your Model is a Mystery Our profession is one rooted in modern computer science and technological advancements. Many of the most powerful solutions at our fingertips are ones that would have been too computationally expensive decades ago.
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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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