Updated: Mar 22
Audience: Middle and High School Students
Just like math uses numbers and equations to find a solution, companies use data and algorithms to predict what people like. Image by Markus Spiske from Pixabay.
When browsing YouTube, TikTok, Instagram, or many of the other social media platforms that have become so popular recently, we often find ourselves wandering into deep rabbit holes, watching videos that seem to reflect our interests more and more as we continue.
Let’s say you were to search up “drawing tutorials” on YouTube. When returning to the YouTube homepage, you’d find all of the suggested videos being drawing tutorials. This is because of data and how companies use it to understand people’s lives. Technology has advanced to the point where it has become a critical and necessary part of everyone’s day-to-day activities. Computers, laptops, phones, and smart watches all connect to each other using a network of connections. The Internet is a product of these connections, allowing people to ignore distance and send or receive information over many thousands of miles.
Algorithms are calculations and rules used in programming to keep track of all of this information. Every action on the Internet creates data, and this data goes through algorithms to be organized and used. Websites that you visit, advertisements that you look at, and videos that you watch are all actions that generate data. Data is “harvested” or “extracted” by companies, and then used in their algorithms to better understand what people like and how they interact online. In a world where technology companies make money based on who pays more attention to them, understanding what people actually like is very important. If YouTube, for example, knows my search history and what videos I like, they’ll be more likely to recommend those videos to me, and I will be more likely to watch them. This creates a cycle of recommending, viewing, and data collection.
An example of how data can be visualized and used to look at viewing patterns. Image by Stephen Philips from Unsplash.
Every time you like a post on social media or click on a video, there are algorithms and online bots that look at what type of post or video you’re liking, and then suggest more of those posts/videos to you. This is why you may find yourself browsing YouTube for hours after searching something up; the algorithms recognize what videos you like and give you more and more of those videos, which makes us spend more time on their platform. There were countless times when I was looking up something for a school project, such as “how plants grow” for science class, and then was bombarded with tons of other advertisements and video suggestions about plants. This is because algorithms can use data to know our interests and search history. It would be like if a librarian knew every single book you’ve ever read and could predict what books you might want to read next.
According to Dr. William Goodrum Ph.D, companies have begun to see data as a form of “land”, which they can improve upon rather than simply purchase.
This means companies are starting to use data in more ways than giving you video or post recommendations. Companies use data to create “maps” about which people are more likely to buy their product, and can create extremely specific advertisements that people are more likely to click on. Data is sold between companies so they each have a better idea about what their customers like. Small businesses use advertising data from Google and Facebook, for example, so that they can appeal to a specific audience, and get more clicks, likes, shares, etc.
The impact of data is huge because of the ways in which it is used, and the potential for how it can be used in the future is limitless. Data isn’t bound by physical restrictions, like other industries. The new generation of students now has access to new STEM resources and materials that can help them become more well-versed in how data is used and collected. And for students and members of the youth, knowing how data is used will help you understand how this global network functions, and what our role in it is.
Goodrum, Will. “Data Is Not Oil. It Is Land.” Data Science Consulting, Elder's Research, 2018, www.elderresearch.com/blog/value-of-data.
Klosowski, Thorin. “Big Companies Harvest Our Data. This Is Who They Think I Am.” The New York Times, The New York Times, 28 May 2020, www.nytimes.com/wirecutter/blog/data-harvesting-by-companies/.
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