Not everything needs to be an app

I was approached once by an entrepreneur, let’s call him Ed. Ed wanted me to build an app that detects car engine failures. He was a trust fund boy who was annoyed by the fact that some of his many cars failed without warning. Ed read too much news about deep learning and how wonderful it was to solve everything, and which too much cash and time available, he wanted to become a tech startup founder.

It was not clear whether Ed’s app was intended as a B2B or B2C app. Ed had no experience marketing apps nor selling IT to car mechanics, so though luck to sell this. I did find one competitor with a very similar app, a startup that folded quickly and the founder did not even list it on his LinkedIn profile.

Assuming Ed’s brilliance would figure out this market, there are technical issues on the data side. Way too many car model/year/failure type triplets, which would make it very hard to collect usable data.

Well, at least all the trouble would be worth it, right? Solving the very pressing problem of running wrong diagnostics on cars?

Author: Pablo Maldonado

I am a consultant, technical trainer and lecturer in automation, data science and AI. My students, either at universities or companies, appreciate my hands-on approach coming from my experience at several projects across industries like financial services, marketing, and HR. Since 2017, I have been leading workshops for 30+ clients in 10+ countries including major companies such as Shell, Renault, PwC, O’Reilly Media, O2, La Poste, as well as institutions like the European Investment Bank, the Czech National Bank, the Australian Government, and many others. I have written a couple books (on Shiny, and on Deep Learning). I hold a PhD in Applied Mathematics from the Sorbonne Université in Paris, with a specialization in Game Theory and Markov Decision Processes.

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