I live in Melbourne like the author and I certainly don't speak for the entire industry. But here's it's mostly government and banks and other enterprise where data is just a cost-centre so middle-management could say they were big on data science, which has since transformed into being big on AI.
There's no end-use for most of the work, there's no stakeholders, often the product being delivered will be a 'platform' of some sort and everyone outside the data area is afraid of sounding stupid by asking what that means (it doesn't mean anything). The result is entire services can go down for months at a time and no-one notices (and then when someone does realise management covers up the fact no-one noticed because it's embarrassing).
Thank you! This definitely makes sense. The "emperor has no clothes"-ism around enterprise data platforms is definitely real. So is the trend of orgs building big, expensive platforms without thinking about who their end users are even supposed to be.
I actually think these are expressions of organizational dysfunction rather than something inherently wrong with data. It's the same way dysfunctional orgs might spend millions on SAFe adoption or prematurely spinning up a global sales motion - incentives and perceptions become misaligned with the reality of what the organization needs to do to succeed, the decisionmakers make bad capital outlays, and the implementers are left feeling like garbage working on a project that's doomed to fail.
Having worked in data in Melbourne and also met the author of the post you link, I think saying the entire industry is a scam is an overgeneralization. There's some especially bad stuff going on in parts of the industry (e.g Melbourne government or government-adjacent parts) but in others parts of the industry data engineering and analysis is hugely valuable.
I live in Melbourne like the author and I certainly don't speak for the entire industry. But here's it's mostly government and banks and other enterprise where data is just a cost-centre so middle-management could say they were big on data science, which has since transformed into being big on AI.
There's no end-use for most of the work, there's no stakeholders, often the product being delivered will be a 'platform' of some sort and everyone outside the data area is afraid of sounding stupid by asking what that means (it doesn't mean anything). The result is entire services can go down for months at a time and no-one notices (and then when someone does realise management covers up the fact no-one noticed because it's embarrassing).