« Can't say it better | Main | A paradox of data collection »


Feed You can follow this conversation by subscribing to the comment feed for this post.

Raghuveer Parthasarathy

Though your points -- that data on us are being collected with our knowledge and that it can be used for purposes we may not imagine -- are good ones, your examples have a lot of spin on them. For example: you write,

"Take as an example, you drive through high-risk neighborhoods to and from work each day - if the auto insurer learns that, it will hike your premium. That's not a benefit to you."

You could just as easily write: Take as an example, you drive through *low*-risk neighborhoods to and from work each day; several auto-insurers buy your data, and one realizes that you provide a low risk to them, and offers you a policy with a lower cost. That's a benefit to you! Or: it would be silly for auto insurance cost to be the same for young men as for middle-aged women; if so, the latter would be effectively subsidizing the former. Similarly, other harder-to-collect data reveal previously hidden risks, with which one pool of people is, unknown to them, subsidizing another. For example, data on driving through "dangerous" neighborhoods. (Actually, dangerous neighborhoods aren't that dangerous, but that's a separate issue.)

Similarly: some people actually like "coupons for other items," etc. I'm not one of them, and I opt out of any marketing I can, but it's paranoia to think of this as a nefarious plot, or something that no one considers a benefit.

I think consumerism and marketing have too great an impact on society, and I also would like it to be possible to easily opt out of data collection schemes. Nonetheless, I do think that overblown spin from companies shouldn't be countered with overblown spin on the other side.


RP: It was intentional what I wrote. The case of hiking your premium leads to higher revenues for the insurer while that of lowering your premium leads to foregone revenues which need to be recovered elsewhere. Incentives matter!

Differentiating between young men and middle-aged women does not require data sleaze. It's already been done.

"Coupons for other items" should properly be described as "coupons for things you don't intend to buy" because my argument applies to any item you intend to buy.

I did spend my prior career in marketing analytics, and I'm just giving you what I've seen happen.


A good example would be the use of information about where I obtain fuel for my car. There are then several possibilities.
1. I'm told honestly that there is a cheaper option.
2. I'm told that there is a cheaper option but it is only when it is from a sponsoring company.
3. I get advertisements to buy from somewhere that is more expensive, even though the car knows that it is not in my best interests.
If I own something then I expect it to act in my best interests, so I would only agree to 1. Hopefully the reaction of the public to anything else will make sure that happens.


Ken: Nice example. The problem is the secrecy, hence data sleaze. If the public knows, then the businesses have the incentive to act in their interest. If the chance of exposure is low, there is an incentive to act for one's self-interest.

Verify your Comment

Previewing your Comment

This is only a preview. Your comment has not yet been posted.

Your comment could not be posted. Error type:
Your comment has been posted. Post another comment

The letters and numbers you entered did not match the image. Please try again.

As a final step before posting your comment, enter the letters and numbers you see in the image below. This prevents automated programs from posting comments.

Having trouble reading this image? View an alternate.


Post a comment

Your Information

(Name is required. Email address will not be displayed with the comment.)


Link to Principal Analytics Prep

See our curriculum, instructors. Apply.
Business analytics and data visualization expert. Author and Speaker. Founder of Principal Analytics Prep, MS Applied Analytics at Columbia. See my full bio.

Next Events

Oct: 31 Webinar on Data Visualization, online at JMP

Nov: 1 NYU unCOMMON Salon Public Lecture, New York, NY

Nov: 8 Tufts Gordon Institute: A Conversation with Kaiser Fung, Facebook Live

Nov: 8 Tufts TGI Careers & Networking Night panel, Somerville, MA

Nov: 26 Data Visualization New York Meetup, New York, NY

Nov: 27 NYPL Data Analytics Resume Workshop, New York, NY

Nov: 30 Purdue School of Engineering Seminar, West Lafayette, IN

Dec: 1 Purdue Mathematics, Data Science, and Industry Conference, West Lafayette, IN

Past Events

See here

Future Courses (New York)

Summer: Statistical Reasoning & Numbersense, Principal Analytics Prep (4 weeks)

Summer: Applied Analytics Frameworks & Methods, Columbia (6 weeks)

Junk Charts Blog

Link to junkcharts

Graphics design by Amanda Lee


  • only in Big Data