What is enterprise search?

Enterprise search, or corporate search software, is a solution for finding data and information within an enterprise organization. Content can be both structured or unstructured data, such as product images, or internal business data metrics. Enterprise search software is used for search applications such as web, ecommerce, knowledge bases, and customer service apps.

Enterprise search lets users quickly find content on your site, aiding in productivity and efficiency. It can be customer-facing — like a search bar that allows users to browse your website’s content. Or it can be internal — used by your team to access business information, documents, and all the nuts and bolts of your tech stack, data, and logs.

Enterprise search works by allowing data to be indexed, queried, and displayed to authorized users. It works like any other search engine but is limited to content related to a given enterprise (often internal content).

Often with the help of a web crawler, an enterprise search engine can function in three phases:

1. Exploration: Also referred to as the collection phase, this is the phase in which an enterprise search engine will deploy a web crawler to explore or “crawl” sites and directories for data. The enterprise web crawler then extracts the data for indexing.
2. Indexing: Once data has been extracted by the enterprise web crawler, it is sorted into indexes or relational databases. This makes the data findable when queried. During the indexing phase, the search engine might also perform other processes, such as metadata extraction and auto-summarization. These processes also improve information findability. Indexing can also happen via APIs or connectors — native, which are fully managed, or self-managed using customizable frameworks — that sync data from a data source to your deployment.
3. Querying: When data has been indexed, it can be queried by end-users. In an enterprise, employees can search for information, files, and documents, and receive answers based on their authorizations or permissions. Customers can query product and business information, or search for frequently answered questions. To answer a query, a search engine will review the indexes and look for matches.

However, not all search has to happen with a web crawler — though it is the easiest way. Layers of customization can supplement these phases. With solutions like Elasticsearch, ingest pipelines allow you to transform your data to create consistency before it’s even indexed. With certain enterprise search solutions, natural language processing (NLP) and machine learning (ML) are used to identify and provide relevant results to queries.

There are different types of enterprise search, but they vary in the querying phase of the search.

Siloed: A siloed search will perform an individual search in each repository, and return results by datasource.

Federated: A federated search will perform an individual search that is sent to multiple databases at once. The results will be returned by datasource.

Unified: A unified search will perform a singular search into a given index. After searching across multiple data sources, platforms, and applications, it will return a singular set of results.

AI search: AI search will apply machine learning to a unified index in order to create a more relevant list of results.

Enterprise search technology varies from vendor to vendor, but some key features are common across the board:

Connectors
An enterprise search engine may require data connectors to index data. A connector is a component that uses various protocols to provide a codeless connection to and from different touchpoints. In other words, it enables you to sync data from an original source to an index. Data connectors are an important component of data ingestion, which is key to the functionality of enterprise search.

Data security
Data privacy and security are key components of enterprise search. With some platforms, security features are included as part of their comprehensive service. In others, it is a separate detail to consider.

The software must be configurable so that it complies with corporate security policies and governmental regulations. A corporate search platform should also control accessibility to users with the right permissions. This ensures that critical business information is protected.

Additional components of enterprise search include:

• Analytics and insights: The ability to monitor performance, identify issues and trends, and track patterns empowers teams responsible for administering search to optimize the search experience.
• Machine learning: Incorporate ML into your search platform to integrate the power of generative AI, vector databases, semantic search, and more. Machine learning capabilities enhance search relevance and performance. Machine learning may also include features for transparency and automation such as allowing teams to approve or automate relevant result recommendations based on search history or user behavior.
• Deployment flexibility: Many enterprises have multi-cloud environments and require their corporate search software to be deployed across their ecosystem. A flexible search platform will include the flexibility for an enterprise to run the software on-premises or in multi-cloud or hybrid environments.
• Search UI: An enterprise search engine can give you the power to quickly customize your search capabilities. Corporate search engines usually have a user-facing search bar. This search bar may be configured to use auto-suggest or natural language search to provide users with an intuitive experience.

Efficiency and productivity are key drivers for implementing enterprise search. Internally, enterprise search lets workers quickly find relevant information needed to complete tasks, increasing productivity.

Externally, enterprise search can also translate to profitability. Customer-facing enterprise search is more likely to convert a browsing shopper into a buyer. By improving customers’ on-site experience, by making frequently asked questions easily accessible, or by providing relevant product information or suggestions, enterprise search plays a critical part in customer satisfaction and brand loyalty.

Enterprise search can be used in several different business contexts:

• Ecommerce: Customers can use a search bar to look for products. Depending on the query and search engine, they may get exact matches or relevant matches. Search can also be used to personalize product suggestions and improve the product discovery process with product recommendations.
• Customer support: By providing enterprise search, businesses offer customers access to self-service support knowledge bases. A powerful search tool helps customers to get answers to their questions and resolve their issues quickly. Support agents can also access internal support knowledge bases and technical customer and ticket information to resolve customer issues faster and drive down support costs.
• Workplace search: Enterprise search is also an internal search tool for any organization. A search tool that can connect to all of the data sources and productivity tools used by the enterprise can help increase team productivity by shortening the time it takes for employees to find and access the information and the files they need.
• Website: Enterprise websites can use a search bar to improve website engagement and navigation. Potential customers and web browsers can find what they’re looking for more easily. This can lead to better customer engagement, retention, and brand loyalty.

The future of enterprise search is constantly evolving, driven by a need to improve the user experience. Better search capabilities yield better results and relevance, increasing user engagement. With the advent of large language models (LLMs) and generative AI, search can be programmed to predict and understand user intent, and deliver highly specific responses to queries. The desire to incorporate recent technologies like ChatGPT also requires highly scalable toolkits.

Vector databases also play a part in the future of enterprise search. By enabling more complex data retrieval that is not limited to keywords and synonyms, vector database-integrated enterprise search can deliver a multi-modal search experience.

Deep dive into 2024 technical search trends. Watch this webinar to learn best practices, emerging methodologies, and how the top trends are influencing developers in 2024.

Elastic offers enterprise search with Elasticsearch — a simple to set up, flexible search platform that lets developers build search experiences to meet business requirements and deploy them anywhere.

With Elastic, development teams can build customer-facing search experiences that are user-friendly and leverage the power of AI. Elastic also offers workplace search for internal enterprise search, which helps teams increase productivity and efficiency with knowledge sharing and content retrieval.

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