Data quality is the gold standard and a key requirement for building superior AI models. Engineers are well aware that AI models rarely fail because of the wrong algorithm, but rather because of wrong timestamps.
Organizations that claim to have “ten years of trend data” give the impression that they have collected information about a specific topic over a full decade, containing real changes, big patterns, and ups and downs instead of just short-term noise. Most likely, that claim means ten years of 15-minute averages, from a system whose naming convention was reset three times, and whose network dropped for a week every Spring.
That most definitely does not constitute data continuity. Data continuity and resolution matter more than the duration.