[Background] Many startup environments and even traditional software companies have embraced the use of MVPs (Minimum Viable Products) to allow quickly experimenting solution options. The MVP concept has influenced the way in which development teams apply Software Engineering (SE) practices. However, the overall understanding of this influence of MVPs on SE practices is still poor. [Objective] Our goal is to characterize the publication landscape on practices that have been used in the context of software MVPs. [Method] We conducted a systematic mapping study using a hybrid search strategy that consists of a database search and parallel forward and backward snowballing. [Results] We identified 33 papers, published between 2013 and 2020. We observed some trends related to MVP ideation and evaluation practices. For instance, regarding ideation, we found six different approaches (e.g., Design Thinking, Lean Inception) and mainly informal end-user involvement practices (e.g., workshops, interviews). For evaluation there is an emphasis on end-user validations based on practices such as usability tests, A/B testing, and usage data analysis. However, there is still limited research related to MVP technical feasibility assessment and effort estimation. We also observed a lack of scientific rigor in many of the identified studies. [Conclusion] Our analysis suggests that there are opportunities for solution proposals to address gaps concerning technical feasibility assessment and effort estimation. Also, more effort needs to be invested into empirically evaluating the existing MVP-related practices.

Innovation depends on the exploitation of market potential with products that are aligned with customer needs. However, building innovative products is becoming gradually more challenging because of increased market volatility, uncertainty, complexity and ambiguity. In our Innovation Lab, inside an e-Procurement Company, we encountered several challenges when implementing an innovation process to develop an initial unstructured data processing Minimum Viable Product (MVP) based on opensource big data tools: (i) raising and prioritizing user demands; (ii) deciding about adequate tools; and (iii) understanding how to promptly set up a viable product. In this paper, we share our open-source innovation experience in bringing novel solutions to an oil company's suppliers. In general, we present and discuss how we have been applying our innovation process to create MVPs, and which technical decision helped us accelerate the MVP development in the presence of a large-scale, unstructured database and open-source big data tools. Overall, we believe the proposed lean-based development process can help practitioners and researchers who want to understand and improve their knowledge about lean products, how to build MVPs, and advance open-source innovation involving big data tools.