What is Unified Search and How Does it Compare to Federated Search?

Imagine you're a sales executive searching for specific information that could help close a deal. You dig through multiple databases, file systems, and cloud services but can't find what you're looking for. This is where unified search and federated search come in - two search systems designed to help users access information from multiple sources quickly and easily. As the way people search changes, traditional keyword-based search engines are no longer enough. In this article, we'll explore the differences between unified search and federated search and how they can improve the search experience for users.

Decoding search performance

The way people search for information online is constantly evolving, and traditional keyword-based search engines are struggling to keep up. People increasingly use voice searches, natural language queries, and long-tail keywords. Over one-third of all internet searches are now longer than four words. As a result, traditional search engines that rely solely on exact keyword matches are becoming less effective.

One example of this shift is in the way people search for products. People often searched for generic terms like "shoes" or "jackets" in the past. However, today's shoppers use more specific search queries, such as "black leather ankle boots with a low heel" or "waterproof jackets for hiking". This shift in search behavior has significant implications for businesses trying to reach customers online.

Search engines are adopting new query systems to keep up with these changes. Traditional search engines simply match search terms with pages on the internet that contain those exact terms. However, this approach is insufficient to deliver the most relevant and accurate results. That's where federated and unified search comes in.

Federated search is a method of searching across multiple data sources simultaneously. The results are then "federated" or combined into a single set of results, making it easier for users to find what they're looking for. Federated search is often used to search across multiple databases or catalogs, and it can be instrumental in organizations where data is stored in multiple silos.

Unified search, on the other hand, is the act of searching for content across multiple silos at once. This could be within a single enterprise, across an intranet, or across the entire internet. A search engine like Google is an excellent example of unified search. Unified search typically retrieves results from a single index, making it more efficient and streamlined than federated search.

As search behavior continues to evolve, unified search will become even more critical. By providing a more comprehensive and efficient search experience, unified search can help businesses deliver more relevant and accurate results to their customers.

But before we really come to such conclusions, it's only fair to dive deeper into both the federated and unified search to understand what they're really about!

What is federated search and How it works

Federated search is a type of search that allows users to search multiple databases or other data sources simultaneously. When a user enters a search query, the federated search engine searches across all the data sources and combines the results into a single set of results. This can make it easier for users to find what they want, as they don't have to go to each data source separately.

Federated search sends the user's query to each data source in real time, collects the results, and presents them to the user. To do this, the federated search engine must have access to each data source's search interface. This can be challenging, as different data sources may use different search interfaces or protocols. To address this challenge, some federated search engines use connectors - software modules that allow the federated search engine to interact with various data sources.

However, despite its benefits, federated search has several limitations. The limitations of federated search include the following:

1. Incomplete content coverage

Federated search doesn't crawl or index all the content within an organization like unified search does. As a result, some content may be missed when using federated search. This limitation is because federated search requires individual data sources to be queried separately.

2. Reliance on metadata

Federated search often relies on metadata to identify relevant information. Metadata can be incomplete or inaccurate, leading to poor search results. This can occur because metadata is often created by humans, who may need to apply consistent standards or remember to include important information.

3. Slower performance

Federated search can be slow and difficult to use because it relies on querying each data source separately. This process can be time-consuming and inefficient, especially for users searching for information across multiple data sources.

4. Security risk

Federated search poses security risks because it pulls information from multiple sources. This increases the risk of sensitive data being exposed. For example, a user may search for a sensitive file across numerous data sources using federated search, which could inadvertently expose the file to unauthorized individuals.

Despite its limitations, federated search is helpful in specific contexts. For example, federated search is often used to access specific data types, such as library catalogs or databases. It can also be helpful for organizations with multiple data sources that are not integrated into a unified search index.

Now, let's understand unified search to see what the contrast is about! 

What is unified search (and how it makes search smarter and faster)?

Unified search is a newer approach to search that allows users to search for information across multiple data silos or sources more comprehensively and efficiently. Unlike federated search, which searches multiple silos in a more piecemeal fashion, unified search combines results from all relevant data sources into a single index, offering a more complete and streamlined search experience.

A search engine like Google is an excellent example of unified search in action. When you search for something on Google, you're not just searching the web but also images, videos, news articles, and more, all in one place. This is made possible through Google's sophisticated indexing algorithms and machine learning capabilities that can quickly sift through billions of data points to provide the most relevant results to the user.

In the enterprise space, unified search can be even more powerful. Companies can use unified search to combine data from all their different systems and platforms, including CRM, HR, and finance applications, into a single index that can be quickly searched and accessed by employees. This means that employees can find the information they need quickly and easily, without navigating multiple systems or data silos.

Unified search uses a single search index to retrieve and display search results. This index is created by indexing all the data sources the search engine is designed to search. The data is then analyzed, and the search engine identifies relevant search terms and weights and stores the results.

Unified search has several advantages over federated search, including faster search times, more complete search results, and a more streamlined user experience. As a result, it has become increasingly popular in the enterprise space, where companies are looking for ways to improve search performance and make data more accessible to employees.

The difference between federated search and unified search

While each approach has its strengths and weaknesses, unified search is generally considered a more efficient and effective way of finding the information you need.

While both federated search and unified search allow users to search across multiple data sources, there are some key differences. For example, unified search retrieves results from a single index, while federated search retrieves results from multiple indexes. Additionally, unified search typically offers a more comprehensive and streamlined user experience, while federated search can be more complex and time-consuming.

Here are some more differences between unified search and federated search:

1. Scalability

Unified search is more scalable than federated search. Since it retrieves results from a single index, adding more data to it is easier as the organization grows. In contrast, federated search can become more complex and challenging to manage as data sources increase.

2. Speed

Unified search is generally faster than federated search. Since it only needs to search one index, the search process is simpler and faster. In contrast, federated search requires searching multiple indexes, which can be slower and more complex.

3. Relevance

Unified search can often provide more relevant results than federated search. Since it searches a single index, it can apply more advanced ranking algorithms and machine learning techniques to improve the accuracy of the search results.

4. Ease of Use

Unified search is typically easier than federated search. The user interface is more straightforward and intuitive, making it easier for users to find the information they need. In contrast, federated search can be more complex and challenging to navigate, especially for less experienced users.

5. Cost

Unified search can be more cost-effective than federated search. Since it only needs to search a single index, it can be less expensive to deploy and maintain than a federated search solution that needs to integrate with multiple data sources.

Overall, the choice between unified search and federated search will depend on your organization's specific needs. While federated search may be more suitable for some use cases, unified search is generally considered a more efficient and effective way of finding the information you need. 

Conclusion

One example of a unified search tool is Zevi, which enables users to search across multiple data silos and retrieve relevant information quickly and easily. With features such as natural language processing and machine learning, Zevi can improve search accuracy and provide personalized results to users. Search UX is of supreme importance to users today, and with an intelligent search solution, you will ensure that your customers feel like they're doing no work while they get all the information they desire.