•This article focuses on prescriptive analytics, which is the less mature area of business analytics in comparison with descriptive and predictive analytics.•Prescriptive analytics are positioned as the next step towards increasing data analytics maturity and leading to optimized decision making ahead of time.•The existing literature pertaining to prescriptive analytics is reviewed and prominent methods for its implementation are examined.•The article identifies research challenges and outlines directions for future research in the field of prescriptive analytics. Business analytics aims to enable organizations to make quicker, better, and more intelligent decisions with the aim to create business value. To date, the major focus in the academic and industrial realms is on descriptive and predictive analytics. Nevertheless, prescriptive analytics, which seeks to find the best course of action for the future, has been increasingly gathering the research interest. Prescriptive analytics is often considered as the next step towards increasing data analytics maturity and leading to optimized decision making ahead of time for business performance improvement. This paper investigates the existing literature pertaining to prescriptive analytics and prominent methods for its implementation, provides clarity on the research field of prescriptive analytics, synthesizes the literature review in order to identify the existing research challenges, and outlines directions for future research.