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Adoption of data and compute-intensive research in geosciences is hindered by the same social and technological reasons as other science disciplines - we're humans after all. As a result, many of the new opportunities to advance science in today's rapidly evolving technology landscape are not approachable by domain geoscientists. Organizations must acknowledge and actively mitigate these intrinsic biases and knowledge gaps in their users and staff. Over the past ten years, CyVerse (www.cyverse.org) has carried out the mission "to design, deploy, and expand a national cyberinfrastructure for life sciences research, and to train scientists in its use." During this time, CyVerse has supported and enabled transdisciplinary collaborations across institutions and communities, overseen many successes, and encountered failures. Our lessons learned in user engagement, both social and technical, are germane to the problems facing the geoscience community today. A key element of overcoming social barriers is to set up an effective education, outreach, and training (EOT) team to drive initial adoption as well as continued use. A strong EOT group can reach new users, particularly those in under-represented communities, reduce power distance relationships, and mitigate users' uncertainty avoidance toward adopting new technology. Timely user support across the life of a project, based on mutual respect between the developers' and researchers' different skill sets, is critical to successful collaboration. Without support, users become frustrated and abandon research questions whose technical issues require solutions that are 'simple' from a developer's perspective, but are unknown by the scientist. At CyVerse, we have found there is no one solution that fits all research challenges. Our strategy has been to maintain a system of systems (SoS) where users can choose 'lego-blocks' to build a solution that matches their problem. This SoS ideology has allowed CyVerse users to extend and scale workflows without becoming entangled in problems which reduce productivity and slow scientific discovery. Likewise, CyVerse addresses the handling of data through its entire lifecycle, from creation to publication to future reuse, supporting community driven big data projects and individual researchers.