According to the SAS institute, DQM provides a "context-specific process for improving the fitness of data that’s used for analysis and decision-making". Essentially, it is a process to ensure that data is reliable and effective.
Thomas C. Redman states, "companies are beginning to realize that, properly managed, data [is] an asset of potentially limitless potential... [and] AI unlocks that potential". DQM enables AI's potential through important practices that improve data quality.
DQM practices focus on several key areas: validity, accuracy and precision, redundancy erasure, consistency, and timeliness. These serve as the primary measurements of how effective data can be.
DQM practices include data profiling, data cleansing, and data standardization. Each of these practices is essential for ensuring that AI models are trained on high-quality, reliable data.
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