Big Data, Artificial Intelligence, and Their Implications in the Tourism Industry

Big Data, Artificial Intelligence, and Their Implications in the Tourism Industry

Evrim Çeltek (Tourism Business and Hotel Management College, Gaziosmanpasa University, Turkey) and Ibrahim Ilhan (Faculty of Tourism, Nevşehir Hacı Bektas Veli University, Turkey)
DOI: 10.4018/978-1-7998-1989-9.ch006

Abstract

Tourism businesses use AI and big data to connect guests creatively and meet their expectations with personalized service. Big data enables tourism professionals to learn more about their customers, and the more they know, the better experience they can offer to customers. As it provides real value, AI has already become an integral part of operations, and this trend will continue. Tourism businesses use AI tools to reduce operating costs and maintenance bills as in many other sectors. AI-oriented marketing has already been widely used in the hospitality industry. Moreover, as long as technology evolves and becomes more complex, tourism professionals will find more ways and methods to implement big data and AI to satisfy customers, and AI will continue to transform the tourism industry. Properties, advantages, and problems of artificial intelligence and big data are discussed in this chapter, and some examples are given from the perspective of the tourism industry.
Chapter Preview
Top

Introduction

Big data is defined as large amount of data sets that cannot be analyzed and managed with traditional data processing tools (Xu et al., 2019). It is briefly described as 5V: Volume, Velocity, Variety, Verification and Value (Atalay and Çelik, 2017). Big data is based on the society's ability to utilize knowledge in new ways to produce useful insights or value-creating goods and services (Schönberger and Cukier, 2013: 11). Artificial intelligence (AI) is an area of computer science focusing on the creation of intelligent machines that work and react as humans do. Artificial intelligence is the simulation of human intelligence processing through machines, especially computer systems (Şener, 2019). These processes include learning (the acquisition of information and rules, and use of information), reasoning (use of rules to achieve approximate or conclusive results), and self-correction. Specific AI applications include expert systems, speech recognition and machine vision.

Tourism businesses use AI and big data to connect guests creatively and meet their expectations with personalized service. Big data enables tourism professionals to learn more about their customers. And the more they know the better experience they can offer to customers. As it provides real value, AI has already become an integral part of operations, and this trend will continue in the future. Tourism businesses use AI tools to reduce operating costs and maintenance bills as in many other sectors. AI-oriented marketing has already been widely used in the hospitality industry. Moreover, as long as technology evolves and becomes more complex, tourism experts will find more ways and methods to implement big data and AI to satisfy customers, and AI will continue to transform the tourism industry.

The use of artificial intelligence and big data applications are discussed in this chapter. Properties, advantages and problems of artificial intelligence and big data are discussed, and some examples are given from the perspective of tourism industry.

Key Terms in this Chapter

Artificial Intelligence: Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and reacts like humans.

Natural Language Processing (NLP): Natural language processing is one of the most important steps in analyzing the texts obtained for institutions and organizations investing in big data technologies.

Big Data: Big data is defined as a large amount of data sets that cannot be analyzed and managed with traditional data processing tools.

Data Mining: Data mining can also be defined as the discovery of information from data.

Deep Learning Platforms: A special type of machine learning consisting of artificial neural networks with multiple layers of abstraction. Today, it is primarily used in model recognition and classification applications supported by very large data sets.

Text Mining: Text mining is a technique that makes it possible to automate processes to derive major trends in large volumes of text content and to evaluate statistical engagement on different topics.

Machine Learning Platforms: It is used to provide models, compute algorithms, develop APIs and develop training toolsets, provide data to implement models, and design, train and deploy to other machines. Mostly it includes forecasting or classification of tourism data.

Internet of Things: The Internet of Things is a concept of creating online networks in part by placing chips, sensors, and communication modules in the everyday objects, and data in everything that surrounds people.

Complete Chapter List

Search this Book:
Reset