Enterprise Search: What is Enterprise Search? And when do you need it?

Imagine you wake up on a workday and get asked, 'how do we purchase a business sub?' you might share a link to a how-to article or even jump on a call to share your screen.

What if you had to do it a hundred times everyday?

You wouldn't, no one would. That’s why we document information first. But what happens when we have a big team? When simple ‘search for keywords across tools’ doesn’t cut it anymore?

That’s when Enterprise Search comes into a company’s DNA. And why wouldn’t it? Gartner thinks 54% of information workers across the globe complain about interrupting their work to look for relevant information.

An Enterprise Search helps employees query the entire company’s documented knowledge to find answers. It started out in a Google-like interface when the tech was invented as an in-house tool by enterprise companies. Because of the growing demand for enterprise search solutions, there’s been quite a lot of improvement and the emergence of new dedicated solution providers.

In fact, the best ones take information retrieval up a notch using AI. Rather than serving a doc matching your keywords, AI-search engines understand questions like, “What’s our time-off policy?”, look up relevant docs, and give an exact answer.

To label these properly, Gartner created a new enterprise search category called "Insight Engines," which help businesses synthesise or even proactively by ingesting, organising, and analysing data. Forrester defines the same category as "Cognitive Search," a search function which uses AI capabilities like NLP and other machine learning capabilities to ingest, analyse, and query digital data content from multiple sources.

So, there’s a lot to uncover. Keep reading to understand more about what enterprise search is, about search analytics, how it benefits enterprises, and how to implement it.

What is enterprise search?

Enterprise search is a search tool that helps employees find information across all company sources, all types of data, to cut down on information retrieval time. Your team can either build it or you can purchase a dedicated solution.

And the productivity gains from an enterprise search solution are massive. Instead of looking up all tools and DMing teammates, employees just search in 1 place and get the exact information they need. It’s like using Google - but a private one that has results from all your apps, docs, databases, tables, spreadsheets, etc.

Enterprise search solutions do that by identifying and enabling the indexing, searching, and display of specific content to authorised users across the enterprise. But let’s understand more about why it’s important in the first place.

Why is Enterprise Search Important?

Enterprise Search is important because it saves millions of dollars in company time that would’ve been wasted in searching for information.

McKinsey says employees spend 1.8 hours every day searching for information. And the number is likely to go up with every new email, doc, or spreadsheet your company creates.

While 1.8 hours/day doesn’t seem a lot, the power of compounding shows exactly why it’s a huge problem. As per our Time Saving Calculator, a company with 500 employees making an avg. $60,000/yr could be saving a whopping $89,710/yr if they dealt with the everyday questions about information access amongst employees. But there’s a lot more pros.

Are there different types of enterprise search?

There are 4 different types of Enterprise Search:

1. Siloed Search
2. Federated Search
3. Unified Search
4. Cognitive Search

1. Siloed Search

• How it works: Performs separate searches within each data repository (like a file system, database, or intranet). The results are then presented by data source.
• Best for: Smaller organisations with limited data sources or very specific search needs within certain datasets.
• Example: A small company's HR department uses a dedicated search tool for searching resumes and another search tool for searching the employee handbook. Searching across both these sources requires individual searches.

2. Federated Search

• How it works: Broadcasts a single search query to multiple data repositories simultaneously. Results are returned and aggregated, but usually still presented by their original source.
• Best for: Organisations that need to search across multiple systems but don't require extremely sophisticated results blending.
• Example: A university library system offers a search interface that queries the library catalogue, their digital article archive, and institutional repository simultaneously. The search results page shows separate tabs for books, articles, and theses.

3. Unified Search

• How it works: Gathers data from various systems into a single, centralised index. Searches are performed on this unified index, providing a single list of results with blended relevance.
• Best for: Larger organisations with complex data landscapes where providing a seamless, cross-system search experience is essential.
• Example: A large tech company implements a search solution that indexes their codebase, internal documentation, employee directory, and customer support tickets. Employees can find what they need from all these systems with a single search.

4. AI-Powered Search (Cognitive Search)

• How it works: Leverages advanced techniques like natural language processing (NLP) and machine learning to understand the intent behind search queries, improve search relevance, and provide more insightful results.
• Best for: Organisations dealing with large amounts of unstructured data, where users want intuitive search experiences similar to popular web search engines.
• Example: An e-commerce site uses an AI-powered search solution that understands synonyms, handles misspellings, and suggests products based on user behaviour. A search for "summer dress" might return results for sundresses, maxi dresses, and other warm-weather attire.

While all of them work differently, they’re all advancements on each other. At the core, all of them even follow the same process.

How does Enterprise Search work?

Enterprise Search works in 4 phases. Here they are:

1. Data Collection & Indexing
   1. Crawling: The enterprise search engine uses web crawlers (similar to web search engines) to explore data sources and identify relevant content.
   2. Data Transformation: Extracted data from various formats is transformed into a structure that the search engine can understand.
   3. Indexing: The transformed data is organised into a searchable index, much like the index of a book, allowing for quick retrieval.
2. Query Processing
   1. User Query: A user enters a search query (keywords or a natural language question).
   2. Query Understanding: The search engine analyses the query, potentially using techniques like natural language processing (NLP) to understand the intent behind the search.
   3. Searching the Index: The search engine looks for matches between the user's query and the contents of its index.
3. Result Ranking & Presentation
   1. Relevance: Results are ranked based on how well they match the query. This can involve factors like keyword occurrence,

What are the benefits of Enterprise Search?

