In a well-known article published in 2012 in the Harvard Business Review , authors Tomas H. Davenport and DJ Patil pointed out that data scientist would be “the sexiest profession of the 21st century” . Drew Conway's now popular diagram depicts how these professionals must possess a complex set of skills and characteristics, ranging from IT, mathematics and statistics, without neglecting extensive knowledge of the job market.

‍

Data science – or 'data analysis', as it is also called by some – is an evolution of a broader area: Business Intelligence , which in itself is a term that applies to a wide range of of architectures, databases, analysis tools, applications and methodologies that allow the discovery and explanation of 'hidden' or unknown aspects in the data, revealing information of great importance in the decision process. It is this transformation of data into information that allows the extraction of knowledge, the creation of expertise, and subsequent transformation into wisdom. However, this change requires companies to have an analytical mindset and a strategy on how to manage their data. Therefore, companies need to not only have an administration and integration policy to ensure how and where data from different systems is stored, accessed and consolidated, but also have programs that ensure their quality, in order to guarantee their validity.

‍

It is this transformation of data into information that allows the extraction of knowledge, the creation of expertise, and subsequent transformation into wisdom.

‍

Nowadays, there are several examples of companies that base their decision-making processes on their data. Bernard Marr explains this in his work The Intelligent Company: Five Steps to Success with Evidence-Based Management , demonstrating that big names such as Google, Coca-Cola, Tesco, Yahoo, among others, hardly make any strategic decision that is not established in data.

‍

BUT WHAT DOES THIS DATA SCIENCE HAVE TO DO WITH THE HOTEL INDUSTRY? EVERYTHING AND NOTHING!

‍

• NOTHING , because nowadays, with the exception of large hotel chains, large travel agencies or large tour operators, practically no entity in the Hotel and Tourism sector uses data science in the management of its day-to-day operations.
• EVERYTHING , because all companies should make use of their data! A good example of this is AirBnB, which makes a point of integrating a data scientist into each leadership team. And the truth is that companies that do not adapt to this culture of analysis will quickly lose competitiveness with those that already do.

This mentality that integrates facts into decision-making is not, however, something that can be implemented all at once. It is rather a process that unfolds in a few steps:

Step 1 | TO DESCRIBE

Initially, companies start by analyzing their information history. By having a centralized warehouse of information, with quick access, managers can quickly analyze historical data in different aspects, which allows them to make faster and, above all, better informed decisions.

For example: When studying the marketing investment in advertising for a special Christmas pack, a hotel can analyze in demographic terms where the largest number of people come from, better targeting the available budget.

‍

Step 2 | PREDICT

Predictive models begin to be built that make use of all available data to predict operations, anticipate and discover trends.

For example: A hotel can build a model to predict reservation cancellations and thereby reduce uncertainty in terms of net demand, thus allowing it to adopt better overbooking and cancellation policies.

‍

Step 3 | PERSPECTIVE

In this last step, also known as 'optimization' or 'scenario construction', companies begin to take real advantage of their data to build hypothetical scenarios and optimize their operations.

For example: Based on check-in and check-out patterns , a more suitable time can be defined for reception staff.

‍

In the next publications I will continue to explore the application of data science in the Hospitality Industry, revealing examples where it is already used, including addressing the technologies used and the associated challenges.

‍