Building AI-Native Orgs
Learn more about the HyperTrail platform and discover how it transforms business operations and customer experience across industries.
Popular articles
Data Lakes as a Mantra
For the past decade or more, the mantra preached by digital innovation consultants and hyperscale providers has centered on the power of the data lake. The initial promise was liberation from the rigid schemas and protracted negotiation cycles of the traditional data warehouse. The data lake offered a seemingly elegant solution: ingest all your raw data, regardless of structure, and process it later when specific analytical needs arose. This approach was particularly appealing as it allowed companies to capitalize on the low-cost storage offered by cloud providers and avoid the upfront complexities of schema definition.
As the concept evolved, various iterations emerged — data lakehouses, data rivers, and other similar constructs — all reinforcing the idea of a centralized repository for vast quantities of unstructured and semi-structured data. Hyperscalers, in particular, positioned the data lake as the foundational layer for all future data initiatives, including the nascent field of Artificial Intelligence.
However, in the age of agentic AI, where real-time decision-making and autonomous task execution are becoming critical, the fundamental limitations of the data lake architecture are becoming increasingly apparent. While data lakes excel at storing massive volumes of historical data for batch analytics and reporting, they are fundamentally ill-suited for the low-latency, high-concurrency demands of real-time AI agents.
Consider a simple yet powerful use case: sending a hyper-personalized welcome message to a guest arriving at a hotel. This requires reacting to a check-in event from the property management system and tasking an AI agent to craft a compelling message. To do this effectively, the agent needs to quickly understand the guest — their loyalty status, perhaps some publicly available information gleaned in real-time — and tailor the communication accordingly.
In a traditional data lake environment, the agent would likely need to navigate a vast and potentially disorganized repository of data. It would have to sift through numerous schemas, tables, and data silos to piece together a relevant profile. This on-the-fly data analytics at scale is inherently inefficient and unlikely to meet the real-time constraints of the interaction. Despite the years spent diligently gathering data into the lake, it remains unprepared for real-time personalization.
This isn’t a new problem. Enterprises have long struggled with the latency inherent in data lake architectures for even basic real-time use cases, such as building real-time customer 360 views. The very premise of the data lake — process data later — makes it fundamentally incompatible with the immediate demands of agentic AI.
How HyperTrail changes the Data Paradigm
At HyperTrail, we’ve been tackling this challenge head-on. Our key insight is that you don’t necessarily need to fundamentally alter your existing data lake strategy, which remains valuable for a wide range of analytical purposes. The solution lies in creating a real-time, personalized data store that indexes the relevant data as it flows into your lake.
We call this our Entity Store, an event-sourced data repository that utilizes AI-generated connectors to intelligently transform incoming data — even large files or complex objects — and retain only the high-quality, actionable information in a structured, time-series format. This Entity Store is indexed around the concept of creating a high-velocity summary of your data, specifically designed for rapid access by AI agents.
This approach aligns seamlessly with existing data lake initiatives. You maintain your comprehensive, high-quality data in your established bronze, silver, and gold zones, while the Entity Store acts as a high-performance, cache for real-time access in specific use cases.
Returning to our hotel welcome message example, the AI agent can now directly query the Entity Store for the specific guest. A fast, targeted search returns a consolidated entity containing the most relevant information for immediate personalization. The agent no longer needs to wade through the complexities of the data lake to answer the fundamental question: “Who is this guest, and what information can I leverage to create a meaningful interaction?” This enables the effective deployment of agentic AI without requiring a complete overhaul of your data infrastructure.
The beauty of this approach is its non-disruptive nature. You continue your existing data ingestion pipelines into your lake, while simultaneously feeding relevant data streams into the HyperTrail Entity Store. The Entity Store employs rules-based processing to intelligently aggregate related data points — clickstream data, reservations, loyalty profiles — into unified entities centered around key business concepts, such as the customer, the employee, the product, the property or any other enterprise asset. This allows any authorized system or AI agent to quickly retrieve a comprehensive view of a specific entity.
While our initial example focused on customer experience, the Entity Store is designed to be a broad generalization of the customer 360 concept, extending to other critical business entities such as products, locations, and even personnel. The definition of “actionable data” within the Entity Store is key: it’s data that can be used in real-time by either an AI agent or an employee to make a decision or take action, or data crucial for linking different entity facts. Less immediately relevant technical or historical details, while valuable for reporting in the data lake, can be excluded from the Entity Store to maintain its speed and efficiency, thereby lowering costs.
We believe that indexing relevant data in real-time as it enters your data lake offers a significantly easier and more effective path to personalization. It allows you to focus on the immediate needs of the guest or customer interacting with you today, rather than being bogged down by the vast history stored in your data lake. While historical data has its place in understanding trends, real-time insights are paramount for transforming the immediate customer experience.
In conclusion, while data lakes have served (and still serve) a valuable purpose for large-scale data storage and batch analytics, they are not the optimal architecture for leveraging the transformative potential of agentic AI. The real-time demands of intelligent agents necessitate a different approach — one that prioritizes low-latency access to actionable, contextually relevant data. Indexing and serving this data in real-time, while still leveraging the comprehensive storage of the data lake, offers a more pragmatic and effective path for travel brands and other enterprises to embrace the age of AI.
