<![CDATA[Amidst the escalation of the digital economy, commercialization of data assets (data monetization) has become a crucial strategy for startups to open new revenue streams. However, the ambition to convert data into profit is often hampered by information silos, low data quality, and legal risks following the implementation of the Personal Data Protection Law (PDP Law). This literature research aims to formulate a conceptual framework for adaptive data governance for startups by adopting the Data Management Body of Knowledge v2 (DMBOK v2) guidelines. Using a systematic literature review approach to reputable scientific articles and regulatory documents, this research constructs a governance model that aligns business needs with technical compliance. The resulting theoretical synthesis yields the Agile Data Commercialization Governance Framework (ADCGF), a streamlined model focused on the three main pillars of DMBOK v2. These pillars include data governance with a minimalist role structure (Data Governance), standardization of data product quality based on completeness and accuracy metrics (Data Quality), and de-identification techniques through anonymization and aggregation (Data Security & Privacy). The implementation of this model demonstrates that privacy regulatory restrictions are not a barrier to entrepreneurial innovation, but rather a strategic anchor for building market trust. The resulting blueprint is expected to serve as a practical guide for digital businesses in optimizing the economic potential of data securely, legally, and with high economic value.]]>
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