What do we call the accumulation of inefficiencies in data systems over time that can hinder data quality?

The accumulation of inefficiencies in data systems over time that can hinder data quality is referred to as data debt. This concept captures the idea that as data systems evolve, they often become encumbered by various issues such as outdated data structures, inconsistent data entry practices, and legacy systems that no longer meet current needs. Just like financial debt accumulates interest over time, data debt grows as these inefficiencies are not addressed. This can lead to significant challenges in managing data quality, as poor data practices directly impact the reliability and effectiveness of data-driven decision-making processes.

In contrast, data fatigue pertains to the overwhelming feeling or state caused by excessive data handling or consumption, which does not necessarily align with inefficiencies in data systems. Data corruption specifically refers to the alteration or loss of data integrity, which is a different issue altogether. Data latency relates to the delay in the processing or delivery of data, again unrelated to the concept of systematic inefficiency accumulating over time. Therefore, data debt is the term that best encapsulates the challenge of deteriorating data quality due to these inefficiencies.