The Different Capabilities of Intelligent Search Engines

Introduction

How often do your end-users search through multiple data systems to find relevant information that they need for their operational tasks? How many search queries do they perform throughout every day? The likelihood is that they are not working efficiently at all. There is a flourishing trend in information and business intelligence called ‘intelligent searching’, and it is unfolding traditional information and knowledge warehouse discovery.

Intelligent search has its origins within machine learning, natural language processing, and artificial cognitive engagement, or most commonly known as artificial intelligence. Combined and integrated, these technologies assist end-users in finding the information they need and when they need it. As a result, it can improve your organization and help members find information quicker and with less effort.

The Business Case for Intelligent Search

Intelligent searching should be a mandated practice for organizations seeking data-driven acumen. Intelligent search has diverse benefits, including the following:

• Improve the data quality of information retrieved via digital searches.
• Improve customer service through updated and accurate information.
• Enhance and strengthen a corporate knowledge base by reviewing and aggregating all unstructured data sources.
• Reducing, and eventually discarding, old data silos that exist between different departments and divisions.

Main Capabilities of Intelligent Search Engines

Data is considered an asset in most enterprises, and it must be protected and secured. However, it could get difficult to retrieve the necessary data or information, especially when multiple departments need to screen it simultaneously. Companies need the right means and mechanisms to discover and assemble this data, which has become an innovative process via AI and intelligent searching.

• Content Gathering: Through artificial intelligence and intelligent search engines, end-user searches now scrutinize unstructured and structured data and perform across different data types, including content-rich media. A company may control their data and access to it across all platforms and data pathways, providing fast and real-time information.
• Item Extraction: End-users often manually enter information into a data system from a record they have had to archive. An intelligent search automatically sources uncategorized information and categorizes it. This makes it easier to organize and locate relevant data.
• Connotative Search and Analysis: Using superior algorithms, intelligent search engines perform more than simply retrieving query results. They also utilize semantics and analytics to learn both the meaning and context of the end-user search and provide search results based on connection and significance—all according to the context with which the end-user performed their initial search.
• Natural Language Processing and Enrichment: Natural language processing provides intelligent search engines with the means to analyze and categorize data, and produce appealing results. According to predictive clarifications and associated requirements, these search results may be in the form of data sets or insights and end-user fashioned dashboards. These search engines improve their NLP language processing ability over time and exposure to bulk metadata through a continuous, systematic process.
• Intelligent Image Searching: Through a ‘visual search’ that utilizes a programmable data recognition solution, an intelligent search tool allows the examination and fragmentation of data images into categorized metadata for future end-user searches. This process can help end-users obtain specific objects in images and photos or find relevant images according to search descriptions within the search field.

Key Five Benefits of Intelligent Searching and Intelligent Search Engines

1. Content Classification: Uploaded data, documents, and information can be systematically organized into portfolios. This ensures ease of use and improves data retrieval quality.
2. Advanced Efficiency: End-users will waste less time searching for the information they need. They can retrieve data quickly without exerting much effort.
3. More Valid Insights: Organizations can obtain deep insight from their data and data repositories—and often visually as well. This understanding will become a competitive advantage against key marketplace competitors.
4. Educated Decision Making: Due to more expeditious access to crucial information and data analytics, decision-making will become highly efficient and strategic. This positive outcome will enhance business success and reduce the risks posed by principal competitors.
5. Boosts Customer Satisfaction: By utilizing real-time access to crucial information that allows a proactive response to customers, customer service will be significantly improved. Gathered data can also be used to further personalize the customer experience.

Conclusion

Without a methodical and organized practice to search volumes of data and information, end-users will waste precious working time investigating and searching for data that will more likely be lacking, or unnecessary for the task at hand. Intelligent searching is the foundational framework that infiltrates all data platforms and sources, services, and products—offering valuable information connections and profound insights.