Analysis of Big Data Maturity Stage in Hospitality Industry

Big data analytics has an extremely significant impact on many areas in all businesses and industries including hospitality. This study aims to guide information technology (IT) professionals in hospitality on their big data expedition. In particular, the purpose of this study is to identify the maturity stage of the big data in hospitality industry in an objective way so that hotels be able to understand their progress, and realize what it will take to get to the next stage of big data maturity through the scores they will receive based on the survey.

💡 Research Summary

The paper investigates the current state of big‑data adoption in the hospitality sector and proposes a structured maturity‑assessment framework to help hotels gauge their progress and plan the next steps toward advanced analytics. After outlining the transformative potential of big data—ranging from personalized guest experiences and demand forecasting to operational cost reductions—the authors review existing IT maturity models and data‑management standards (e.g., DAMA‑DMBoK). They synthesize these insights into a five‑stage hospitality‑specific maturity model: Initiation, Management, Definition, Optimization, and Innovation. Each stage is evaluated across five dimensions: data infrastructure, analytical capability, organizational culture, decision‑making integration, and performance measurement.

To operationalize the model, the researchers designed a 25‑item questionnaire covering the aforementioned dimensions. Items were rated on a five‑point Likert scale. The survey was distributed online to 158 hotels in South Korea, including major chains and independent properties, achieving an 84 % response rate. Statistical analysis (descriptive statistics, factor analysis, weighted scoring) placed the average respondent in the “Definition” stage (overall score ≈ 3.2/5). While most hotels have established data collection and storage systems, only about 20 % have deployed sophisticated analytics such as machine learning or real‑time streaming. Cultural readiness for data‑driven decision making is relatively high, yet actual integration of analytics into operational workflows occurs in roughly one‑third of the sample. Governance and security practices are underdeveloped, exposing firms to data‑quality issues and regulatory risk.

The discussion attributes these gaps to infrastructure‑first investment strategies, talent shortages, and siloed departmental structures. The authors outline a phased roadmap: (1) Strengthen infrastructure with cloud‑based data lakes and standardized ETL pipelines; (2) Automate analytics using open‑source machine‑learning platforms and CI/CD for data models; (3) Embed analytics into decision processes via KPI‑aligned dashboards and cross‑functional data teams; (4) Drive innovation through personalized services, dynamic pricing, and predictive maintenance.

In conclusion, the study provides a practical diagnostic tool for hospitality executives, enabling them to benchmark their big‑data maturity, identify priority actions, and allocate resources efficiently. Limitations include reliance on self‑reported survey data, insufficient statistical differentiation by hotel size or region, and lack of longitudinal validation with actual operational metrics. Future research is recommended to conduct longitudinal studies, incorporate objective performance data, and develop case‑based deep dives that illustrate successful transitions across maturity stages.