There’s no doubt that the amount of data stored on company servers about us is evergrowing. In fact, “every two years, the volume of data across the world doubles in size” (Bartley, 2020). As more social media, websites, apps and companies are created, more and more data is stored. It’s hard to say whether we’ll ever reach a point where companies have enough data on us. Or will they forever be in pursuit of more information about us to know what vouchers to email us or what YouTube advertisements they think we’ll click on?
Reasons for the Growth of Big Data
But companies don’t just keep this data for no rhyme or reason. They do it for two main reasons: to target us with things we’ll like (e.g, advertisements for products they think we’ll enjoy), and to also sell this data to other companies. It’s well known that the purpose of a business is to make money (usually alongside another goal such as providing a good service for its customers or trying to be the best ___ in the world). For a company to maximise its profit, it will (specifically E-commerce businesses) needs to maximise what I call its “visitor to purchase ratio”. Ideally, every visitor (a person who visits the e-commerce site) would buy something. While this is practically impossible, what is possible, however, is various techniques companies can use to pull the ratio closer to 1:1. One of these techniques is using Big Data.
If we have an e-commerce website with various goods, and one of the customers purchases a lot of flowers, then we can use this data to know that we should show you more data. This may be good or bad depending on your views of data collection, but companies can take this one step further with advertisements. If we figure out exactly what kinds of things you like, then we can take that data and pay companies like Google or FaceBook to advertise those types of products to you. This will result in more people clicking on the advertisement and purchasing those items, thereby bringing the visitors-to-purchase ratio ever-so-slightly closer to 1:1. This is why companies are hungry for more data about people – it makes them more money by targeting their adverts to you.
Measuring Big Data
When reading about Big Data, you’ll hear things like “The world will produce slightly over 180 zettabytes of data by 2025.” (Petrov, 2022), but what does this mean? What’s a “Zettabyte?”. In short, computers store data in binary (1s and 0s) – these “bits” (the name of an individual 1 or 0) can be used to hold data. Whenever you want to store something (e.g, your name) in a file, your computer will convert your name into a specific order of 0s and 1s. When you want to read the content of the file, your computer will simply decode the binary and turn it into readable text. One small issue is that text takes up 16 or 8 bits (depending on the encoding you’re using). This means that if you have a text file with 100 characters in it, it would be either 1600 or 800 bits. This may not sound too bad but imagine you have a file containing a huge dataset, then it wouldn’t be practical to say you have "574027542890375480293" bits of data (or however much you have). So instead, we use different “levels” of data. Pretty much, once you have 4 bits, then those 4 bits are called a “nibble”. 2 nibbles (8 bits) are called a “Byte”, then 1024 bytes is a kilobyte, 1024 kilobytes is a megabyte, 1024 megabytes are a gigabyte and so on (it goes on for much longer but I imagine you wouldn’t want to read that).
Bartley, K. (2020, March 27). Data Statistics—How much data is there in the world? Rivery. rivery.io/blog/big-data-statistics-how-much..
Petrov, C. (2022, September 8). 25+ Impressive Big Data Statistics for 2022. Techjury. techjury.net/blog/big-data-statistics
(This is one of the blog posts that I wrote during my Big Data module in College. If you would like to see more, please check out my Big Data series).