Types of enterprise search

There are 4 different types of Enterprise Search:

The four different types of Enterprise Search

1. Siloed Search

  • How it works: Performs separate searches within each data repository (like a file system, database, or intranet). The results are then presented by data source.
  • Best for: Smaller organisations with limited data sources or very specific search needs within certain datasets.
  • Example: A small company's HR department uses a dedicated search tool for searching resumes and another search tool for searching the employee handbook. Searching across both these sources requires individual searches.

2. Federated Search

  • How it works: Broadcasts a single search query to multiple data repositories simultaneously. Results are returned and aggregated, but usually still presented by their original source.
  • Best for: Organisations that need to search across multiple systems but don't require extremely sophisticated results blending.
  • Example: A university library system offers a search interface that queries the library catalogue, their digital article archive, and institutional repository simultaneously. The search results page shows separate tabs for books, articles, and theses.

3. Unified Search

  • How it works: Gathers data from various systems into a single, centralised index. Searches are performed on this unified index, providing a single list of results with blended relevance.
  • Best for: Larger organisations with complex data landscapes where providing a seamless, cross-system search experience is essential.
  • Example: A large tech company implements a search solution that indexes their codebase, internal documentation, employee directory, and customer support tickets. Employees can find what they need from all these systems with a single search.

4. AI-Powered Search (Cognitive Search)

  • How it works: Leverages advanced techniques like natural language processing (NLP) and machine learning to understand the intent behind search queries, improve search relevance, and provide more insightful results.
  • Best for: Organisations dealing with large amounts of unstructured data, where users want intuitive search experiences similar to popular web search engines.
  • Example: An e-commerce site uses an AI-powered search solution that understands synonyms, handles misspellings, and suggests products based on user behaviour. A search for "summer dress" might return results for sundresses, maxi dresses, and other warm-weather attire.

While all of them work differently, they’re all advancements on each other. At the core, all of them even follow the same process.

Ishaan Gupta
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Ishaan Gupta is a writer at Slite. He doom scrolls for research and geeks out on all things creativity. Send him nice Substack articles to be on his good side.

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