The Importance of Data Maturation in Deploying Machine Learning in Hospitality Operations

Oracle Hospitality has recently shed light on the importance of ‘data maturation’ when it comes to commercializing machine learning (ML) in the hospitality industry. This term emphasizes the need to give computers time to sift through training data, test variables, and find patterns that will lead to the development of algorithms and models to advance business goals.

In a world where time is a limiting resource, it is crucial to understand the significant gap in learning between machines and humans. While humans learn largely by causal inference, computers learn about correlation. This means that machines need a large amount of data points to develop a probabilistic model of how the world works and refine it through testing.

Two key actions are implied in the process of data maturation: deeper data connections and multivariate testing. Deeper data connections involve integrating various systems to provide more accurate algorithms and models, while multivariate testing requires testing and examining how people respond to the machine’s modeling in order to refine it.

The importance of developing a plan for deploying ML now and thinking in terms of data maturation over the long run cannot be overstated. An example from Nor1 illustrates how data maturation affects pre-arrival upselling revenues, emphasizing the need for continuous testing and refinement to make sense of the vast amount of variables at play.

In conclusion, the journey towards successful ML implementation in the hospitality industry requires patience, testing, and a long-term strategy. By understanding the concept of data maturation and the importance of accumulating observations over time, hoteliers can set themselves up for success in the ever-evolving world of AI technology.