Insight engine’s are cognitively enabled platforms. They interact with users in a more natural fashion, learn and progress as they gain more experience with data and user behavior, and proactively establish links between related data from various internal and external sources.
“Enterprise search has dominated the way people find information, but significant changes in what is available and what is desired are gathering pace. Digital content leaders must embrace a new technology that is redefining the market around search, one that Gartner terms the ‘insight engine.’” (Source)
A cognitive insight engine provides a combination of indexing, natural-language processing, and machine-learning technologies to review an increasingly relevant corpus of knowledge from all sources of unstructured and structured data. These engines use naturalistic or visual query interfaces to deliver knowledge to people via text and cognitive visualizations.
How do searches via insight engines deliver relevant knowledge?
Insight engines extract valuable information from large volumes of complex, diverse data sources. It is important to tap into all available sources, whether they are internal or external, structured or unstructured, to provide deeper insights to users so they can make better decisions. Cognitive search provides this connection and provides comprehensive insights.
It provides contextually relevant information. Finding relevant knowledge across all available data requires cognitive systems that use natural-language processing (NLP) to understand what unstructured data from texts (e.g., documents, social media blogs, engineering reports, market research) and rich-media content (e.g., videos, scans) is about. Machine-learning algorithms help refine the insights gained from data. Trade and company dictionaries and ontologies help discover synonyms and relationships between different terms and concepts. That means there’s a lot of intelligence and horsepower under the hood of a system providing relevant knowledge or insight.
It leverages machine-learning capabilities to continuously improve the relevancy of search results. Machine-learning algorithms (e.g., collaborative filtering and recommendations, clusterization, similarity calculations for unstructured contents, and predictive analyses) provide added value by continuously refining and enhancing search results to provide the best relevancy to users.
Cognitive searches in insight engines give data-driven organizations a new generation of search functionality, enabling them to go far beyond the traditional search box and allowing users to get immediate, relevant knowledge at the right time on the right device.
Gartner Reviews for Insight Engine platforms: Gartner Vendor Reviews