human
In a DAM Maturity Model for a luxury brand, all the sub-pillars are at the OPERATIONAL or OPTIMAL level except the one called FINDABILITY, which is at AD-HOC. Even if all the pillars are at a good level, they are the result of several years' work, with the implementation of numerous process bypass measures to feed, control or retrieve information. The thesaurus, created manually by the teams, makes a significant contribution to the DAM's organization, while the keywords enable an efficient search, although not as effective as other DAMS. Although a great deal of specific development has been carried out, always with a good sense of prioritization, the Stella DAM still remains below other DAMs (without being up to scratch).  Full-text or advanced search performance is generally inadequate compared with the competition, limited by the number of metadata for technical reasons. The significant development costs are not justified in relation to what the DAM should provide in terms of functionalities. Search limitations and very slow performance lead teams to launch several queries in parallel with several different keywords in order to hope for a multiple result approaching the initial query. Reporting is very basic and much of the consolidation is done manually. The user experience is poor, as you can't navigate from one folder to another by going up or down, the search bar isn't always present, and renaming or moving a folder or file is almost impossible.
Could you provide me with a more detailed summary, grouping items by category and integrating the problems of other Digital Assets Management products?
gpt
--- Reasoning ---
1. Is the question multiple-choice question?
No
2. Is it implied multiple-choice question?
No
3. If two experts were independently asked the question, will the final answer be exactly the same?
No

--- Answer ---
open