Evolving User Behaviour with Enterprise Search

The field of Enterprise Search is witnessing a transformative evolution. Based on my experiences in 2024 building and deploying LLM-enabled enterprise search solutions, I’d like to share some insights and observations. For those interested in understanding my journey in enterprise search, I recommend reviewing my earlier blog on this topic.

As enterprise data increasingly becomes centralized within a unified search framework, certain realities come to light. In this article, I’ll explore these challenges and invite readers to share their own experiences or observations.

Key Observations and Challenges

1. Diverse Application Designs: Integrating data from various applications into a centralized search system reveals a wide array of application design styles.
2. Inconsistent Entitlement Management: Applications often manage entitlements in diverse ways, presenting challenges for standardized access control.
3. Layered Access Control: Many applications implement tiered or prioritized access mechanisms, further complicating integration.
4. Vendor Constraints: Some vendor applications lack APIs to expose data for indexing, as they consider application data proprietary intellectual property.

Identity Management vs. Access Management

Successfully integrating applications into an enterprise search system requires a clear understanding of the differences between Identity Management and Access Management. These two disciplines, though interdependent, serve distinct purposes:

Behavioural Shifts in Data Interaction

When enterprise search systems index unstructured data across applications, users can access this data through a unified search interface. With the integration of LLMs and chatbot capabilities, users no longer need to interact with the source application directly, leading to several shifts:

• User Experience Consistency: Application and data owners often expect the user experiences and personas embedded in the original application to be replicated within the search engine interface. This expectation places significant pressure on the search engineering team to ensure consistency in user journeys.

Solution: To address this, search teams should develop SDKs for application teams to build integration connectors, democratizing the process and avoiding bottlenecks in adoption and scaling.

• Data Refresh Frequency: Maintaining data integrity requires search engines to handle dynamic updates, such as changes in data authorizations. While indexing avoids creating local copies and mitigates versioning issues, search systems must stay updated to reflect changes in access rights to prevent unauthorized data access.
• Data Quality and Contextual Relevance: Overlapping data definitions across departments can create context-setting challenges, which gradually improve as more queries train the system. Legacy "garbage data" further complicates search result quality and chatbot effectiveness, requiring robust data governance measures.

Looking Ahead

As enterprise search evolves, the challenges faced by search engineering teams are numerous and complex. It is crucial for stakeholders to be aware of these realities, enabling informed decision-making and continued progress.

If you’ve encountered similar challenges or have insights to share, I would welcome the opportunity to engage in a conversation. Together, we can explore how enterprise search can continue to adapt and thrive in an ever-changing landscape.