I would say that this applies even more so outside of academia. At early stages of development, a research group's or company's product is by necessity a report or a presentation rather than a physical plant's or process's real, quantifiable performance. No malicious intent is required; it's just all too easy to fool yourself or cherry-pick data to support desired conclusions when the recordkeeping is poor.

In my hard-tech experiment-heavy start-up there's no way we could have made any actual technical progress without setting up a solid data preservation and analysis framework first. For every experimental run, all the original sensor data are collected and immediately uploaded along with any photos, videos, and operator comments to a uniquely-tagged confluence page. Results and data from any further data or product analysis are linked to this original page.

As an anecdotal example, we recently caught swapped dataset labels in results from analysis performed on our physical samples by a third-party lab. We were able to do this easily just because we could refer back to every other piece of information regarding these samples, including the conditions in which they were generated months prior to this analysis. As soon all the data were on display at once, the discrepancies were obvious.