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Edited By: Andrew Gonzales
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Your age, your income, your gender, the things you like to share with your friends, the things you would like to keep to yourself combined with thousands of other data points have become a trillion-dollar savings accounts for the biggest tech companies in the world and in the next five years they are going to start making withdrawals.
It’s no secret that big tech companies have been collecting petabytes of data on every user they can lure onto their platform. Big data has become one of the most attractive assets to investors. Companies like Snowflake, Teradata and Palantir that do nothing but compile and manage billions of data points and turn them into useful information are worth billions of dollars.
The data manager Snowflake was so attractive that they even got the legendary Warren Buffet to change his investing strategy around tech IPOs so he could include them in his portfolio. Those are just the companies that make sense of your data, the companies that can actually harvest it are worth trillions and that’s because your information is useful for SO much more than just targeted advertising. Jeff Bezos may or may not be using Alexa to listen out for product recommendations but that’s just the cherry on top of the big data Sunday, there are much more profitable business strategies used by the big data companies than using your data to recommend you a new toaster.
Big data is becoming the new big oil because memory storage has become orders of magnitude cheaper. In the 2000’s big tech companies started to realize that their user data would be highly monetisable through ecommerce and direct sales to third-party data brokers so they started to collect as much as they could. Back then high-volume data centre memory still cost as much as $10,000 per terabyte and it was stored on highly inefficient hard drives.
All web companies HAD to store some transactional user data like login information, names, account status and everything integral to their service. The big data companies went further and took a chance by collecting thousands of other non-essential data points like how long users stayed on certain pages, what time of day people would interact with certain services and what caused users to leave the platform.
The business value of this data was obvious, but it was too expensive to store and compiling such large data sets into usable information would take weeks or months with the computers available at the time. The companies made the bet that storage would become cheaper and computer processing would become faster and as it did the data they were collecting would become proportionally more valuable. The companies that started earliest took to the biggest risks, but they were able to build the biggest data moats.