Tech Insights

Data Model Enhancement

Last updated , generated by Sumble
Explore more →

What is Data Model Enhancement?

Data Model Enhancement refers to the process of refining and improving the structure and organization of a data model. This involves optimizing data relationships, adding or modifying attributes, and ensuring data integrity and consistency. Common uses include improving data quality, simplifying data access, enhancing data analysis capabilities, and supporting evolving business requirements. It may also involve techniques like normalization, denormalization, and the introduction of new entities or relationships to better reflect the real-world entities being modeled.

What other technologies are related to Data Model Enhancement?

Data Model Enhancement Complementary Technologies

Essential for data migration and synchronization within data model enhancement efforts.
mentioned alongside Data Model Enhancement in 78% (114) of relevant job posts
Often required during data model enhancements to transform existing data to fit the new model.
mentioned alongside Data Model Enhancement in 75% (107) of relevant job posts
Technology enabling data exchange between SAP systems, which is vital for propagating changes made during data model enhancements.
mentioned alongside Data Model Enhancement in 74% (106) of relevant job posts

Which job functions mention Data Model Enhancement?

Job function
Jobs mentioning Data Model Enhancement
Orgs mentioning Data Model Enhancement

This tech insight summary was produced by Sumble. We provide rich account intelligence data.

On our web app, we make a lot of our data available for browsing at no cost.

We have two paid products, Sumble Signals and Sumble Enrich, that integrate with your internal sales systems.