Enterprise Search benefits a company by its advanced search capabilities helping it save time, money, customer support tickets, employee productivity, churn, and more. Let’s dive in:

Hundreds of thousands of dollars in time savings

An excellent study from International Data Corporation indicates that a company could be 30% more productive by just improving their searching habits.

If you want to calculate time savings for your company, simply think of your total payroll and calculate 30% of it. Your company is paying all that money in salaries for redundant, repetitive searches. This is why building/buying an enterprise search solution is often one of the first steps by a growing company.

Much lesser time wasted. How? By changing your company’s knowledge loop.

There's 8 steps behind every question that gets asked on MS teams or Slack:

• Asker
  • Trying to look for answer themselves
  • Figuring out who to ask
  • Asking
  • Waiting
• Teller
  • Switching context
  • Either looking up the answer themselves or making documentation for it
  • Verifying
  • Answering

Enterprise search saves time for all 8 steps and replaces it with a new loop:

• Ask
• Get an answer. If not, submit question as unanswered
• Relevant folks get notified and do it
• No employee ever spends time on finding the a to that q ever again.

Improves your company’s information infrastructure

Your company’s information consists of structured and unstructured data. Enforcing adoption of an enterprise search, is directly dependent on how much of your data is structured v/s unstructured.

While tools do a good job of processing unstructured data, using a search solution encourages employees to store information in a more structured way as well.

Better customer experience

Enterprise search speeds up issue resolution and information retrieval, improving customer satisfaction and service. There’s even a whole category of customer-focused knowledge bases to help customers give a single source of truth to customer service representatives look up features, etc. right from one place. They’re called customer service knowledge bases.

If Enterprise Search is so good, why doesn’t every company build a search bar on their own? Because it’s very hard to build a good solution for it. Let’s find out:

Why is it difficult to build Enterprise Search Engine?

It’s hard to build Enterprise Search systems because of technical complexity. To maintain, think, build, onboard, maintain, and improve a dedicated solution is tough. And there’s even more challenges that emerge on deeper digging. Some of them are:

It’s hard to collect different data from different apps

Enterprises often have vast amounts of data in various formats (structured and unstructured) stored in multiple databases and across disparate systems like file shares, databases, cloud storage, document management systems, and CRMs. Centralising and indexing this massive data is complex and can be resource-intensive.

It’s hard to scale a tech solution

An enterprise search solution needs to scale seamlessly as your organisation and data grow. Designing a system that can adapt and perform effectively under increasing loads requires expertise and can drive up costs.

It’s hard to design

Enterprise Search often needs tailoring to meet specific organisational needs and workflows. Customizations add to development or licensing costs depending on the solution's flexibility.

It’s hard to build Security Compliance

Ensuring fine-grained security controls and respecting data access permissions for different users within an organisation is essential, but adds another layer of complexity.

It’s hard to have cutting-edge improvements

Implementing features like natural language processing, AI-powered relevance tuning, and federated name search capabilities (searching across multiple systems) requires specialised skills and can drive up costs.

It’s hard to maintain post deploying

Choosing the right deployment model (cloud-based vs. on-premises) and the ongoing maintenance, updates, and support further impact the overall cost and effort of implementation.

What use cases work with enterprise search?

Enterprise search has remarkably diverse applications across industries. Key use cases include:

Digital Workplace

• Example: Employees can quickly locate the latest benefits information, project templates, brand guidelines, or even past meeting notes without navigating complex folder structures or sending frustrating "Does anyone know..." emails.

Customer Service

• Example: Support agents find troubleshooting guides for a specific product issue, pull up customer history within seconds during a call, and even access knowledge base articles to improve their overall expertise and reduce resolution times.

Knowledge Management

• Example: New hires easily find onboarding checklists, access department-specific procedures, and locate up-to-date best practice documents relevant to their role. This accelerates their onboarding process and empowers them with the knowledge they need to be successful.

Expertise Location

• Example: A project manager needs a developer with Python experience and familiarity with machine learning concepts. Search results reveal several employees across different teams with the right skills, even uncovering potential collaborators they weren't previously aware of.

Talent Search

• Example: HR recruiters search resumes, internal profiles, and even past performance reviews to identify potential candidates with specific experience or skillsets for an open position. This enables the organization to leverage existing talent and reduces the time and cost associated with external hiring.

Intranet

• Example: The company intranet becomes a fully searchable hub for policies, announcements, forms, contact directories, and even the cafeteria menu. This reduces the volume of inquiries to HR and other support departments, freeing up valuable time.

Insight Engine

• Example: Analysing sales data across regions and demographics reveals patterns in customer preferences, allowing for targeted marketing campaigns and data-driven product development. Business intelligence reports can be generated in minutes, rather than relying on manual data collection and analysis.

