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Transforming Hotel Data Analytics : Resilient Data Warehouse

The travel and hospitality industry is evolving through the adoption of data analytics and BI solutions. This is done by modernizing the hotel data analytics infrastructure. Here, we’ll discuss the ways to build a secure and scalable data warehouse and the role of analytics in the industry. 

The hospitality industry is investing in data analytics and business intelligence to effectively manage the increasing demand from customers. Data analytics helps unlock the hidden trends and patterns in large amounts of data to understand customer behavior, preferences, likes, dislikes, etc. It enables hotels and similar service providers to streamline operations and personalize offerings based on customer requirements. 

The global hospitality market is valued at $3.95 million in 2024 and is expected to touch $7.239 million by 2027 at a CAGR (compound annual growth rate) of 10.62%. Hotels can adopt data-driven models to derive real-time hospitality business intelligence insights and reports to make faster and better decisions. For this, you need to invest in a reliable, secure, and scalable data warehouse. 

In this blog, we’ll read more about the role of data analytics in the industry and the best practices to follow when building a data warehouse for hotel data analytics. 

The global hospitality market is expected to touch $7.239 million by 2027

What is Hospitality Data Analysis?

Hospitality data analytics is the use of analytical tools to process historical and real-time data from the hospitality industry. It is a powerful tool that can positively impact various aspects of the business, such as customer experience, marketing, pricing, food and beverage sales, occupancy rates, etc. 

Hotels prefer to partner with a reliable hospitality data analytics company to set up the necessary IT infrastructure for implementing data analytics and business intelligence. The service provider will build a data warehouse and integrate it with BI tools like Tableau, Power BI, etc., and create custom dashboards for employees to access the insights in real time. 

Best Practices for Building a Secure and Scalable Data Warehouse

The global active data warehousing market touched $10.8 billion in 2023 and is expected to reach $21.5 billion by 2032 at a CAGR (compound annual growth rate) of 7.68%. 

The data warehouse is a central repository storing massive amounts of data collected from multiple sources. It can be integrated with numerous third-party applications to run real-time analytics and derive business intelligence reports. However, building a data warehouse requires planning and expertise. You should ensure it is secure, scalable, and built using the best tools and technologies in the market. 

Typically, a data warehouse contains three main components  

  • Data sources layer 
  • Data integration layer
  • Data storage and retrieval layer 

Each layer is equally important and has definite purposes. These have to be aligned with your business requirements and long-term goals. The data warehouse is not limited to the present but is a tool for the future. That’s why data warehouse developers and service providers follow the below-listed best practices to deliver the best travel analytics solutions to businesses. 

The main components data warehouse contains

Choosing the Technology 

A data warehouse can be built using various databases like traditional relational databases, open-source solutions, cloud-based databases, columnar databases, etc. Here, you choose a type based on your business volume and future plans. Since it gets expensive to build multiple data warehouses, you need a model that can be easily scaled and expanded as your business grows. That way, you add more layers to the existing model without starting from scratch every time. Moreover, the data warehouse should continue to deliver efficient results without lags and breakdowns. Open-source and cloud-based data warehousing models are preferred in today’s world due to the flexibility and customizability they offer. 

Designing the Data Warehouse Model 

The data modeling method you select affects the analytics and insights you derive by processing the datasets in the data warehouse. Go for reliable data modeling techniques like snowflake schema or start schema as they allow optimization of data retrieval. This leads to efficient query processing without consuming too many resources. You should also consider the types of queries you will use during day-to-day work. For example, a hotel employee has to constantly track the number of guests, advance booking, available free rooms, etc. 

Streamlining the ETL Process 

ETL stands for Extract, Transform, and Load. This stage focuses on extracting data from multiple sources and transforming it into structured formats to eliminate redundancy and then loading it into the data storage systems. With the continuous generation of data in the hospitality industry, the derived hotel data analytics can be accurate when the ETL process is efficient and free of errors. Techniques like parallel processing, data validation, etc., can enhance the ETL pipeline and create seamless data flow in the establishment. 

Ensuring Data Integrity and Consistency 

Data is the core of data-driven decision-making models. Insights derived from low-quality data can be unreliable and incorrect, leading to wrong decisions. This can be very costly, especially in the hospitality industry, where customer experience is a priority. For a hotel to derive accurate and actionable insights, the input data used for hotel data analytics has to be of top quality and free of mistakes and duplication. The data warehouse should have the means to implement data checks at various stages to increase overall consistency and quality. Data profiling techniques have to be implemented to detect anomalies in data sets, tags, etc., and highlight missing or incorrect values before the data is used for business intelligence reporting. 

