A Guide to Data Analytics in the Travel Industry

By Talo Thomson

Published on March 21, 2023

Aerial view of a large airport with long runways to provide an example of data analytics in the travel industry

Today, modern travel and tourism thrive on data. For example, airlines have historically applied analytics to revenue management, while successful hospitality leaders make data-driven decisions around property allocation and workforce management.

While this industry has used data and analytics for a long time, many large travel organizations still struggle with data silos, which prevent them from gaining the most value from their data. To fully realize data’s value, organizations in the travel industry need to dismantle data silos so that they can securely and efficiently leverage analytics across their organizations.

What is big data in the travel and tourism industry?

Organizations in the travel and tourism vertical use big data and analytics to find patterns in structured and unstructured data that allow them to make informed business decisions. As an industry with tight margins, travel and tourism companies can use analytics to detect trends that help them reduce costs, decide future product and service offerings, and develop successful business strategies.

For example, companies in this vertical can use big data and analytics to:

  • Forecast customer demands

  • Personalize services

  • Market travel packages

  • Optimize pricing

Increasingly, companies like Expedia may combine these capabilities into a single package that, for example, bundles hotel and airfare packages at a reduced rate and markets these packages to a targeted group of people.

What types of data are collected?

Travel organizations collect different data using various sources. Some common data sources in the travel and tourism industry include:

  • User-generated content (UGC): Data obtained from questionnaires and social networks, including photo or survey data

  • Device data: Data obtained from third-party resources that includes GPS data, mobile roaming data, Bluetooth data, and mobile browser data

  • Transaction data: Data obtained from web services, such as Google Analytics, that includes web search, web page visits, or online booking information

Businesses that can integrate this data across silos (and build more comprehensive user profiles) boast a significant competitive advantage.

Why is data analytics important for travel organizations?

Travel can be stressful and emotionally fraught. With data analytics, travel organizations can gain real-time insights about customers to make strategic decisions and improve their travel experience. By aggregating and analyzing data from multiple sources, these companies can understand customer behavior and market trends so they can provide the types of customer experiences that create brand loyalty.

For example, if an airline needs to cancel a flight, it can leverage data analytics to notify customers of the change and help them adjust their travel plans. This can transform a would-be bad experience into a positive one, making people less likely to leave bad reviews (and more likely to recommend the airline to others).

What are common data challenges for the travel industry?

Some companies struggle to optimize their data’s value and leverage analytics effectively. When companies lack a data governance strategy, they may struggle to identify all consumer data or flag personal data as subject to compliance audits. They may also suffer from data duplication, which undermines their analytics models.

Data security

The travel industry collects, transmits, processes, and stores a wide range of personally identifiable information (PII) from customers, which are of interest to bad actors, as cybercriminals target this valuable data. What’s more, many companies struggle with rigid legacy technologies that increase the risk of a data breach.

Regulatory compliance

Additionally, this PII is often highly regulated. All companies in the travel industry that collect PII need to comply with privacy laws like the European Union General Data Protection Regulation (GDPR) or the California Privacy Rights Act (CPRA). These regulations introduce two particular challenges that these companies must address to remain compliant:

  • Right to be forgotten: Companies need a mechanism to delete all of a consumer’s PII upon request

  • Right to access: Companies need to provide consumers copies of or access to the PII they use when a person requests it

Companies must also provide paths to consumers wishing to make these requests.

Data access management

Both data privacy and security require an organization to have appropriate data access controls in place. Companies need to ensure they grant access only to resources and data that people need to perform their job functions. Otherwise, they risk a data privacy violation.

Lack of data culture

While many companies want to be data-driven, many lack a data culture where everyone in the organization values, practices, and encourages responsible data use. Without a strong data culture, the organization is unable to align data and analytics to business outcomes because people don’t have access to the data that allows them to achieve these goals.

Data ethics

Without effective data governance, many organizations lack the ability to manage consumers’ data ethically. Ethical data management requires travel organizations to go beyond the minimum baseline requirements of data privacy and protection law and focus on building trusting relationships that ensure data trustworthiness.

How is data analytics used in the travel industry?

The travel and tourism industry can use predictive, descriptive, and prescriptive analytics to make data-driven decisions that ultimately enhance revenue, mitigate risk, and increase efficiencies. Below are a few examples:

Revenue management and optimization

Big data analysis enables companies to make data-driven decisions about pricing based on historical transactional data. Data analytics offers a comprehensive view of what is or isn’t working, and these insights can inform new business goals and drive revenue optimization.

Customer experience personalization

Aggregating and analyzing data across customer touchpoints enables companies to understand consumer preferences and expectations. When companies know this, they can offer experiences and upgrades tailored to unique consumer desires.

