Optimal investment strategy on data analytics capabilities of startups via Markov decision analysis

Abstract

Startup companies operate in an unpredictable and unstable business environment. They​ have the potential to grow through optimal decision-making ►Or a suboptimal decision could spell the end of the company. Data analytics is often used to power and guide startup growth decisions. This​ study explores whether spending more on data analytics can help startups grow and what data analytics capabilities a company should develop to maximize its return on data analytics investment. The Markov decision process methods are used to model growth and spending and derive the optimal strategy based on a cloud hyper-scaling dataset containing billing information from tens of thousands of startups. Our method explores whether increasing data analytics capabilities in startup companies can boost their growth. We also compare our optimal strategy to the realized spending of the best, average, and worst-performing companies. We find that the actions of the best-performing startup companies highly correspond with the optimal policy. In addition, the actions of the average and worst-performing companies are not in line with optimal policy. Based on our results, An overview of the options for procuring data analytics capabilities is suggested.