What goes into an effective enterprise search engine for businesses?
When it comes to the enterprise, a poor search experience is more than just bad UX and frustration. It becomes a costly knowledge drain that can stunt business growth. How does enterprise search go beyond public web search, and what goes into making it effective?
Ineffective search engines present a host of problems that often don’t seem imminent. It could be a customer’s frustration in trying infinite iterations of keywords; or the endless scrolling by a casual researcher to surface the perfect resource. However, these challenges are far exacerbated for the enterprise.
Enterprise search is not like ordinary web search. Such consumer search is for everyday information, such as how to buy groceries or where to find a babysitter. Enterprise search, on the other hand, is for mission-critical information. For example, searching in a nuclear power plant or in a corporation’s financial department.
The challenges of enterprise search are far more costly
In this era of explosive information growth, few problems loom larger for enterprises than finding information. Poor enterprise search poses a menacing threat to an organisations’ competitiveness in the long run. Inefficient or insufficient search functionalities force employees to spend more time searching than getting work done, becoming a costly hindrance.
On top of that, systemic knowledge drain is a big problem. When employees leave, working knowledge leaves with them, versus having it retained, organised and discoverable in enterprise search engines. When information is not found often, companies end up wasting resources paying for external subscriptions to supplement the gap. Effective enterprise search engines help to plug the gap in knowledge drain. They are essential for employees to spend more time analysing than searching, make better decisions and solve problems at a faster pace.
Why can't an enterprise just go ‘Google search’ it?
Google search and enterprise search tools are in fact broadly comparable in their basic functionality. They share three components that enable them to function: ingesting, indexing, and searching. Let’s briefly break down these architectural similarities before delving deeper into their differences.
Three shared components of public web and enterprise search engines
- Data ingesting: Otherwise known as crawling, this process scans the relevant data sources to discover content. Crawlers recurrently download information and follow the links found on each source page. Whatever data that has been consumed will be used to generate the search answers in later steps.
- Data indexing: Consumed data is organised into a database that enables the data to be searchable later. Indexing software like bots and spiders parse the data by collecting key information such as links and keywords. These are stored as an index so that the process of retrieving information in response to search queries is faster and easier.
- Search query: A search interface allows users to input search queries that will call upon the index to return the relevant search answer to the user.
However, within each component, these two engines serve vastly different purposes. The key difference hinges on one fundamental factor: data.
Google search finds information from public sources and Google’s own repositories (eg. YouTube, Gmail, Workplaces). Such sources of information are typically indexed by the creators and unmanaged by Google.
However, information found in enterprises tends to be fragmented and diverse. They sit in different places. They could be hosted on cloud or on premise. Or across a broad range of enterprise applications—like content management systems (CMS), enterprise resource planning solutions (ERP), customer relationship management suites (CRM). Or in data lakes, email clients, archives, intranet. The list goes on.
Enterprise search tools also need to retrieve information of all sorts—data that’s structured and unstructured. Users need to be able to index, organise and manage on demand and allow customisable access restrictions.
With such complexity of data, accessing insights becomes complex in turn. Users can’t simply rely on direct keyword search which are common in public web search. One, the guessing game is far more costly as the stakes with mission-critical tasks are much greater. And two, users in the enterprise might encounter ‘unknown unknowns’, which is when they are unable to even produce a search query to the answer they seek. They would need more predictive knowledge discovery tools and a robust index to surface search answers even with vague queries.
Enterprise search engines administrators need more control
In these aspects, enterprise search users and administrators require search engines to offer them more control. Control over information submission and readability, content and user access, security settings and content discovery management. There are several key components that are crucial to an effective enterprise search engine.
5 considerations of an effective enterprise search engine
1. Governed ingestion of a wider range of repositories
Enterprise search engines need to have a governed method in receiving new information. Most systems use a push and pull method to index content from different repositories.
In this case, push refers to new content pushed through an API into the engine directly. Pull refers to the search engine picking content from selected sources through a connector. This method would apply for a wide range of repositories depending on the organisation’s needs.
Configuring these integrations needs to be easy. Considering the variability of directories and databases an enterprise might have, a DIY tool to create, edit and monitor connectors can make a vast difference in synergising information management on demand.
2. Compatibility with broader data formats through pre-processing and effective document readability
Enterprise-type sources include a variety of documents, relational databases, images, videos, and other forms of multimedia. These tend to be much trickier to ingest, as compared to deploying a web crawler to ingest web resources, which are typically file based like in HTML and TXT.
Challenges include dealing with unstructured information such as in documents; effective enterprise search engines will need to pre-process them using document filters and other techniques, to ‘clean’ and improve their readability. Legacy data sources are also commonly encountered.
3. More accurate, nuanced and automated indexing
Making such a variable range of data formats discoverable through accurate indexing is the main challenge here. Getting this job done, quickly and precisely, is a key requirement in an effective enterprise search engine.
Today, there are modern methods such as the use of content tagging to automatically tag metadata to make this process efficient and less time consuming. An established ontology—i.e. a knowledge graph of entities and their relations within a domain—can help to organise content with far greater detail and accuracy, without manual processing. In TAIGER’s enterprise search tool, Omnitive Search, an Automatic Entity Recognition function does the job.
4. Humanistic perception of more complex search queries
Search functionalities make or break the effectiveness of an enterprise search engine. Typical web search interfaces function well with direct keyword search, considering the simplicity of content types and search rationale. But for mission-critical enterprise search engines, intelligent search features and advanced filters are a necessity to make search easier.
AI driven search features come in handy to both enhance the specificity of search query, and make retrieved information easier to digest. Advanced filters to trigger results from specific topics, database source, document format and date can help users narrow down information more quickly. If the exact search query is unknown to the user, features like autocomplete keyword suggestions or predictive search capabilities are valuable tools to encourage knowledge discovery.
The enterprise search engine need to consider a data searching technique called semantic search. It should employ natural language technologies to allow users to type their queries in natural language for a more intuitive search experience. This also means to take note of the semantics used in the domain the engine is dealing with, such as by understanding the relevant abbreviations, legalese and shorthand language used.
After retrieving search answers, how they are surfaced to the user to facilitate knowledge access must be considered. Enterprise search engines can call out the search answer in a featured snippet, so that users can see the answer without a click. Some advanced engines also accommodate for knowledge discovery by displaying related concepts, or even a knowledge graph of how their information is linked to other related instances and classes for a 360 degree view on a topic.
5. Greater degree of information security
Information security takes far more weight within tightly regulated enterprise knowledge systems than in public web search. The bottom line is that enterprise search engines need to be able to authenticate user access to specific domains and intranet content.
However, administrator access can further enhance this search engine performance. Administrators and heads of departments can be assigned the ability to control both the conditions and access of documents. They can be provided with easy to use tools to customise or configure search features, so they stay on top of changing enterprise needs without repeatedly raising support tickets.
Enterprise search—a piece of the bigger pie
Enterprise search today is the backbone to any enterprise’s information management framework. With all its advanced features, it serves as a strategic piece in the overall business intelligence power of an organisation. It is used within a context usually where it integrates with business intelligence and pulls data from various sources such as CRM, ERP, HR management suites and more.
With enterprise search engines geared up and connected, employees can get answers to complex questions, raise new questions and spend less time looking for information. This means more time on higher value work, that drives growth for the business.