Focusing on Scalability and Performance 

With new data being created every minute, you should inevitably prepare to scale the data warehouse periodically. There are different ways to scale, such as horizontal scaling, vertical scaling, data compression, indexing, partitioning, etc., that allow the central database to accommodate more data for storage and analytics. At the same time, weighing down the data warehouse with massive amounts of datasets can result in lags and delays. This has to be countered to ensure the efficient performance of the data analytics tools. Hospitality data analytics company like DataToBiz helps businesses find the best solutions to ensure scalability and performance in the present and future. 

The Need for Data Backup and Recovery Planning 

Data backup is a must for every business. Data loss is one of the biggest concerns and cannot be ignored. You should have comprehensive data backup and recovery plans when building the data warehouse. The central repository has to be as fail-proof as possible so that you don’t end up losing valuable information. Automating data backup will ensure that the collected datasets are frequently saved in remote locations (data centers) and can be accessed anytime. 

Taking Care of Data Security 

Another vital aspect to consider is data security. Depending on your location and operations, you will have to adhere to security regulations like GDPR, SOC, HIPAA, etc. Be it personal identification information or financial transactions and details, every piece of information belonging to your hotel and your customers has to be safeguarded 24*7. While a data warehouse is easy enough to maintain, it also requires robust security measures to prevent unauthorized access and cyberattacks. Make sure to set up database-level rules to provide restricted access to employees. Data encryption, slave read-only replicas, and custom user groups are common ways to ensure data security. 

Managing Maintenance and Support

Building a data warehouse is not a one-time task. Once the data repository is ready, it has to be continuously monitored and maintained to ensure its reliability. This can be automated by implementing monitoring tools that track performance and calculate the amount of resources utilized to run the queries. This also makes it easier to identify bottlenecks and resolve issues to increase overall performance. A well-maintained data warehouse results in accurate business intelligence in the hospitality industry. The service provider who sets up the data warehouse will also take care of its maintenance and upgrades if required by the establishment. 

How do hotels leverage hotel data analytics?

Hotels, restaurants, resorts, and businesses from the travel and hospitality industry can use data analytics in various ways. It is a comprehensive solution to optimize resources, streamline workflow, and ensure customer satisfaction. 

Here are a few use cases where travel analytics are helpful: 

  • Price optimization 
  • Personalize guest’s experience 
  • Streamline marketing and promotions 
  • Reputation management 
  • Competitor analysis 
  • Sustainability management 
  • Greater operational efficiency 
  • Employee performance management 
  • Risk management 

What are the 5 Types of Data Analytics?

Business analytics in the hospitality sector is diverse and uses different types of analytics to get in-depth actionable insights from hotel data. 

Descriptive Analytics: 

It describes the events that happened in the past by analyzing historical data. For example, you can find the average duration of stay and if there’s a preference for a room/ floor. 

Diagnostic Analytics: 

It helps understand why the events have happened by tracing the patterns in the past data. For example, if the bookings have gone down for a month, you can find out the reason for it. 

Predictive Analytics: 

It is a combination of statistical techniques and machine learning algorithms to determine the occurrence of future events. For example, you can predict when there will be more bookings so that you plan for it in advance. 

Prescriptive Analytics: 

It helps determine the best action to achieve your goals by considering various scenarios. For example, you can find the right channel to reach out to a certain category of customers and bring them to your business. 

Sentiment Analytics:

It analyzes customer and market data to understand which sentiments drive customer decisions, and this information can help your hotel. For example, you can identify the areas of improvement by processing customer feedback. 

What Type of Data Do Travel Companies Collect?

The best travel analytics solutions use data from different sources to get a comprehensive picture of market and customer requirements. The collected data can be broadly categorized into the following: 

Social Media Data: 

Posts, reviews, comments, tags, mentions, etc., on social media platforms like Facebook, X, Instagram, TikTok, etc., are collected to understand if customers are happy with the business, identify areas for improvement, and track if competitors are trying to sabotage the company. 

Transactional Data: 

Invoices, bills, receipts, deliveries, storage data, bookings, payments, etc., come under this category. It provides insights into the financial aspect of the business. 

Machine Data: 

Data stored in system software and applications is called machine data. It contains insights on customer behavior, booking patterns, employee performance, and more. 

What type of Data Do Travel Companies Collect?


Data analytics and data warehouses are necessary investments for hotels to evolve in today’s scenario. Traditional systems have limited use and can be enhanced by implementing modern and digital solutions to analyze business data.  

Find a trustworthy partner to implement the best BI solutions for hotel management. Start by building a secure and robust data warehouse to store and analyze quality data for effective decision-making. 

Talk to us for more information. 

Fact checked by –
Akansha Rani ~ Content Creator & Copy Writer
Tejeswini N ~ Digital Marketing Intern & Content Writer

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