Data Science

(Note: This is a follow-up rant of my pessimistic conclusion about what data science means from two years ago.)

Let me come straight to the point: Data science is synonymous for marketing. Period. Do not let yourself be misguided by online data science/machine learning/statistics lectures which only cover topics from the mathematical or programming area. These will only take up 5% of your work! The rest is 60% hot-air blowing, 50% managing/organizing and 40% delegating to juniors or externs, making that a total workload of 155% (60+ h/week), with expectations on you to generate projects and to build Big Data strategies. (After all, you’re smart, right? Otherwise you wouldn’t know that much math and AI. You’re smart, thus you can land big projects, right?) And the area of application is advertising, the data is customers, the outcome is customers—more people buying more stuff.

No, thanks for the offers, but I won’t work in advertising.

Who actually is making Big Data a hot topic? It’s those who create these solutions in the first place and sell them to companies who use these in their marketing processes.

“Data is the new oil.” Really? So, how do you power your cars with data? BS! We’re still living in the oil interval.

It’s so sad that AI is not solving any problems. It’s used for making game characters act smarter, it’s used for more effective advertising, it’s used for making call centers obsolete (by executing voice commands), it’s used for shopping agents (by executing voice commands), it’s used for autonomous driving of cars, vacuums and lawnmowers. It’s entirely used for making us (fat first-world people) even more lazy (fat), even more consumeristic (fat) and even more entertained (fat). These are solutions looking for a problem, but they can’t find one. Seriously, I see much more potential in the blockchain, which deserves its own rant soon.