Data analysis, big data, etc. Already hit a point of data pollution concern. There are more datasets available than there are possible scientific and financial uses in a given timeframe. Basically, there’s people whose job is to sit around all day and try to find uses with any ROI for data that companies already paid for or already collected without any particular goal in mind. Because if they don’t, then those expenses will turn into losses.
Think of it this way, while companies would pay dearly for datasets for advertisement, for example, these include the data of several million people. While the individual data point you personally generated would amount to fractions of a cent in value. So, our data is already heavily devalued.
At the same time, the highest cost is in scrubbing, cleaning, tailoring and analyzing data. Turning data into actionable information. Collecting the data itself is only a small portion of costs. While the vast majority of data collected is low quality or straight up useless. To mitigate data pollution, there’s a whole field of data science whose sole job is to decide what needs to be deleted and thrown away, for it harms the usability of the rest of the dataset.
Equally, companies pay millions in online advertisements, but the individual impression you see on a webpage or search results cost a few cents at most.