Employees spend nearly a third of their time searching for information they need to do their jobs. To help teams work more efficiently, organizations are implementing search and knowledge discovery technologies.
Two leading options are insight engines and enterprise search systems. But what‘s the difference, and which is better for your business?
In this 2600+ word guide, we’ll compare insight engines and enterprise search to help you decide which better fits your needs.
The Growing Need for Intelligent Search
Let‘s first understand why businesses are adopting new search technologies:
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Time savings: Employees lose over 30% of work time searching for information manually. Intelligent search gets them answers faster.
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Competitive edge: Faster access to insights from data, documents and collaboration drives better, data-driven decisions.
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Overloaded workers: Information overload leads to multitasking and distraction. Intelligent search cuts through the noise.
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Data proliferation: Unstructured data like documents and emails are growing at a rate of 60% to 80% a year. Search needs to keep pace.
Many organizations have enterprise search systems to index documents, emails, wikis and websites for query. But legacy search tools have limitations:
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They struggle with new unstructured data types like messaging apps, video and IoT sensor data.
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They don’t understand context or user intent, leading to irrelevant results.
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They require rigid taxonomy and metadata management by IT.
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They don‘t provide analysis or recommendations to drive decisions.
Modern insight engines overcome these hurdles by combining search, AI and analytics. Let‘s examine insight engines and today‘s enterprise search solutions in more detail.
What Are Insight Engines?
Insight engines, also called cognitive search or enterprise knowledge graphs, are AI-powered search systems that synthesize big data into contextual answers and recommendations.
Insight engines integrate:
- Enterprise search to index varied data sources
- Natural language processing (NLP) to interpret text data
- Knowledge graphs to map concepts and relationships
- Analytics to uncover patterns and insights
- Machine learning to improve relevancy and recommendations over time
Unlike basic search tools, insight engines synthesize disparate information to provide relevant, actionable intelligence to guide decisions.
Insight engines analyze big data to provide contextual answers and recommendations (Image source: Examples.com)
Let‘s look at some examples of how insight engines work:
Use Case 1: Competitive Intelligence
Say your team needs the latest market share data on a competitor. An insight engine would:
- Crawl news, financial reports, case studies and other sources for mentions of the competitor
- Extract key statistics, relationships and events using NLP
- Synthesize details into a shareable intelligence brief on the competitor‘s market position over time
This saves analysts from manually aggregating data from multiple systems.
Use Case 2: Supply Chain Risk Assessment
An insight engine can also mitigate supply chain risks. For example:
- The system ingests shipping manifests, customs data, weather forecasts and news of transport strikes
- It applies graph analysis to model potential delays and shortcuts across distribution networks
- The engine highlights shipments likely to miss Service Level Agreements (SLAs)
- It suggests optimal rerouting based on constraints like cost, emissions and reliability
This allows logistics teams to spot delivery bottlenecks early and take corrective actions.
Insight engines bring tangible productivity and performance improvements across many industries and functions:
Industry Applications
- Financial research
- Life sciences R&D
- Operational intelligence
- Supply chain optimization
Business Capabilities Enhanced
- Competitive intelligence
- Regulatory and compliance review
- Customer service query handling
- Sales lead qualification
- Predictive analytics
Compared to basic search, insight engines allow wider access to intelligence that drives strategic planning and day-to-day decisions.
What is Enterprise Search?
Enterprise search systems index an organization‘s documents, communications, intranet sites and other unstructured data in one place. This gives employees a single search box to query instead of digging through emails, file shares, wikis, CRMs and other silos.
Enterprise search consolidates siloed data for unified querying (Image source: Examples.com)
Enterprise search engines typically provide:
- Connectors to ingest common corporate data sources like file shares and databases
- Crawling to index unstructured data content like documents and emails
- Search and relevance ranking algorithms
- Customizable dashboards to analyze usage and optimize taxonomy
- Permissions and access controls for security
- APIs to surface results in other applications
While today‘s leading enterprise search platforms have strong core functionality, they lack the intelligent analytics of insight engines.
