The generative AI-based business glossaries are a nice feature and help in standardizing terms across different departments.
The integration with existing systems is not as smooth as I had hoped. We faced significant challenges during the setup phase.
It does help in connecting data producers and consumers, but the overall performance and reliability need improvement for it to be truly beneficial.
I appreciate the concept of creating a decentralized data ecosystem, which is a critical need for many enterprises today.
The platform is quite complex to navigate, and the learning curve is steep. It feels like it needs more user-friendly documentation.
While it aims to address the technical debts of data management, our team struggled to implement it effectively, which ultimately delayed our data monetization initiatives.
The idea of a semantic query engine is promising.
However, it often fails to deliver relevant results, which makes it frustrating to use in real-world applications.
While it aims to help manage data more effectively, we found it ultimately hindered our operations due to its inconsistency.
I like the concept of entity-centric models, which helps in organizing our data effectively.
The platform can be quite slow, especially when handling large datasets.
While it offers some solutions for data management, the overall experience has been somewhat underwhelming due to performance issues.
LEGOAI's tools like OntoCraft are quite innovative and have potential for improving our data processes.
The interface could be more intuitive, and there is a lack of comprehensive tutorials to guide new users.
It assists in data federation, which has streamlined our data access, but I believe there’s room for improvement in speed and reliability.