Venture capital (VC) has been growing rapidly in recent years. So far the screening and evaluation of potential startups as investment objects largely depends on the venture capitalist's personal experience, network and qualitative evaluations. In the era of big data, the advent of new data sources and analytic techniques enables a data-driven investment process. Grounded in systems theory and the theory of complementarity, this study reports the findings from an exploratory study of 13 VC firms that synthesize and use novel data sources. Our analysis shows that the data-driven approach, in particular, impacts the deal origination and screening stages of investment. It leads to informational and transactional benefits, which lower operational costs in the short term and enlarge the potential return on investment of a VC firm in the long term. We contribute to the literature by shedding light on how various data sources complementarily lead to additional business value.
In recent years, organizations manage an increasing amount of data in order to make better decisions, personalize products, or sell data. By data being combined from various sources, data assets interact with each other. When the interactions are synergistic, they create greater benefits than the sum of the value of the individual data assets. This study explores enablers, mechanisms, and potential outcomes of synergistic interactions among data assets. Based on systems theory and a synthesis of relevant synergy literature, I developed an initial synergy framework in a data context. On this basis, I conducted 14 qualitative interviews to assess the validity of my initial framework. The interview results assisted me in refining and contextualizing a unified conceptual framework of data synergies. The paper reveals that compatibility and contextual relatedness as enablers and informational complementary as a mechanism can lead to super-additive information value among data assets.
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