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This talk examines the application of human mobility data based on mobility networks extracted from varying granularities in data-driven informed policymaking through two case studies. The first part of the talk will focus on enhancing local economies by predicting the future well-being of businesses based on customer co-location networks extracted from credit card transaction data from an OECD country, which are used to create business-level features. The results show that the proposed co-location network features yield on-par performance with traditional and highly private financial indicators (e.g., earning per asset and debt ratio) while providing a higher level of safeguarding against privacy attacks. In the second part, I present a comprehensive exploration of neighborhood adaptability indicators utilizing mobility networks derived from extensive smartphone data in New York City, one of the global economic centers of the world. This study analyzes the extent of topological changes in mobility networks across various sociodemographic groups and employs simulations to assess mobility patterns under hypothetical points-of-interest densities. Overall, this discussion underscores the use of mobility networks in driving data-informed policymaking through case studies in different domains. The results of the conducted studies provide actionable insights for policymakers, public administrations, and governments as data-driven policymaking becomes increasingly more significant.