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

By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData CollectiveSmartData Collective
  • Analytics
    AnalyticsShow More
    data analytics and truck accident claims
    How Data Analytics Reduces Truck Accidents and Speeds Up Claims
    7 Min Read
    predictive analytics for interior designers
    Interior Designers Boost Profits with Predictive Analytics
    8 Min Read
    image fx (67)
    Improving LinkedIn Ad Strategies with Data Analytics
    9 Min Read
    big data and remote work
    Data Helps Speech-Language Pathologists Deliver Better Results
    6 Min Read
    data driven insights
    How Data-Driven Insights Are Addressing Gaps in Patient Communication and Equity
    8 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

Quality vs. Quantity
Mathew Ingram: Google Helps Newspapers
BI Is Indeed Counter-Cyclical, And Led By SAP BusinessObjects
Making Your “Marketing Marriage” Work!
90s Sites and Stickiness

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

data analytics and truck accident claims
How Data Analytics Reduces Truck Accidents and Speeds Up Claims
Analytics Big Data Exclusive
predictive analytics for interior designers
Interior Designers Boost Profits with Predictive Analytics
Analytics Exclusive Predictive Analytics
big data and cybercrime
Stopping Lateral Movement in a Data-Heavy, Edge-First World
Big Data Exclusive
AI and data mining
What the Rise of AI Web Scrapers Means for Data Teams
Artificial Intelligence Big Data Exclusive

Stay Connected

1.2kFollowersLike
33.7kFollowersFollow
222FollowersPin

You Might also Like

Once Again: Unsubscribe Best Practices

4 Min Read

The Register: IBM iron predicts the future

3 Min Read

Denial of access explained

4 Min Read

Imagine

7 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 in ecommerce
Artificial Intelligence for eCommerce: A Closer Look
Artificial Intelligence
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
© 2008-25 SmartData Collective. All Rights Reserved.
Go to mobile version
Welcome Back!

Sign in to your account

Username or Email Address
Password

Lost your password?