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Viable framework 1.0, evaluating viability of startups by using a data based framework
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Most individuals have at least one business idea that they believe might be great. At the same time governments, academics and the media are also encouraging these individuals to come forth and become entrepreneurs. There is little doubt today that the entrepreneurial sector is critical for the future development and sustainability of the economy (representing 33% of global GDP and 45% of total employment). However, not all the ideas from entrepreneurs are viable.
Many times, entrepreneurs do not have enough knowledge and/or skills to evaluate and assess their own business ideas during the early stage. On the other side, investors are often times swamped with hundreds of business plans to evaluate, having to allocate a large percentage of internal, but limited resources to this process often times causing them to miss interesting opportunities. How can be determined if a business venture is viable in advance? If it will be able to survive and reach success? Viable Framework 1.0 aims to provide a data based framework as a tool to evaluate the viability of early stage startups. This framework can help investors (and corporations) and entrepreneurs by creating a common language between both sides of the development process of new fast growing potential ventures. The methodology to create the framework has been structured in four steps. The first step is the literature review with the goal of analysing previous research in similar fields, defining the concepts of viability, success and survival, and collecting a comprehensive list of factors that might determine success. The second step is to shortlist the initial list of success determinants with the criteria of number of appearances in the articles,
academic works and books studied in the literature review. The third step is to ask investors about their own definition of viability, success and survival, and about the importance of these collected and shortlisted determinants of success in order to compare the theoretical and the practical points of view. And the fourth step is to analyse Crunchbase, as a defacto standard database on startups in order to detect some insightful information, contrast with the factors collected in the literature review then measured by investors, and try to find predictive models using data mining and machine learning.
After executing the four steps, we concluded than the theory and practice meet on the need of strong team capable to lead a brilliant execution . There is a general alignment, among all the sources analysed about the need of a complete, strong and determined team with the ability to design, plan and execute an scalable business model. The contradictions detected between literature-investor inputs and data inputs reveal the need of building better datasets in order to predict viability by looking at information from different investment stages. Other contradictions, for which we did not find a clear explanation, would require further research and that is why we Viable Framework 1.0, evaluating viability of startups by using a data based framework.
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Business Analytics