Let‘s walk through two examples of how enterprise search improves productivity.
Use Case 1: IT Ticket Resolution
An enterprise search index saves IT agents from combing through ticketing systems and system logs to diagnose a "service degraded" alert. The agent simply queries the search engine, which serves up related tickets plus server metrics and events. This fast access to contextual information speeds issue resolution.
Use Case 2: Regulatory Review
For audits and risk analysis, legal and compliance teams must sift through policies, communications, contracts and filing documents. Rather than search multiple repositories, enterprise search offers one place to query leagues of content. Review is faster with all context in one search result.
With a unified index, enterprise search gives all employees "Google-like" access to organizational knowledge. Users get answers in less time without having to know exact document locations.
Now that we‘ve defined these technologies, let‘s compare insight engines and enterprise search head-to-head.
Insight Engines vs. Enterprise Search: Key Differences
While insight engines and enterprise search both improve findability of information, they take distinct approaches:
| Insight Engine | Enterprise Search | |
|---|---|---|
| Purpose | Deliver contextual insights for decisions | Find relevant matching documents |
| Data Sources | Structured + unstructured data | Mainly unstructured documents |
| Processing | Advanced NLP analyzes text ML detects patterns |
Limited NLP for query typing Matching and ranking |
| Knowledge Model | Rich knowledge graphs map concepts Continuously learns user needs |
Basic taxonomy classification |
| Capabilities | Analyze data relationships Provide recommendations |
Retrieve matching results |
| Performance Gains | Strategic planning and predictions Process automation |
Faster access to known items |
Let‘s analyze the key differences in more detail:
Purpose: Insights vs. Matching
Insight engines aim to synthesize connections across data to reveal trends and recommendations. their purpose is to enhance decisions and planning.
Enterprise search instead helps users simply find items that match search criteria. The purpose is to improve discoverability of existing information.
Data Sources and Processing
Insight engines integrate a wider variety of structured and unstructured data, understanding text through advances NLP. This breadth of data powers more impactful analytics.
Enterprise search predominantly indexes unstructured documents through metadata and basic linguistics. Processing focuses on serving up matching content.
Knowledge Model: Graphs vs. Taxonomy
Insight engines continuously map concepts into rich knowledge graphs that capture relationships in data. With machine learning, these graphs adapt to users‘ contexts and interests to serve hyper-relevant insights.
Enterprise search relies more on predefined taxonomies classifying content. Query typing and ranking algorithms help improve result relevancy.
Capabilities: Analysis vs. Retrieval
By connecting dots across data, insight engines reveal trends and recommend next actions. Analytics empower planning and predictions.
Enterprise search instead focuses on searching and retrieving relevant documents that already exist across silos. Users still interpret connections.
The above differences lead to varied performance improvements from each technology:
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Insight engines enhance strategic decisions and planning through data synthesis and prediction. They also enable process automation by triggering downstream workflows based on analytic outputs.
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Enterprise search provides faster access to known items in systems across the organizations. Employees spend less time hunting for information needed in the moment.
In summary:
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Insight engines analyze big data to drive decisions and new discoveries
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Enterprise search finds known items fast for productivity gains
Now that we‘ve compared their technical capabilities, let‘s discuss optimal use cases and adoption trends.
When Should You Consider an Insight Engine vs. Enterprise Search?
With insight engines entering the market, some organizations opt to replace or augment existing enterprise search systems. Others choose insight engines for net-new use cases.
Trends Toward Insight Engines
By 2025, over one third of enterprises will implement insight engines in some capacity for enhanced intelligence. Drivers include:
- Untapped value in dark data like audio, video and sensor streams
- Need for predictive insights from analytics
- Desire for no-code self-service access to answers
As insight engine adoption grows, deployment options expand:
- Complement enterprise search where it falls short on accessing pooled wisdom
- Replace enterprise search with insight engines‘ richer connectivity to people, systems and context
- New analytics use cases unavailable before through search alone
Let‘s examine some specific applications where insight engines have clear advantages.
