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Data Science research is undergoing a revolution fueled by the transformativepower of technology, the Internet, and an ever increasing computationalcapacity. The rate at which sophisticated algorithms can be developed isunprecedented, yet they remain outpaced by the massive amounts of data that areincreasingly available to researchers. Here we argue for the need to creativelyleverage the scientific research and algorithm development community as an axisof robust innovation. Engaging these communities in the scientific discoveryenterprise by critical assessments, community experiments, and/or crowdsourcingwill multiply opportunities to develop new data driven, reproducible and wellbenchmarked algorithmic solutions to fundamental and applied problems ofcurrent interest. Coordinated community engagement in the analysis of highlycomplex and massive data has emerged as one approach to find robustmethodologies that best address these challenges. When community engagement isdone in the form of competitions, also known as challenges, the validation ofthe analytical methodology is inherently addressed, establishing performancebenchmarks. Finally, challenges foster open innovation across multipledisciplines to create communities that collaborate directly or indirectly toaddress significant scientific gaps. Together, participants can solve importantproblems as varied as health research, climate change, and social equity.Ultimately, challenges can catalyze and accelerate the synthesis of complexdata into knowledge or actionable information, and should be viewed a powerfultool to make lasting social and research contributions.

AI Competitions and Benchmarks: The life cycle of challenges and benchmarks