Data-Driven Solutions in VC Investments - A cross-sectional study on best-in-class data strategies within deal origination

Abstract

The development of new technologies and the innovative trends that have emerged over the last years are transforming and disrupting several traditional industries. In a world that is becoming increasingly connected and digitized, data-driven strategies have captured the interest of venture capital investors. Actors within the VC industry have identified the potential of data-driven solutions to improve the operational efficiency within different investment stages - particularly to strengthen and enhance the deal sourcing phase, which is relatively prone to suboptimal resource allocation. The primary aim of this thesis was to investigate, together with Volvo Group Venture Capital, which are the best data-driven strategies currently used by top-performing VC firms to adapt to this observable transformation. In order to achieve that, the authors conducted a crosssectional study primarily based on interviews with top-performing VC firms as well as with experienced professors within the entrepreneurial finance and venture capital ecosystem. The findings provide relevant insights on how data-driven VCs are currently performing their deal sourcing strategies while simultaneously highlighting the role of data-driven tools to support investment firms in retrieving, organizing, and presenting the data. By studying and combining these best practices, the authors were able to provide a selection of data dimensions and databases together with relevant tools for the organization and structuring of the data to transform it into useful information for strategic financial decision-making. Furthermore, the results of the study can also be considered as support and a starting point for VC firms currently redefining their data strategies.