Imagine this: The year is 2005. Your superior calls you; they want a full report on a supplier working with your company. So you start searching in every file you have access to on your computer. You find quotations, lists of products, and proof of payment. You file the report. The supplier claims missing information and unpaid receipts; you call the accounting department. The file is still missing data. You start searching alternative spellings, manually search scans and images, and find – only hours later – data in a folder called Archives. It is now 5 pm, another unproductive day. It is time to go home.
Now consider this: The year is 2020. Your superior calls you; they want a full report on a supplier working with your company. You log into your intelligent enterprise search portal and type in the name of your supplier. It automatically gives you all the current information you need, with pending receipts from accounting, archived data, suggestions of alternative spellings, and relevant rich media available. It took you 5 minutes to send that report. You can now go about your productive day.
The Rise of AI-based Intelligent Search
There was a time when a manual search may have been enough. We did not drown in pools of data, and documents came structured for the most part. But, we now live in the era of big data, where we can gather massive amounts of information – often an unbearable load for one person to handle. We also implemented metadata manually – a time-consuming process – in case a company may need it in the future.
Today, our searches encompass standard documents to include social media information, rich media data, and sometimes multiple systems – lacking integration and consistency – within the same company.
This journey paved the way for Intelligent search – a search intelligent enough to understand the requirements of the user. It uses AI technology and algorithms to provide an engaged, relevant search experience. It offers a single portal to search and analyze data – including the content of images, videos, and other forms of multimedia. These searches help employees find the information they need – when they need it – by working effectively and efficiently across multiple data types. Intelligent search can now handle all kinds of structured and unstructured content – in various file types – while stressing on classification for further accessibility.
The application of AI in intelligent search has shown to be very successful in several industries such as technology, banking, marketing, and entertainment. By having access to critical information that could be the basis of corporate decisions, intelligent enterprise search has multiple benefits.
Main Capabilities of Intelligent Search Engines
Today, data is the wealth of enterprises, a wealth that should be well-invested. To do so, enterprises need the right tools to locate and collect this data, which has become a revolutionary process with the use of AI and Intelligent search.
1) Content Aggregation
With AI-based Intelligent Search engines, searches now inspect both structured and unstructured data and operate beyond different data types – including rich media. They process all data an enterprise may possess across all of its platforms, departments, and data paths, providing real-time access to information.
2) Entity Extraction
Employees may often find themselves manually entering information into a system from a document they have to archive. Intelligent based search, however, may automatically take uncategorized data and categorize it. For example, they might extract first names, last names, skills, or education from bulks of CVs to gain hiring information insight, making data readily available for the process of retrieving information.
3) Semantic Search and Analysis
With the use of advanced algorithms, Intelligent Search engines go beyond just retrieving superficial results. They use semantics and analytics to understand both meaning and context of the search and provide search results based on relevance, according to the context the user is searching in.
4) NLP Enrichment
The use of Natural Language Processing allows Intelligent Search Engines to analyze data, categorize it, and deliver interactive and engaging results. These results may be in the form of data sets or insights and dashboards, according to predictive solutions and associated requirements. These engines develop their NLP language with time and exposure to bulks of metadata through an on-going process.
5) Intelligent Image Search
With the use of “Visual Search” and programmed data recognition, this search tool allows the dissection and fragmentation of images into categorized metadata for future searches or use. In other terms, it can help users find specific objects in images and pictures, or find relevant images according to descriptions written out in the search tab.
Top 5 benefits of Intelligent Search
- Classification of Content: Uploaded metadata and documents can be automatically organized into folders, making way for easier search and enhancing the quality of data retrieval.
- Improved Efficiency: Employees spend less time searching for information; they retrieve information with less duplication of effort and time spent searching for existing data or results.
- Better Insights : Companies can find insight from their data and metadata, often in a visual form. These insights can thus become an underlying competitive advantage against competing organizations.
- Improved Decision Making: Due to faster access to critical information and analytics, decision making becomes more efficient; this includes reducing risks by detecting fraud and threats.
- Increased Customer Satisfaction: Through real-time access to information that allows a proactive response to customers, customer service can improve by transforming queries into personalized experiences.
How can Intalio Help you with your Intelligent search project?
Without an efficient way to search through bulks of information, employees waste time researching data that will probably be lacking. The more enterprises supply these systems with data, the more efficient they become, and the more they make information accessible. Companies can develop this process using techniques such as optical character recognition, document scanning, and analyzing more data types. The intelligent search engine then automatically tags documents – both old and new – with metadata and prepares it for search.
If you are still behind on updating your current search portals or behind on uploading metadata onto your new AI system, we can help you get with the times.
Say goodbye to your employees’ wasted time and effort, and say hello to the era of no data left behind.
Discover Intalio Data Governance tools, built to enhance your enterprise search capabilities.