What does the term "data model transformation" primarily refer to?

The term "data model transformation" primarily refers to adjusting data structures to meet new conditions. This process involves modifying how data is organized, represented, or accessed to align with evolving requirements, such as changes in user needs, technological advancements, or shifts in organizational goals. By transforming data models, organizations can ensure that their data remains relevant and usable, enabling better decision-making and analysis.

This transformation can involve various operations, including normalizing data structures, implementing new relationships between data entities, or reconfiguring attributes to accommodate new types of data or workflows. The emphasis is on the conceptual or logical adjustment of how data is structured, rather than on physical storage or enhancements related to visual representation. The focus is primarily on ensuring that the data model can effectively support the ongoing demands of the organization or application it serves.