Challenges in Data Driven Decision Making in the Early Stages of a Startup

Abstract

Entrepreneurship is usually considered a chaotic process paved with uncertainties. Without a method to the madness, entrepreneurship can become detrimental to the well-being of the entrepreneur. The early stage of a start-up refers to all important activities that help identify a potential business from an idea. This discovery process is characterized by stress and uncertainty which makes data-driven decision-making particularly challenging. Unlike mature phases (post-launch and product-market fit phase) where data is available in plenty, early phase decision-making depends on either limited data or generating one’s own data. Uncertainty gives rise to biases in decision-making. The two most negatively impacting biases are confirmation bias and self-serving bias. In combination, they affect the way an entrepreneur in the early phase interprets both data and the consequences of his/her decision. Through a qualitative interview-based study involving 30 entrepreneurs in the early phase and 10 entrepreneurs in the mature stage, this study focuses on recognizing the presence of these biases and proposes a habit-based process for grooming early-stage entrepreneurs. The scientific principles underlying the proposed framework have been detailed out and pragmatic solutions for improving early-phase decision-making have been derived.