Legal Sector

• Example: Lawyers quickly search case law precedents, contracts, regulatory documents, and even internal expert depositions related to their specific case. This streamlines legal research and helps build stronger arguments.

Finance & Banking

• Example: Financial analysts pull up-to-the-minute market reports, customer profiles, risk assessment data, and news feeds for comprehensive, informed decision-making.

Regulatory Compliance

• Example: Companies automate checks of new customers against sanctions lists, monitor internal communications for potential red flags, and streamline the reporting of suspicious activity. This helps ensure compliance, reduces risk, and protects the organisation's reputation.

Field Operations Support

• Example: Technicians on the road access repair manuals, safety guidelines, real-time updates on parts availability, and even customer-specific notes directly from their mobile devices. This minimises downtime and ensures quick and accurate problem resolution for customers.

E-commerce

• Example: Customers effortlessly find the product they need on online stores using intuitive search that understands natural language and offers helpful suggestions. Search can even be personalised based on browsing history, resulting in increased sales conversions and happy customers.

How can you implement Enterprise Search?

Here's a simplified 8-step overview of implementing an enterprise search engine:

1. Assessment: Define your organisation's specific search needs, the types of data you want to index, and your desired user experience.
2. Vendor Selection:
   1. Build vs. Buy: Decide whether to build a custom solution in-house (more control, but resource-intensive) or use an existing enterprise search platform.
   2. Research vendors: Compare features, pricing models, scalability, and suitability for your use case based on your needs assessment.
3. Deployment:
   1. Cloud: Configure and deploy your chosen solution in a cloud environment.
   2. On-premises: Install the search software on your own servers and configure it
4. Data Integration: Connect the system to your various data sources (file shares, databases, cloud storage, etc.).
5. Indexing and Tuning: Crawl your data to build the search index and optimise relevance ranking.
6. User Interface: Design a user-friendly search interface, potentially customised to match company branding or workflows.
7. Testing: Conduct user testing to evaluate search effectiveness and gather feedback.
8. Training and Rollout: Train your users on how to leverage the new search solution optimally.

Important Considerations:

• Complexity: Implementing enterprise search can be a complex endeavour, especially for large organisations with intricate data landscapes. You may need IT expertise or consider partnering with an implementation specialist.
• Change Management: Successful implementation involves preparing your organisation for the new search system and addressing any potential resistance.

What are the pricing factors for an Enterprise Search Solution?

There are 6 key features and pricing factors to consider while considering building/buying an Enterprise Search solution:

1. Scale: The number of users, the volume of data to be indexed, and the complexity of your data landscape all impact the cost.
2. Deployment Model:
   1. Cloud-based (SaaS): Subscription-based pricing, often charged per user or per volume of data indexed. Can be more affordable for smaller setups or pilot projects.
   2. On-premises: Higher upfront investment in hardware and software licences. Maintenance costs can add up over time.
3. Customization: Tailoring the search experience to your specific needs can increase development costs or licensing fees.
4. Advanced Features: AI-powered capabilities like cognitive search often come at a premium.
5. Data sources and volume: Estimate the overall size of the content you want to make searchable.
6. Number of users

What are the main criteria for choosing enterprise search software?

Data Connectors

It's critical to figure out how many data connectors an enterprise search engine needs to index data from different data sources. It's good practice to include future sources along with those you plan to index. But if a source will be removed soon, skip it. This is especially true if the relevant data will move to a new source.

Privacy and Security

Security and privacy of data are very important when searching for information in a company. The search tool must follow the corporate security policies and rules of the company, SOC2, and laws like GDPR. We must take steps to make sure that data is safe and kept private, which protects important business assets.

• Follows rules for compliance that are set by the government and different industries
• Encryption built into the indexing process protects content from people who want to do harm
• Customization of IP restrictions and encryption processes
• Connecting to providers of single sign-on (SSO)
• Each user can control how they get to information, and security filters are used for indexed content
• Multilayer security is used in cloud environments, on-premises data centers, intranets, and operations

Intelligent Search or Predictive AI

Predictive AI is widely regarded as the future of enterprise search engines. By using machine learning algorithms incorporating self-learning algorithms and artificial intelligence into these tools, it becomes possible to innovate by continuously learning from users and enhancing search results based on their usage patterns. Furthermore, leveraging custom APIs designed to optimize search tools for specific audiences allows for the delivery of fine-tuned results that improve over time.

Conclusion: All things considered, do you need Enterprise Search Software?

Yes, you need to have an an enterprise search system. You might think building it is tough but It can be done, especially if you have a big organisation with tons of data to wrangle. Sure, the upfront cost could be hefty – think around 30% of your payroll costs. But the potential productivity boost down the line could seriously make it worth your while.

Here's the thing, though: building and taking care of a custom search system is no small task. Going with an outside solution lets you tap into all the bells and whistles and get expert support without having to manage the whole thing yourself. Think of it like hiring a search assistant who's always there to find exactly what you need, fast.

So, should you do it? That's the big question! An enterprise search solution can seriously level up how your teams work together and help you find info when you need it. If that sounds like something you could use, it's definitely worth putting some thought into!

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