What Is Enterprise Search?

Search across many enterprise-grade data sources goes by many names. Here’s what you should consider.

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Components of Good Enterprise Search

Good enterprise search lets employees search across many enterprise content sources - like Salesforce, blogs, Google Drive, SharePoint, Slack, etc. - to find the most relevant content. We break down the different methods, why it’s important, and what you should look for.

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    Search-as-a Service
    Flexible, multi-tenant cloud infrastructure
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    Unified Index
    Unify structured and unstructured content in a single index
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    Machine Learning
    Embrace AI and the efficiencies of automation
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    Behavioral Data
    Detect user intent via signals like clicks and time on page and understand the journey

Why Is Enterprise Search Important?

Why Is Enterprise Search Important?

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.

self-service portal that lets users request help with ease

AKA Cognitive Search, Insight Engines…

AKA Cognitive Search, Insight Engines…

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.

Why Is Enterprise Search Hard?

Why Is Enterprise Search Hard?

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.

Why entreprise search is hard

Different Types of Enterprise Search

Different Types of Enterprise Search

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.

Types of entreprise search
  • Siloed
    Individual search into each repository with results by datasource.
  • Federated
    Singular search broadcast into many databases, but results are returned by datasource
  • Unified
    Singular search into a singular index that uses machine learning to rank results into a single list
  • AI-Search
    Machine learning applied to the unified index to create a more relevant results list

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
The first choice is lousy as it requires work by the user. The second option, which is called a query time merge, requires tremendous compute power, time, and is based on a rules-based ranking mechanism that might be wrong.

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.

How Do You Connect Content to Your Search Engine?

How Do You Connect Content to Your Search Engine?

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.
Push method - A Push API exposes services that allow you to push items and their permission models into a source, and security identities into a security identity provider, rather than letting standard Coveo crawlers pull this content.
Index Sources EmptyState Illustration

How Are Search Results Ranked?

How Are Search Results Ranked?

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.
Ranking Expression ES Spot Illustration

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:


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.

Get Started Today

See how AI-powered enterprise search can take your content from confusing and cluttered to simple and streamlined.

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