Background: Harmonizing informatics in the era of next generation sequencing (NGS) is a precision medicine priority as unconnected data silos unacceptably stall advancement of genomic medicine. GA4GH is a not for profit organization that promotes responsible and effective sharing of genomic/clinical data. In this context, GA4GH conducted a survey of international cancer sequencing initiatives to catalog global activity and dissect perceived barriers to data sharing. Methods: A total of 108 genomic sequencing initiatives were invited to participate in a web based survey that assessed perceived barriers to data sharing. Responses were presented as Likert scales (minor barrier ( = 1); major barrier ( = 6)) and were analyzed by Chi-square testing. Results: Responses were obtained from 59 initiatives (55%) which were largely North American or European based (61%). Initiatives varied in samples analyzed/year ((1-1000 (small): 49%; > 1000 (large): 51%)) and were well distributed between Diagnostic (D) and Research (R) intent (D: 15%; R: 37%; D/R: 34%; Unknown:14%). Biggest perceived barriers to effective data sharing (Likert scales ≥ 4) were: A: lack of funding (75%); incompatible data systems (69%); insufficient clinical data capture (58%); B: lack of informatics expertise (49%); legal (36%) and privacy issues (34%); (A vs B, p < 0.001)). Informatics barriers did not differ between initiative size (p = NS) or intent (D v R, p = NS), but larger initiatives had greater difficulty in capturing clinical data (p < 0.001). Financial barriers did not differ according to size or initiative’s intent (D vs R). Data protection legislation barriers were more apparent in European initiatives (p = 0.04). Conclusions: In this survey, funding, informatics system, clinical data capture and European data protection legislation were perceived as the greatest hurdles to effective data sharing. This more granular definition of potential deficits is informing GA4GH’s efforts to promote system interoperability, collect and effectively share clinical outcome data and harmonize legal/ethics policies, thus maximizing the value of clinico-genomic data for improving patient care.