That's a fair point, and it's conceivable this could work. But if the scrapers have had a legitimate source of truth in your data for a meaningful amount of time before you pull the trigger on this, it will get significantly harder.
In practice I'm talking about data which is very multidimensional and has more nuanced metrics than something like a price point. As an example, let's say a certain company exposes sequentially crawlable online orders without authentication. A scraper is going to reason about the data instead of just accepting it, and it's going to look for relatively minute errors. In particular, if it suddenly pulls in many more orders in a single day, or if a particular product shoots up in popularity, or if the price of an item in inventory suddenly changes from what it was historically, an alert will fire off.
As a real world example, I was working on a project to scrape all orders for a publicly traded fast food company that offered online orders and delivery. In the middle of one quarter, without warning, we suddenly saw activity that looked legitimate, but noticeably different order data occurring in a way that significantly increase revenue projections. My colleagues and I basically didn't trust the data whatsoever until we could find a localized (and not well publicized online) promotion that accounted for the changes.
Basically, I'm saying that it can be done, but it's hard and not a silver bullet. If the data is being used as part of a timeseries, then data meaningful enough to disrupt operations if going to be noticed and manually reviewed to identify a narrative that explains it; if the data is believably false but not meaningful enough to change a trend, it's not really going to matter to them anyway.
Yes, there is a massive amount of crawling that happens in the service of financial forecasting. Satellite imagery, drones, web pages, API endpoints, receipt data (free financial aggregators sell this), location data (free geolocation services sell this), purchase history (free email clients sell this), etc. This rabbit hole goes very deep. Some of it is actively sold by free services for "market research", some of it is collected from sources that don't bother with authentication or access control and make all of their data public.
> Note to self: All things facing the web in ecommerce must be cryptographically randomized. :(
The bigger issue is that sensitive information should be behind authentication. If you require authentication instead of just a "browsewrap" terms and conditions clause at the bottom of the page, that data becomes legally actionable if it's ever found. Otherwise you're relying on (at best) a robots.txt to do the enforcement for you.
Many companies overlook this, even if they're told about it, because it doesn't compromise users or constitute a security vulnerability. So savvy firms take the data as a competitive advantage and use it for market research.
In practice I'm talking about data which is very multidimensional and has more nuanced metrics than something like a price point. As an example, let's say a certain company exposes sequentially crawlable online orders without authentication. A scraper is going to reason about the data instead of just accepting it, and it's going to look for relatively minute errors. In particular, if it suddenly pulls in many more orders in a single day, or if a particular product shoots up in popularity, or if the price of an item in inventory suddenly changes from what it was historically, an alert will fire off.
As a real world example, I was working on a project to scrape all orders for a publicly traded fast food company that offered online orders and delivery. In the middle of one quarter, without warning, we suddenly saw activity that looked legitimate, but noticeably different order data occurring in a way that significantly increase revenue projections. My colleagues and I basically didn't trust the data whatsoever until we could find a localized (and not well publicized online) promotion that accounted for the changes.
Basically, I'm saying that it can be done, but it's hard and not a silver bullet. If the data is being used as part of a timeseries, then data meaningful enough to disrupt operations if going to be noticed and manually reviewed to identify a narrative that explains it; if the data is believably false but not meaningful enough to change a trend, it's not really going to matter to them anyway.