Over the last several decades, considerable effort has gone into developing better numerical models for fractured rocks to improve groundwater flow and contaminant transport predictions. However, a large degree of heterogeneity causes formidable challenges in characterization, monitoring, and modeling. On the modeling front, difficulty in capturing heterogeneity has spawned several conceptual models. Generally, conceptual models fall under three categories: Equivalent Continuum (EC), Discrete Fracture Network (DFN), and Stochastic Continuum (SC) modeling approaches, each with varying parameterizations leading to different characterization requirements. Recently, efforts have been spent on Hydraulic Tomography (HT), which fuses drawdown signals from multiple pumping tests to characterize the spatial heterogeneity in hydraulic conductivity (K) and specific storage (S_s) through inverse modelling. Inverse models for HT analysis typically rely on SC or DFN approaches. On one hand, SC-based HT analysis yields smooth hydraulic parameter estimates, when the monitoring resolution and number of pumping tests available for analysis are limited. Moreover, because HT relies on pumping tests, matrix K may not be accurately mapped for certain rock types. On the other hand, DFN approaches for HT analysis could potentially be problematic because the accurate mapping of fractures between boreholes is impossible with existing technologies. A better approach may be a hybrid approach in which accurate geological data are strategically incorporated into a SC-based HT analysis. Specifically, fracture geometry, connectivity and matrix hydraulic parameters of varying accuracy can be utilized as priori information in HT analysis. Our research reveals the importance of incorporating accurate geological data in HT analyses when drawdown data are sparse and not available within the matrix. When accurate data are not available, HT analysis without geologic information is more reliable than those based on wrong or incomplete description of geologic features. Overall, inverse modeling and data fusion are necessary steps in building robust groundwater flow and transport models in fractured geologic media.