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Microorganisms represent some of the most ancient and resilient forms of life on Earth. Insights gained from their study are highly significant due to their broad applications in human health, environmental systems, pharmaceuticals, agriculture, industry, biotechnology, food processing, and other fields. Since the early days of microbiology, information concerning microorganisms has faced persistent challenges due to the inherent complexity of microbial data. These challenges include non-standardized nomenclature, the assignment of identical genus and species names to strains with distinct phenotypic characteristics, frequent revisions in taxonomic classification, the use of multiple names or synonyms for the same strain over time, and inconsistencies in strain identifiers across microbial culture collections. The exponential growth of microbial data, driven by advances in research and data acquisition technologies, has made it increasingly difficult to ensure the quality, consistency, and interoperability of microbial information across heterogeneous systems. Traditional data governance frameworks, typically designed for business-oriented data, are inadequate when applied to complex scientific domains such as microbiology, where information is highly variable, distributed, and often embedded in scientific literature rather than structured databases. In this paper, we propose a six-layer theoretical conceptual model specifically designed for microbial data, which organizes information into eight macro entities and applies structured processes for data standardization, integration, quality assurance, governance, and presentation. The model supports essential data quality dimensions and introduces a unique strain identifier to harmonize references across systems, offering a scalable solution for reliable microbial data management and enabling future interoperability among microbial repositories.