Where Insight Engines Excel
Consider insight engines when your goals require:
Deeper analysis – Competitive intelligence, fraud detection, risk management and other analytical use cases demand advanced synthesis across more data signals.
Interpreting "dark data" – Derive structure and meaning from untapped photo, video, speech and textual data with NLP and vision AI.
Enhanced automation and control – Self-service analytics empower more employees to generate insights that trigger workflows.
Smarter techniques like machine learning, semantic search and knowledge graphs open new possibilities for enhancing decisions through previously inaccessible data.
Insight engines truly differentiate business competitiveness here.
Where to Stick with Enterprise Search
In some scenarios, enterprise search still provides the best user experience:
Broad content access – Allow all employees to easily search enterprise documents and communications.
Strong permissions model – Maintain control over who sees regulated, confidential or deleted content.
Query large indices – For the highest search throughput with predictable cost.
Handle multimedia assets – Catalog rich media like video plus related metadata all in one place.
So consider keeping your enterprise search platform for wide information access along with an insight engine for advanced analytics.
The following chart summarizes suitable uses cases:
| Better Fit: Insight Engine | Better Fit: Enterprise Search | |
|---|---|---|
| Key Users | Data scientists, analysts | General knowledge workers |
| Data Sources | Diverse and siloed pools, "dark data" | Central repositories like SharePoint |
| Decision Types | Strategic planning, predictive insights | Everyday search for items and documents |
| Technology Needs | Advanced NLP and ML | Multimedia handling, access controls |
| Performance Gains | Enhanced forecasting and automation | Employee productivity |
Think first about your targeted users, their decision needs, and desired business performance lift to pick either insight engine or enterprise search. Or you may justify both simultaneously.
Recommended Insight Engine Platforms
Many enterprise software vendors now offer packaged insight engines with search, analytics, and AI capabilities:
Microsoft Azure Cognitive Search – Insight features layered on top of market-leading cloud search. Strong integration with other Azure data and analytics services.
Google Cloud Search – Leverages Google‘s AI leadership for rich semantic search. Integrates neatly with GCP‘s big data platform.
AWS Kendra – Machine learning powered insights for text-heavy data lakes hosted on S3.
IBM Watson Discovery – Longstanding NLP strengths enhanced with search and graph analytics.
Expert.ai Insight Engine – Specializes in extracting symbolic information from unstructured text data with hybrid AI.
Lucidworks Insight Engine – Unified data platform purpose built for insight engines, underpinning many branded applications.
Mindbreeze InSpire – Gleans actionable intelligence from across enterprise data sources.
And explore 25+ additional providers compared here. Look for robust cataloging of data sources, NLP and ML proficiency, smart knowledge models, and analytical modules when evaluating solutions.
Key Takeaways Comparing Insight Engines and Enterprise Search
We‘ve covered quite a bit contrasting insight engines and enterprise search systems. Let‘s recap the key comparisons:
- Insight engines synthesize big data with AI/ML to uncover new strategic insights and recommendations.
- Enterprise search efficiently finds known unstructured documents spread across silos.
- While their technical capabilities differ, insight engines and enterprise search can play complementary roles in an intelligent information architecture.
- Choose insight engines when in need of advanced automation or predictive prescribing based on cross-data analysis.
- Stick with enterprise search for widespread multimedia cataloging with permissions controls.
- Leading enterprise software vendors offer insight engine solutions ready to deploy.
Getting the right information architecture aligned to user needs accelerates both productivity and breakthrough innovation. Evaluate both insight engines and enterprise search to craft your optimal strategy.
Now that you understand insight engines‘ business goals and technical capabilities, see how they map to common organizational objectives using this interactive tool.
Or arrange a custom briefing from our experts to discuss if insight engines or enterprise knowledge graphs should be part of your digital transformation plan.