On Being a Data Skeptic

by Cathy O'Neil

"Data is here, it's growing, and it's powerful." Author Cathy O'Neil argues that the right approach to data is skeptical, not cynical--it understands that, while powerful, data science tools often fail. Data is nuanced, and "a really excellent skeptic puts the term 'science' into 'data science.'" The big data revolution shouldn't be dismissed as hype, but current data science tools and models shouldn't be hailed as the end-all-be-all, either.

Genres: Nonfiction, Technology, Science, Mathematics, Economics, Social Science

28 pages, Kindle Edition

First published September 30, 2013

About the author

Cathy O’Neil is the author of the bestselling Weapons of Math Destruction, which won the Euler Book Prize and was longlisted for the National Book Award. She received her PhD in mathematics from Harvard and has worked in finance, tech, and academia. She launched the Lede Program for data journalism at Columbia University and recently founded ORCAA, an algorithmic auditing company. O’Neil is a regular contributor to Bloomberg View.

Reviews

[Seth Golbey · Rating 4 out of 5]

Food for thought

For those of us without a doctorate in statistics, this paper is a useful map to the garden path down which decision-makers can be led by flawed quantitative methods. It also makes a case for the proper care and feeding of the data scientists we rely upon to help answer the most vexing questions. It provides a leg to stand on for us who were already skeptical of the reliance on quantitative methods to the exclusion of qualitative analysis but didn't know the right questions to ask.

[Geoff · Rating 5 out of 5]

This is a short read basically about how one should be skeptical, not critical, of data and big data. Something very prevalent in today's society. I really like the thoughts that Cathy put down in this short over view particularly about the importance of framing data and not reading in to what the presenter is trying to convey but looking deeper in to what the data is actually showing.

If you are looking for a quick insight from a very talented, intelligent, individual on the subject of data skepticism I highly recommend this book.

[Gavin · Rating 3 out of 5]

Extremely sane and salutary; along with MacAskill and Gates, this was one of the books I felt worth schematising, to hold its insights close; bullet list forthcoming. She appears to have taken a (book-selling?) pessimistic turn in the years since (but I haven't read that one yet).

[Johnny · Rating 2 out of 5]

After reading Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy this felt like a not so much structured summary of the main points of WMD. I didn’t like the separation in ‘Nerds’ and ‘Business People’ at all and would have been lost too often without knowing the WMD book.

[Ed Barton · Rating 5 out of 5]

A Must Read

Exceptionally written and easy to understand, O’Neil’s book is a must read for anyone building, interpreting or desiring modeling based on data science. The writing is funny, direct and accurate. The lessons are ones we continue to struggle to learn. One of the best reads on the subject I’ve ever picked up. You’ve gotta read this book.

[Patrick · Rating 3 out of 5]

Interesting paper on being cognizant of how big data is being (mis)used. Appreciated the break out between what ‘Nerds’ and ‘Business People’ should focus on to each increase their level of awareness regarding the role they play in using of big data.

[Renato Brazioli · Rating 4 out of 5]

Enlightening!

Must read for Data Science and any (big) data producer/consumer/addict.

It’s a brief summary of other works of the author, it could be a starting point for questions about data.

[Kathy Nealen · Rating 4 out of 5]

Essay on the dangers of too much trust and not enough understanding about big data and how it is used or presented.

[Sean · Rating 2 out of 5]

Rude (refers to people as "nerds", gives condescending advice), not very helpful. 20-page mini-book (paper). Generally advises people to be cautious and skeptical of claims. Gee.

[Cj Jenkins · Rating 5 out of 5]

Cathy O’Neil is simply the best

[Hung Chu · Rating 1 out of 5]

random ideas coerced into a narrative. very poorly planned and written.

[Omayma]

As I am a data skeptic, I like when people touch this topic. I believe she addressed the tension between business people and data scientists/practitioners briefly and concisely, giving recommendations for both.

I liked when she concluded:
"we need to find a place inside business for skepticism. This is a tough job given the VC culture of startups in which one is constantly under the gun to perform, and it's just as hard in the cover-your-ass corporate typical of larger companies"

[Mms Mamdouh Al Shamy · Rating 3 out of 5]

I found this book great from so many perspectives. As the writer described "healthy level of skepticism is good for business and for fruitful creativity." so he kept hinting out issues business folks and nerds to make a common ground between both sides. I enjoyed the language of the book so descriptive.

[Matt · Rating 2 out of 5]

This was clearly just a working paper re-worked as a Kindle release. While decent, the writing was below the caliber of the author's other work. That said, it details some of the logic behind being skeptical of "big data." It's probably best to view it as a sort of first step toward her book Weapons of Math Destruction.

[Michelle Tran · Rating 2 out of 5]

There are a number of good points in this book, but the prose reminds me of highschool...

[Shantanu Sharma · Rating 3 out of 5]

This book provides a very high-level introduction to why being a data skeptic is good. Wider case studies illustrating what a data skeptic should do, would have made it more useful.

[Bobu · Rating 2 out of 5]

Important topic, interesting examples, but presented really unstructured.