Cookies help us display personalized product recommendations and ensure you have great shopping experience.

We use cookies, including third-party cookies from Google to serve personalized ads through AdSense, to operate this site and understand how it is used. By continuing to browse, you accept this use. See our Privacy Policy and Terms of Use for details, including how to opt out of personalized advertising.
Accept
SmartData CollectiveSmartData Collective
  • Analytics
    AnalyticsShow More
    chatgpt image jul 21, 2026, 04 34 30 pm
    4 Core Benefits of Predictive Maintenance after Vibration Analysis
    10 Min Read
    How Does Data Mining Boost Customer Satisfaction in Logistics? Harnessing Analytics for Results -- AI-generated illustration
    How Does Data Mining Boost Customer Satisfaction in Logistics? Harnessing Analytics for Results
    11 Min Read
    chatgpt image jul 13, 2026, 04 23 45 pm
    How Data Analytics Helps Companies Improve User Engagement
    19 Min Read
    chatgpt image jul 13, 2026, 03 59 46 pm
    How Data Analytics Improves Multi-Location Search Strategies
    10 Min Read
    cybersecurity efforts
    How Behavioral Analytics and AI Are Redefining Cybersecurity for Boca Raton Businesses
    14 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-25 SmartData Collective. All Rights Reserved.
Reading: AmazonFail = TaxonomyFail?
Share
Notification
Font ResizerAa
SmartData CollectiveSmartData Collective
Font ResizerAa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Uncategorized > AmazonFail = TaxonomyFail?
Uncategorized

AmazonFail = TaxonomyFail?

Daniel Tunkelang
Daniel Tunkelang
3 Min Read
SHARE

By now, #amazonfail seems like old news (yesterday’s detwitus?), though apparently Amazon’s PR folks are still doing damage control.

But what intrigues me was something in Clay Shirky’s nostra culpa post comparing the collective outrage against Amazon to the Tawana Brawley incident. While the post on a whole did not move me (perhaps because I don’t have any guilt to atone for), I did see a valuable nugget:

The problems they have with labeling and handling contested categories is a problem with all categorization systems since the world began. Metadata is worldview; sorting is a political act. Amazon would love to avoid those problems if they could – who needs the tsouris? — but they can’t. No one gets cataloging “right” in any perfect sense, and no algorithm returns the “correct” results. We know that, because we see it every day, in every large-scale system we use. No set of labels or algorithms solves anything once and for all; any working system for showing data to the user is a bag of optimizations and tradeoffs that are a lot worse than some Platonic ideal, but a lot better than nothing.

Indeed, perhaps the problem is that Amazon relies too mu…

More Read

Python Programs for Non-Python People
ReadWriteWeb Interview With Tim Berners-Lee, Part 2: Search…
REvolution R coming to Ubuntu
Cloudy days
ESPC Sets Deadline to Require MD5 Hash Encryption

By now, #amazonfail seems like old news (yesterday’s detwitus?), though apparently Amazon’s PR folks are still doing damage control.

But what intrigues me was something in Clay Shirky’s nostra culpa post comparing the collective outrage against Amazon to the Tawana Brawley incident. While the post on a whole did not move me (perhaps because I don’t have any guilt to atone for), I did see a valuable nugget:

The problems they have with labeling and handling contested categories is a problem with all categorization systems since the world began. Metadata is worldview; sorting is a political act. Amazon would love to avoid those problems if they could – who needs the tsouris? — but they can’t. No one gets cataloging “right” in any perfect sense, and no algorithm returns the “correct” results. We know that, because we see it every day, in every large-scale system we use. No set of labels or algorithms solves anything once and for all; any working system for showing data to the user is a bag of optimizations and tradeoffs that are a lot worse than some Platonic ideal, but a lot better than nothing.

Indeed, perhaps the problem is that Amazon relies too much on algorithmic cleverness when it should be taking a more transparent HCIR approach. Perhaps not what Shirky was after, but it’s consistent with all of the versions I’ve heard of what went wrong.

Link to original post

Share This Article
Facebook Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

Best Age Estimation Software in 2026: Which Facial Age Providers Actually Hold Up -- AI-generated illustration
Best Age Estimation Software in 2026: Which Facial Age Providers Actually Hold Up
Artificial Intelligence Exclusive Machine Learning
Top 8 Multi-Cloud Architecture Tools for Automated Infrastructure Design in 2026 -- AI-generated illustration
Top 8 Multi-Cloud Architecture Tools for Automated Infrastructure Design in 2026
Cloud Computing Exclusive IT
6 Top Agentic SDLC Platforms for Enterprise Engineering Organizations -- AI-generated illustration
6 Top Agentic SDLC Platforms for Enterprise Engineering Organizations
Artificial Intelligence Exclusive
6 Best Runtime Intelligence Tools for Debugging AI-Generated Code in 2026 -- AI-generated illustration
6 Best Runtime Intelligence Tools for Debugging AI-Generated Code in 2026
Artificial Intelligence Exclusive

Stay Connected

1.2KFollowersLike
33.7KFollowersFollow
222FollowersPin

You Might also Like

Plato’s cave

0 Min Read

Which BI Analytics Tool Does My Company Need?

13 Min Read

Korean wireless chief warns of data overload

4 Min Read

Analogue Business with a Digital Facade

8 Min Read

SmartData Collective is one of the largest & trusted community covering technical content about Big Data, BI, Cloud, Analytics, Artificial Intelligence, IoT & more.

AI chatbots
AI Chatbots Can Help Retailers Convert Live Broadcast Viewers into Sales!
Chatbots
ai is improving the safety of cars
From Bolts to Bots: How AI Is Fortifying the Automotive Industry
Artificial Intelligence

Quick Link

  • About
  • Contact
  • Privacy
Follow US
© 2008-26 SmartData Collective. All Rights Reserved.
Welcome Back!

Sign in to your account

Username or Email Address
Password

Lost your password?