I work in adtech. We actually had to mangle some of our analytics data because it didn't match the broken data a client was used to. Our data was more accurate, but because the numbers didn't match the other analytics suite the client used, so we had to make it worse.

I wish I was joking.

Yeah, but with one more piece of data they'll have solved personalized mattress sales forever. Nevermind that the entity collecting the marketing data is a convenience store chain and doesn't sell their data, allegedly.

The average time on site and bounce rates I have got from advertising my software inside ChatGPT and Reddit are so terrible that I can only assume that most of the clicks I am paying for are fraudulent, although it is not clear who is doing the fraud.

https://successfulsoftware.net/2026/08/13/my-experience-buyi...

https://successfulsoftware.net/2025/08/11/what-i-learned-spe...

A lot of the time you are running experiments and seeing how the data changes, not looking at the data in a vacuum.

Many of the example critiques here don't apply so much when looking at the changes in data:

- if I got 50% more hits on my site this week vs last week, that's meaningful despite 36% of people blocking ads

- if my open rate doubled when I changed my email subject, also meaningful

The other examples are hard to pick holes in as they simply say "Z is a lie", but I can be looking at multiple data sources to decide how much of a lie Z is.

This has been known a long time. Freakanomics did a two part podcast on does advertising actually work. It is long but well worth a listen if you want to hear some science about advertising.

https://freakonomics.com/podcast/does-advertising-actually-w...

The second part goes into internet advertising.

https://freakonomics.com/podcast/does-advertising-actually-w...

There are transcripts of the episodes on the page if you want to read instead of listen.

> Marketers are addicted to bad data

This article cites a random Statista page for the "36% percent of people in the UK use an adblocker" stat.

If it gets you paid, it's good data.

If it makes your clients happy, it's good data.

Business data is for business purposes, not for science purposes.

I dunno. There is a lot of valid and interesting criticism to write about digital marketing. Lots of people have attempted to study the efficacy of digital advertising, and I'd love to see a deep dive into the "MarTech" ecosystem and how shady much of it is. Anyone who has had to work on the go-to-market side of a company knows the general feeling of frustration the author is experiencing.

But this post reads like an aspiring "thought leader" posting a hot take to LinkedIn. It feels like lazy pandering to the "dumb marketers don't math good" crowd.

Here's the same author with a post titled "Marketing and The Modern Data Stack" where he gets very excited about the marketing automation and big data, kicking off the piece with line "There is a huge transformation happening in the data space." and really sells the data-driven future with "In short, you need data, lots of it and it needs to be tightly integrated across the entire customer lifecycle.": https://www.jacquescorbytuech.com/writing/marketing-modern-d...

The biggest spenders in the marketing business are doing so primarily to justify their own size. That is to say, if you're a trillion dollar business, you are going to spend a billion dollars on advertising purely to say that you spent a billion dollars on advertising. The quality of the data - or the clicks - matters less than the fact that you've bought your brand a sense of ubiquity. It's fractally nested corporate classism.

Direct response marketing - i.e. when you are buying ads specifically to get someone to buy a book - is a far smaller fraction of the market. It had a moment in the early 2000s when Google and later Facebook figured out how to extract lots of information about their customers and sell it on to other direct response marketers. But even then, the really lucrative marketers aren't legitimate businesses, they're scammers using the ability to micro-target ads to find their biggest rubes as cheaply and silently as possible.

If you're a regular person trying to buy ads for a legitimate business, you're probably going to get swamped by all of this and taken for multiple rides by several different kinds of scam.

Various dodgy bots and crawlers hammer my website continually. Consequently, my web logs and analytics are garbage. So bad data is all we have, but it's better no data at all.

Everyone is addicted to bad data.

The Red-Green-Refactor pattern is a coping mechanism to deal with the fact that if we want a change to work and the build process tells us we didn't break anything, we bowl right past any subtle hints that we are in the wrong, and our whole code change is a house of cards standing on a bad assumption that will immediately collapse when breathed on.

I have a love-hate relationship with negative tests because of this, and I wonder if there's some way with static analysis or maybe AI to validate that the test that is green because nothing happened isn't green now because I broke the API and the test is now testing nothing in, nothing out instead of something in, nothing out.

Sooner or later in some refactor someone finds a way to break the code without CI catching it.

But we are just people. And if you squint you can see how our relationship to green builds is the same drive that management, sales, and marketing, and scientists get with charts that Make the Numbers Go Up even when the data is just correlated and the proximate cause they were looking for is a hallucination.

Mark Twain knew. Lies, Damned Lies, and Statistics.

Meta is most certainly addicted to bad data.

I believe this because Facebook continues to send spam to an e-mail address that was only used by me for my cat, and only once; and the cat has been dead for 15 years. The dead cat address received three spams from Facebook just yesterday.

There is also bad data out there about another cat that died ten years ago. He keeps getting snail mail from political candidates trying to convince him that they're deeply interested in the cares and concerns of people like him. A dead cat.

What is good data and what is bad data?