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Two Types of Federated Search
Federated search allows for a more seamless user experience and more dynamic data management, but which kind of federated search will work for your business? First, there’s Search-Time Merging, which creates and maintains individual indexes for each data source. There’s also Index-Time Merging, where a single unified index of content is created from data sources. How do you choose which to implement?
Discover more about each type of federated search by downloading the infographic.
The difference between federated and non-federated data is seen in how the data is distributed and accessed across different systems or information sources. Non-federated data is data that is unified within a single system or source, and that’s where it can be searched and retrieved. It’s siloed in its own database and isn’t shared across systems. Users have to go to that source to retrieve it which limits how the data can be used. Federated data, on the other hand, is data that can be accessed across multiple sources. This data is stored in different systems, however, it’s searchable and retrievable across them. This means a user can perform one search query and access multiple data sources at once making the user experience more efficient, holistic, and streamlined.
Federated search offers a number of benefits that greatly enhance the search experience for users. Because it provides search results from multiple databases and websites, it allows users to easily search across multiple sources simultaneously, providing more comprehensive results. This eliminates the need to input multiple queries on separate websites or databases which saves the user time and effort. Federated search also improves efficiency by providing a single, unified interface for retrieving information from multiple indexes, which streamlines the search process and simplifies the user’s journey. It can also greatly increase search accuracy by pulling up the most up-to-date, relevant, and trustworthy information from across sources. Federated search can also integrate AI-driven personalization features based on user data, providing a more personalized and efficient search experience. This powerful tool helps to access a wide range of information efficiently, accurately, and conveniently.
There are many examples of federated search in any organization. It can be used to centralize access to multiple content management systems like document management systems, knowledge bases, and collaboration platforms. It can also be used to centralize access to customer relationship management systems which can allow customer service agents to retrieve customer data, interactions, and history from multiple platforms or sources for smoother customer management. It can also be used across HR systems allowing employees to search and find training content, employee info, and other relevant information from multiple sources within the organization.
