enterprise search

What is enterprise search?

Enterprise search is a type of software that lets users find data spread across organizations' internal repositories, such as content management systems, knowledge bases and customer relationship management (CRM) systems. This software shares similarities with web search engines like Google and Bing, but its search results come from internal company data sources as opposed to the internet.

How does enterprise search work?

Enterprise search tools work in the following three overarching phases as they aggregate data from various sources and make it available to users.

1. Exploration

First, these tools explore and collect all relevant data from organizations' data sources. This can include structured data -- data contained in a standardized format, such as a table -- or unstructured data, which is data stored in less organized formats like text documents.

These tools use built-in web crawlers to extract unstructured data from web-based resources, like company intranets, websites, knowledge bases and social media platforms. To maintain search accuracy over time, enterprise search tools must crawl these data sources regularly. Most tools let users configure how often they'd like to crawl their sites.

Enterprise search engines can also use connectors -- software components that sync data between systems -- to push structured data from repositories, like content management systems, to the search engine. Unlike web crawlers, enterprise search connectors can offer real-time synchronization.

2. Indexing

After the exploration phase, enterprise search tools analyze and enrich the collected data to understand its meaning. Analysis offers insights into the content's meaning and makes logical connections between documents and ideas, while enrichment enhances the content with additional context, such as metadata tags and categorizations.

These processes help the tool index an organization's data in a way that facilitates intuitive search. Some enterprise search tools use AI capabilities, like natural language processing (NLP) and machine learning (ML), to enhance data analysis.

Additionally, as part of the indexing process, enterprise search tools can identify and enforce any permissions associated with documents. For example, they can ensure confidential documents limited to the HR department won't appear in search results for users outside of human resources (HR).

3. Querying

Once an enterprise search tool indexes a company's data, users can query the system for results. In this phase, users ask questions in natural language or input keywords and phrases relevant to the content they want to see. The system then parses the language to understand the searcher's intent. This parsing process can involve simple keyword-matching techniques that look for exact words and phrases in the query. However, many systems use NLP to understand context and complex speech patterns within queries.

After the system identifies a query's intent, it uses algorithms to match it to results based on ranking factors, such as relevance, the user's role, search history and the popularity of the content based on past user interactions. The system then aggregates the most relevant results and displays them on the front end for the user to see.

Enterprise search systems also track and analyze queries to detect trends in user behavior and search patterns. These insights help organizations understand their users' needs, improve search algorithms, and identify gaps in knowledge articles or company training material.

A chart that shows how enterprise search works in three phases.
The enterprise search process occurs in three distinct phases.

Use cases for enterprise search

Enterprise search can help employees, customers and partners find important information. Common use cases include the following:

  • Intranet search. Many organizations use intranets -- private company websites -- to store various types of information, such as HR documents, training materials, project plans and team communications. Enterprise search systems can crawl these sites, which often have thousands of folders, to help users quickly find documents.
  • Company website search. Organizations can use enterprise search tools to help customers find information on public-facing applications like company websites and mobile apps. For instance, a large online retailer might use an AI-powered enterprise search tool to help customers find products they want to purchase.
  • Customer service. Enterprise search can offer customer service agents quick access to knowledge repositories, like training documents, knowledge bases and enterprise wikis, improving problem resolution time.
  • Knowledge management. Valuable business know-how often resides across various repositories within an organization. Enterprise search lets employees easily find knowledge articles and best practice documents spread across the organization.
  • Internal directories. This software search can index the names, email addresses and phone numbers of people, including employees, customers and subject matter experts to offer a comprehensive and easily searchable directory.
  • Talent search. Organizations often store information about employees and potential job candidates, along with their skills and resumes, in HR systems. Enterprise search can help HR professionals search through large talent pools and match candidates with job descriptions.

Benefits and challenges of enterprise search

As enterprise search eliminates data siloes and improves information flow within organizations, it can offer the following benefits:

  • Increased productivity. Many employees spend a significant portion of their workdays searching for information. Enterprise search helps users quickly find documents, letting them spend more time on higher-level tasks, such as speaking with customers or creating marketing strategies.
  • Enhanced decision-making. Enterprise search offers employees simple access to data and documents, such as customer behavior data or best practice documents, that can help them make more informed business decisions.
  • Better collaboration. An intuitive search function lets users easily find and share information across their organization, regardless of which department folder or repository stores the data.
  • Increased sales. This software can boost sales as it integrates with company websites and e-commerce platforms to help customers find desired products.
  • Improved customer experience. Enterprise search engines can offer a smoother CX as they help customer service teams quickly solve problems.

Despite these benefits, enterprise search can pose the following challenges with search accuracy and implementation:

  • Unstructured data. Enterprise search tools can struggle to appropriately index unstructured data, such as text documents and video content, because this type of data lacks predefined tags and metadata.
  • Duplicate versions. As employees edit and update documents, organizations store many versions of the same file. Organizations that don't use version control effectively can see duplicate content clutter their enterprise search results.
  • Security. Enterprise search results can leak sensitive information to the wrong users if organizations don't implement access controls and encryption mechanisms.
  • Installation. Integrating an enterprise search tool with an organization's data repositories can require complex configurations and coding.
  • Crawl errors. Errors with a platform's web crawler can lead to outdated and inaccurate search results.

AI's role in enterprise search

In the 2000s, enterprise search platforms relied heavily on manual content tagging to index unstructured data, like PDF and video files. Yet, the amount of content that organizations created and stored grew massively in the 2010s, making manual content tagging more challenging.

To manage this influx of content, many enterprise search vendors added advanced NLP and ML capabilities to their offerings around the mid-2010s. NLP helps these systems accurately interpret searchers' intents and automatically parse and index massive amounts of unstructured data. ML, on the other hand, can help the tools learn from past searches and automatically improve their ranking algorithms over time.

Advancements in generative AI (GenAI) -- AI that can summarize and create content -- have also begun to reshape how enterprise search works. Unlike traditional search engines that offer results in the form of link or file lists, a GenAI-powered enterprise search tool can answer user questions directly, based on information stored in source documents.

This was last updated in June 2024

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