Real-time data processing and analytics to be a game changer for the travel sector

Love Ojha Vice President - Digital Transformation, IGT Solutions tells VARINDIA of the challenges that the travel industry faces when it comes to primary data management and the how with the increased adoption of AI and machine learning, the industry will undergo a rapid transformation in terms of providing a seamless and efficient customer experience -

What are the primary data management challenges faced by companies in the travel and transportation industry?

The travel industry faces significant data management challenges, driven by the complexity managing high volumes of data across numerous customer interactions, operational processes, and regulatory requirements. Companies must handle data from diverse functions—such as bookings, claims processing, and customer support—requiring secure, flexible, and scalable solutions.

One of the biggest obstacles is the reliance on legacy systems, which often leads to slow response times, high operational costs, and susceptibility to human error due to manual data handling. Additionally, the industry needs to support modern technologies like generative AI, large language models (LLMs), and multi-cloud compatibility to ensure seamless performance. Another critical issue is the lack of a unified vocabulary across various systems and departments. This leads to inconsistencies in how key entities are defined, creating ambiguity and complicating data integration, reporting, and analysis. Without a standardized terminology, different systems may interpret the same data points in different ways, leading to errors and inefficiencies.

How does integration of different data sources become a challenge and how in your views can an effective data management address it?

Integrating data from multiple sources presents several challenges, primarily due to differences in data formats, storage structures, and compatibility. These variations often lead to data silos, where data is isolated in different systems, making it difficult to consolidate and analyze. This issue is particularly pronounced when working with legacy database management systems (DBMS), which were designed for simpler, more static datasets. The problem becomes even more significant when building modern applications, such as AI-driven systems, which require vast amounts of data from diverse origins. Traditional relational databases, which were designed for a time when data was more structured and less dynamic, often struggle to meet the needs of modern applications that require flexible, scalable data integration

We needed flexibility, scalability and high performance, as well as something that worked for us in terms of cost, writing style, and usage. MongoDB’s document data model makes it convenient to integrate diverse data types at large scale. Additionally, the platform they provide also includes MongoDB Atlas Vector Search which allows us to seamlessly and securely build intelligent applications over any type of data using full-featured vector database capabilities.

In what ways does MongoDB's document-oriented model enhance the management of both structured and unstructured data for IGT Solutions?

MongoDB’s document-oriented model enhances IGT Solutions' data management by offering flexibility to handle both structured and unstructured data. It allows seamless integration of diverse data types, like customer records and text-based support tickets, without extensive schema changes. This flexibility supports IGT’s AI-driven solutions like TechBud.AI, enabling efficient processing and faster insights. Additionally, MongoDB’s multi-cloud capabilities ensure scalability and cross-cloud compatibility, meeting IGT’s growing data needs. The ability to store and process data in a unified platform reduces complexity, accelerates development, and improves performance, all while supporting IGT’s AI and customer experience initiatives.

How does IGT Solutions utilise MongoDB Atlas to ensure data security and compliance across its applications?

IGT Solutions utilises MongoDB Atlas to ensure data security and compliance across its applications by leveraging its multi-cloud architecture, which allows for flexible data residency options to meet regulatory requirements. The platform offers robust security features, including encryption of data at rest and in transit, along with strict access controls to safeguard sensitive information.

What specific features of MongoDB were beneficial for IGT Solutions?

IGT Solutions benefits from several key features of MongoDB Atlas. Along with the benefits of the document model, three other advantages stand out:

·  Multi-cloud – MongoDB Atlas enables IGT Solutions to deploy applications across various cloud providers (AWS, Azure, Google Cloud), providing flexibility that supports data residency requirements and regional compliance. This multi-cloud capability enhances operational resilience and allows IGT to leverage the best features of each cloud platform.

·  AI functionality with Vector Database – The integration of operational and vector data storage on a single platform simplifies the development of intelligent applications, facilitating the use of both structured and unstructured data. This reduces complexity and improves efficiency for IGT's development teams.

·  Automation and monitoring to reduce manual toil – Additionally, MongoDB Atlas automates essential database management tasks such as backups, scaling, and updates. This automation alleviates the operational burden on IGT’s IT teams, allowing them to focus more on innovation rather than routine maintenance. MongoDB Atlas also includes tools for continuous monitoring of database performance, enabling IGT to quickly identify and resolve potential issues in a cost effective manner.

What future trends in data management should companies in the travel sector be aware of?

We have been in this space for two and a half decades. That’s a pretty significant time, and our team now has gained a lot of insight into how the industry works. Many of our clients are leaders in the travel and hospitality sector—airlines, travel operators, industry bodies, and service and facilities providers. What we hear from them is that they really want to take advantage of AI and are running small scale experiments but are still often far away from full scale implementation.

In the short-term clients will be wanting to continue to dip their toes – test out different AI services, LLMs and use cases. They will want to do it without having to worry about switching between platforms or databases. So, portability will be key to allow companies to change course without having to make wholesale changes.

The second piece will be that companies are going to discover that building great AI experiences is hard. We think they will increasingly turn to automated case-specific tools built by experts in an industry. For example, that’s why we built Techbud.ai, a comprehensive generative AI platform. The company offers several specialized AI-powered solutions for the travel and hospitality industry. There are tools that cover the full journey lifecycle, from inspiration and planning, to itineraries, baggage handling, customer requirements, and feedback. We’ve evolved from being simply parts of a PoC to becoming an entire end-to-end intelligence suite that can cover any function or industry.

 Overall, we believe that the increased adoption of AI and machine learning for personalized services and predictive analytics, along with the growing importance of real-time data processing and analytics, will be a game changer for the travel sector. These technologies will not only enable more tailored customer experiences but also enhance operational efficiency by predicting trends, optimizing routes, and responding swiftly to changing conditions.