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What Is Enterprise Search?
Enterprise search refers to the process of querying digital content from multiple data sources via a single search bar. It is typically used for web, ecommerce, customer service, and knowledge access. It is also foundational for grounded generative AI applications. The most efficient type of enterprise search is unified.
A great enterprise search tool lets employees search across many enterprise content sources - like Salesforce, blogs, Google Drive, SharePoint, Slack, etc. - to find the most relevant content. We highlight the necessary elements, why it is important, the different enterprise search methods, and how they work.
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Unify structured and unstructured content in a single indexUnified Index
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Flexible, multi-tenant cloud infrastructureSearch-as-a Service
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Embrace AI and the efficiencies of automationMachine Learning
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Detect user intent via signals like clicks and time on page and understand the journeyBehavioral Data
Sometimes called information retrieval, enterprise search is a critical solution for knowledge management and collaboration. And it’s crucial for customer service.
The promise of enterprise search is that a person can discover the most relevant data and content that exists across enterprise data sources through a single interface. This helps improve proficiency, productivity, and employee satisfaction. It also can lead to greater profitability - as now critical, customer-facing information can be found quickly.
When employees need to open documents, read them, assess if they are the right ones (or move on to the next ones), and then rinse and repeat dozens of times in a day, it creates stress. Compound this by the number of possible documents - and the number of questions an employee will wonder about.
No wonder they are burnt out.
Enterprise search software may go by several names, including cognitive search engine, insight engine, unified search, federated search (although for some, federated search may connote data warehouses and data lakes).
However, for an enterprise search engine to become the primary search solution trusted across the organization, it needs to harness search technology that can scale across millions of documents, and multiple data types.
The benefit of enterprise search is to provide digital workers with the most relevant information as quickly as possible. This helps improve proficiency, productivity, and employee satisfaction. It also can lead to greater profitability - as now critical customer-facing information can be found quickly.
When employees search, what they are looking for is usually buried in unstructured or semi-structured content. Think sentences - documents, PDFs, manuals, and call transcripts. The type of content that does not fit well in tables or rows.
This type of search is the hardest to solve.
Imagine searching for delicious recipes - and you have three choices. Ask three friends and they each hand you a stack of recipes. Ask one friend – who grabs several stacks of recipes. Or ask one friend - who aggregates all the recipes, ranks them according to your likes and dislikes, and hands you a relevant listing. Which would you prefer?
These are types of enterprise search, as explained in the table below.
Three types of searching
Let’s dive into how the results shown in each of these types of searches are expressed to the user.
A search engine works by first indexing the content (think the back of a text-book indicating where words or subjects are located). Then a mechanism for searching the index, then finally displaying the results.
Each repository has its own index, its own search and own display.
Each repository is indexed, but a federator sends out a query to each index – and retrieves the answers.
The developer has two choices- Present the information ranked by data source
- Rank and merge the results AFTER the information is retrieved
A more advanced choice is the index-time merge. No matter how many data sources, only one index is created and searched - and a single unified result list is displayed.
Creating a unified index in enterprise search was a difficult problem to solve, since it needed to connect to a wide variety of structured and unstructured information.
In order to search across your enterprise with unified search, you need to connect into data sources. And that’s done through connectors.
A “connector” enables you to plug-in to a content source with either a crawler or a push mechanism.
Crawler - As its name implies, a “crawler” crawls through all sources to extract data — regardless of whether that data is structured or unstructured.
- Structured data is that which is formatted in a way that makes it searchable with SQL queries. For example: Excel files, product inventory, and customer names.
- Unstructured data is that which is not formatted in a highly structured way. For example: text files, audio, video, and social media postings.
How do you implement Enterprise Search?
A secure, multi-tenant cloud-based platform with powerful AI search, recommendations, and personalization is the easiest way to implement enterprise search - at scale.
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Simple and secured indexing
Coveo integrates content from many different sources using its connectors, and consolidates the information in a single, unified index that lives in your Coveo instance (organization).
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Build Your Search Experience
Create a search user interface from scratch, use our WYSIWYG interface editor, or mix and match methods to deliver an intuitive search experience.
Any query into the search box (either standalone or from a Coveo integration) performs a call to Coveo, typically after an end-user interaction. In parallel, a second call is made to Coveo Usage Analytics, indicating an end-user performed a search. This data is then fed to Coveo Machine Learning, to understand which queries and results are useful for end users.
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Set and forget Machine learning models
Easy-to-configure, 15+ Out-of-box machine learning models require little to no maintenance. Coveo self-learning AI personalizes and delivers the results your users expect.
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Manage With Built-in Analytics
Lean and report on how your users are engaging with your search interface. Follow the data to find the gaps!
Search result ranking can be as simple as looking at how many times a given keyword appears in the text. More sophisticated ranking requires creating a relevancy score based on numerous factors, and then displaying these results in descending order.
These factors can include term proximity (that is, how close terms appear to one another in a given document), item modified date (that is, how recently a document was updated, sometimes also referred to as freshness), and term frequency (that is, how often a given term appears in a document), among others.
The way searchers interact with content also creates relationships between queries and results. Even if a term appears more often in one content piece than another, a result with fewer terms may satisfy a searcher’s intent. When users interact with that piece, the search engine can recognize this.
In the Coveo platform, a user’s search query also only retrieves term-containing documents that the user has access to, identified using security identities and permission sets. The search platform preserves an enterprise’s security by isolating only content that that given user has permission to read.
How Does AI Work in Search?
Machine Learning
To meet modern expectations, enterprise search should use artificial intelligence and machine learning to map the content so that the machine knows that a PDF about, say, “unified search,” is similar to a document on “index-time merging.” This enhances search results so the best-performing content always rises to the top.
Coveo’s machine learning models include:
- Automatic Relevance Tuning (ART)
- ART models can be configured to leverage the Coveo Intelligent Term Detection (ITD) feature.
- Query Suggestions (QS)
- Content Recommendations (CR)
- Dynamic Navigation Experience (DNE)
- Product Recommendations (PR)
- User Profile service
- User Stitching service
- Case Classification
- Smart Snippets
Role-based Access Controls
It is vital that a unified index must be able to understand the permissions a user has to access information. Modern enterprise search software uses access controls to enforce security policies on each enterprise user, to ensure security compliance within the search experience.
User Intent
By capturing signal data on every user’s action, modern enterprise search engines can determine intent. By also taking into account personal data (including geo-location) the machine can match a query to mapped content to retrieve the most relevant results.
Machine learning and deep learning algorithms have enabled a new level of relevance analytics for each enterprise search user. Each search result is uniquely tailored to individual users.
Equally, enterprise search capabilities are put to use for external-facing applications such as web search and app search. A robust enterprise search platform should support all these use cases, internally and externally to the enterprise.
Headless
With employees needing to access information from any device, a headless framework allows you to have ultimate control and flexibility of your search interface - regardless of device. Coveo works as a middle-layer for applications, opening a line of communication between the UI elements and Coveo.
Enterprise Search-as-a-Service
Coveo is an enterprise-class, multi-tenant SaaS/PaaS solution that provides a unified, scalable, and secure way to search for contextually relevant content across many enterprise systems
How Do I Determine the Best Search Engine Vendor?
Industry analysts regularly rank search engine vendors. Gartner refers to this category as an Insight Engine, while Forrester refers to it as Cognitive Search.
Unlike Elastic Enterprise Search, Solr, Amazon OpenSearch, or even Amazon Kendra, which require developers to build a search experience from scratch, Coveo enterprise search includes hosted search page templates to get started right away. You can quickly see what a typical search result will look like for a user.
