Effective data management in the scientific community requires standardized terminologies aligned with the FAIR (Findable, Accessible, Interoperable, Reusable) principles. This poster highlights key challenges in establishing these terminologies, essential for data management professionals, policy statements, project submissions, data management plans, data dictionaries, and repositories. Polysemy, where terms have multiple meanings, leads to ambiguity and inconsistencies. Examples include distinctions between roles such as Data Owner, Data Steward, Data Custodian, and Data Manager. Standardizing terminologies enhances clarity, reduces errors, and improves overall data management quality. Key Challenges: How to reuse and/or develop standardized terms for essential data management practices and tools. Address polysemy, provide clear examples to avoid confusion among stakeholders. Identify gaps in existing glossaries, particularly regarding data ownership and intellectual property, and organize crosswalk processes. Discuss governance mechanisms for maintaining and updating terminologies across projects and institutions. Formulate terminologies for cataloging services and skills, improving clarity and communication. Enhance data management quality through better terminologies: Promote, reuse and develop terminologies that directly contribute to higher quality data management practices. Ensure terminologies support the FAIR principles to enhance data reliability, reproducibility, and usability. Disseminate the usefulness of defining, maintaining and using terminologies in the general and disciplinary communities. By providing interdisciplinary examples, this poster aims to highlight terminological challenges and foster discussions on potential solutions to improve data management practices within the scientific community. The poster will also aid communication in the dedicated session and in preparing an open paper on the topic.