Data-driven marketing

Using historical customer data, companies can engage in data-driven marketing of travel deals or pricing based on people’s known interests and needs. For example, travel websites can leverage transaction data to encourage more clicks and conversions.

Audience segmentation

With big data and analytics, travel companies can create more detailed marketing segments that drive better customer journeys. For example, they can create micro segmentations that incorporate multiple factors such as:

  • Age

  • Motive

  • Socioeconomic status

  • Reason for travel

  • Geographic region

These micro segmentations enable travel businesses to market more effectively to unique consumer types.

Seasonality and trend predictions

Many online travel companies use dynamic and flexible pricing strategies to respond to changes in demand and supply. Using predictive analytics, travel companies can forecast customer demand around things like holidays or weather to set optimum prices that maximize revenue.

Reputation oversight

While reputation is important across all industry verticals, it’s particularly critical in the travel industry. Consumers often look at online reviews or talk to friends before making a decision. With advanced analytics, travel organizations can engage in sentiment analysis to identify common sentiments and resolve problems, mitigating revenue and brand impact.

Use cases for analytics in travel and tourism

How can travel and tourism companies use data analytics to improve business ROI? Below are a few examples demonstrating how these organizations wield data as a strategic asset for the business.

Airlines Reporting Corporation (ARC)

This data company acts as a vital intermediary between airlines and travel groups like Kayak or Expedia, settling transactions and offering data products about travel to third parties.

Having been in business for over 50 years, ARC had accumulated a massive amount of data that was stored in siloed, on-premises servers across its 7 business domains. When it embarked on a digital transformation and modernization initiative in 2018, the company migrated all its data to AWS S3 Data Lake and Snowflake Data Cloud to provide accessibility to data to all users.

Using Alation, ARC automated the data curation and cataloging process. “So much is automatic — the metadata extraction, curation, labeling, query log ingestion, and building out the lineage — it’s a big help,” says Leonard Kowk, senior data analyst. By automating these time-consuming processes, ARC was able to achieve a faster time-to-value for its digital transformation strategy.

Today, ARC data project teams use Alation’s self-service capabilities to conduct their own research, accelerating time-to-market for new products.

Virgin Australia

A competitor in the Australian aviation landscape for over 20 years, Virgin Australia’s expanded IT infrastructure led to a heavily siloed architecture that created communication gaps across its business lines. With business areas interpreting data differently, executives received inconsistent information, which hindered informed decision-making.

To become a data-driven organization, the Data Platforms team chose Alation because it provided a business-oriented, easy-to-use solution that enabled collaboration across all business units. By building a governance framework to address data usage and quality issues, Virgin Australia was able to standardize definitions to facilitate data discovery and build trust.

Today, Virgin Australia uses Alation to implement a consistent data quality management strategy that provides executives with actionable business insights.


Finland’s national airline, Finnair, wanted to break down data silos to standardize metrics and support better communication across teams. Finnair chose Alation because it was easy to use for all users, from nontechnical to data experts and auditors. They found people could easily learn the intuitive platform (giving them a faster time-to-use) while also supporting advanced analytics and the unique needs of data auditors.

With Alation’s secure and scalable cloud-based platform, Finnair now has business intelligence dashboards and reports for a single source of truth across operations, customer experience, and financial teams. To support documentation, Finnair leverages Alation’s artificial intelligence (AI) and machine learning (ML) metadata recommendations to ensure data quality.

Today, Finnair uses Alation for data discovery, enabling people to share knowledge across the organization by searching for a term or phrase to find data sources and articles. “Alation enables us to make more informed decisions and create more personalized encounters with our customers,” says Minna Kärhä, Finnair’s head of data. “We have been able to combine datasets that we didn’t combine before.”

How Alation supports data analytics in the travel industry

Alation Data Catalog automates time-consuming manual processes that create barriers for travel organizations who want to establish a data culture. With Alation, you break down data silos and establish a standardized vocabulary across the organization so everyone has access to only the data they need to provide business insights that reduce time-to-market.

Our AI/ML drives pattern recognition to give you insights into how people use data so that you can implement data governance policies and procedures aligned with user needs. Our natural language search enables you to reinforce your data governance strategy without compromising usability, ensuring that everyone has the best and most relevant data whenever they need it.

Curious to see Alation in action?Join a demo to learn how a data intelligence platform can revolutionize the data strategy at your organization.

  • What is big data in the travel and tourism industry?
  • Why is data analytics important for travel organizations?
  • What are common data challenges for the travel industry?
  • How is data analytics used in the travel industry?
  • Use cases for analytics in travel and tourism
  • How Alation supports data analytics in the travel industry


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