I installed a wireless network at home. It worked for two years – and then suddenly it failed. For the whole world, I could not find out how to re-configure it. Embarrassingly enough, I couldn’t even remember how to get into the damn thing.
And I had a very helpful plug-in for my web browser. But after the browser had updated itself, it announced that the plug-in wasn’t compatible anymore. I searched for a replacement, but the list of plug-ins had 5000 items and the search function couldn’t find anything of the same kind…
And I used to access the voicemail on my mobile phone by just pressing a button on the keypad. But unexpectedly it stopped working. Perhaps it happened when I switched operators … but I could not figure out how to get it back. Nor even whether the problem was on my phone, or with the new operator.
You might think I’m just hopeless with new technology. I’m not. Rather the opposite: I’ve been working with computers for more than 30 years. I really belong to the “early adopters”; I’ve often been one of the first users of new technologies.
But every so often, I wind up a “somewhat-later abandoner”.
Digital technology may contain no moving parts but it still, somehow, gets worn, splintered and corroded. It rots. It decays. The rot, though, is mostly invisible (and un-smellable). Still, one day, the thing is broken.
Onceability: The consequence of technology rot
Jonas Söderström
Code often suffers from what people call “bit rot” when it isn’t actively maintained. Data can suffer from the same type of problem; that is, people forget the precise meaning of specialized fields, or data problems from the past may have faded from memory. For example, maybe there was a short-lived data bug that set every customer id to null. Or there was a huge fraudulent transaction that made it look like Q3 2017 was a lot better than it actually was. Often business logic to pull out data from a historical time period can get more and more complicated. For example, there might be a rule like, “ if the date is older than 2019 use the revenue field, between 2019 and 2021 use the revenue_usd field, and after 2022 use the revenue_usd_audited field.” The longer you keep data around, the harder it is to keep track of these special cases. And not all of them can be easily worked around, especially if there is missing data.
Big Data is Dead
Jordan Tigani