What is Enterprise Search?: Use Cases & 30+ Tools in '24

Enterprise search is a valuable tool for businesses since it allows employees to perform instant searches within the company’s knowledge base. Enterprise search software decreases the amount of time it takes for an employee to find the necessary information, leaving more time for higher value-added tasks. This is especially important for today’s lean, digital, agile organizations that strive to get the optimal performance from their teams.

We answered all your enterprise search-related questions:

What is enterprise search?

Enterprise search is a way of search that helps employees find the data from one or multiple databases in a single search query. The searched data can be, in any format, from anywhere inside the company’s databases, document management systems, e-mail servers, on paper and so on.

Enterprise search needs to be more capable than simple keyword search. It should

• be capable of taking into account relationships between words (e.g. meanings, synonyms, antonyms etc.)

These capabilities are being upgraded by generative AI solutions with enterprise search becoming one of the major generative AI applications. Tech giants like Google are starting to leverage LLMs in enterprise search.¹

The relation between Enterprise Search and Knowledge Management

Knowledge management is the process where value is derived from knowledge by making it accessible to everyone within an organization.

For practical knowledge management, the combination of internal data and web-focused search tools has a crucial role. Enterprise search enables these features in a single search query. Therefore enterprise search can be a key driver for successful knowledge management.

Why is it important now?

Capturing data has never been easier. It is less costly than it was and most enterprises are capturing data as part of their operations. However, optimizing data is as important as capturing it because, in terms of work productivity, it needs to be easier and more accessible to find.

There are various studies that highlight the cost of knowledge search time for employees. Here are some examples:

• ISI2022 survey reveals that employees spend 30% of their day looking for documents.

How does it work?

Enterprise search engines require data preparation. Once data is ready for the search engine, users input text queries and receive formatted results.

Content Awareness

Content awareness also called “content collection”, is the process of connecting databases that the search can access.

Content Processing

Incoming contents from different databases have different formats such as XML, HTML, office document formats, or plain text. In this step of enterprise search, documents are converted to the plain text using document filters so they can be searched efficiently. The content processing phase also includes tokenization. For example, characters are converted to lower case to enable fast case-insensitive search .

Indexing

After the content is processed, documents are stored in an index. This index consists of all words, including information about ranking and frequency of the term.

Serving results to user queries

The search system compares the query to the saved index and returns matching results. The search returns entries that include what the user entered as a query and also returns similar results.

What are its use cases?

Enterprise search engines have some common use cases that increase the efficiency of research processes.³ We listed the five most common use cases for you:

• Knowledge management: Applying enterprise search eases and improves the process of knowledge management within the organization. Better search enables easier discovery and prevents creation of duplicate knowledge.
• Intranet Search: It helps intranet users locate the information they need from the organization’s shared drives and databases.
• Expert discovery: You don’t need to know people’s full names if you are looking for experts within the organization. You can filter according to attributes and experience to find experts.
• Talent Search: Enterprise search engines can match candidates with job descriptions from the database of potential candidates.
• Insight Engines: Insight engines are marketed by vendors as an evolved version of enterprise search incorporating more AI capabilities in search.

What are enterprise search benefits?

Some of the benefits enterprise search provides to your business can be listed as:

1. Increased accuracy: By providing relevant search results based on the user’s query, enterprise search engines can improve the accuracy of information retrieval.
2. Enhanced collaboration: Enterprise search engines can help employees find and connect with others who have the relevant expertise, making it easier to collaborate and share knowledge.
3. Better decision-making: Access to accurate and relevant information can help decision-makers make informed decisions.
4. Improved customer service: Enterprise search engines can help customer service representatives quickly find information to resolve customer inquiries, leading to higher customer satisfaction.
5. Reduced costs: By reducing the time and effort required to find information, enterprise search engines can help organizations save money and reduce operational costs.
6. Ensured compliance: Enterprise search engines can help organizations comply with regulatory requirements by providing access to relevant information and ensuring that sensitive information is properly secured.
7. Boosted efficiency: An enterprise search engine can help employees quickly and easily find the information they need, reducing the time spent searching for information and increasing productivity.

What is the difference between enterprise search and insight engine?

Enterprise search engines and insight engines serve the same purpose; to show the results of business users’ queries. However, insight engines are more advanced platforms and they combine with data and machine learning algorithms to process content so that they can provide more relevant and personalized results for users. On the other hand, enterprise search converts content to plain text by using document filters.

What are enterprise search best practices?

• Autocompletion of queries: Autosuggest feature improves the user experience of your enterprise engine. Make sure you choose a tool that offers a list of possible completed words and phrases when users start typing.
• Apply search analytics: Collect query data of users so that you can gain insights about your search engine performance and the topics researched by your users.
• Evaluate your team’s talent: Assess the capability of your business to implement a solution. If your team is a total stranger to search engine architecture, you can hire a third party integration specialist to help you implement the solution.

What is the maturity of your enterprise search engine?

With the advancements in technology, enterprise search applications got smarter. There are different levels of search capabilities of engines. Before investing in a new enterprise solution, you should assess your current search level and identify your requirements based on your business’ goals.

Here is a model from Accenture to guide you to assess your enterprise’s search engine maturity.

What are the major types of enterprise search solution providers?

Businesses can use both open source and proprietary solutions. Each enterprise search vendor has unique pros and cons. Here is the list of vendors divided into two groups:

Open Source Software

• Apache Solr
• Elasticsearch
• Sphinx
• Terrier
• Xapian

Closed Source Software

• Addsearch
• Algolia
• Commvault
• Concept Searching Limited
• Copernic
• Coveo
• Dassault Exalead
• Dieselpoint
• dtSearch Corp.
• Funnelback
• Hyland
• IBM Watson Explorer
• Lookeen
• Lucidworks Fusion
• MarkLogic
• Micro Focus IDOL
• Microsoft Bing
• Mindbreeze
• Oracle Secure Enterprise Search
• SAP
• SLI Systems
• Swiftype Enterprise Search
• TEXIS
• Varonis DatAnswers
• ZL Technologies

Learn more

A recent development in the field is the conflict between Elastic which manages the elastic search open source software and Amazon which uses that software to offer its Elasticsearch product.

Elastic changed terms of their license to ensure that Amazon does not offer future versions of elastic search without compensating them and as a result AWS team forked the previous open source software to create their new open source version. This is likely to be a conflict that will play out in different markets as open source software providers see cloud giants commercialize their software.

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External Links

• 1. “Vertex AI Embeddings for Text: Grounding LLMs made easy.” Google. May 26, 2023. Accessed May 28, 2023
• 2. “Armedia.” December 11, 2023. 
• 3. Paul Nelson (June 6, 2019).”Ten enterprise use cases for search engines“. Accenture. Retrieved May 28, 2023.