cognitive search

What is cognitive search?

Cognitive search represents a new generation of enterprise search that uses artificial intelligence (AI) technologies to improve users' search queries and extract relevant information from multiple diverse data sets. Cognitive search capabilities extend beyond those of a classic search engine to bring numerous data sources together while also providing automated tagging and personalization. It has the potential to greatly improve how an organization's employees discover and access information that's relevant and necessary to their work context.

Cognitive search differs from previously available search products because it combines indexing technology with powerful AI technologies -- such as natural language processing (NLP) capabilities and algorithms -- to scale a variety of data sources and types. Additionally, developers can build search applications that can be embedded into business process applications, such as pharmaceutical research tools and customer portals.

The primary benefits that organizations can reap from cognitive search include its impact on knowledge discovery -- a user's ability to extract useful information from data. For example, cognitive search improves the relevance of extracted information and increases the efficiency of query responses, allowing employees to boost their productivity and provide better service.

Importance and benefits of cognitive search

Keyword-based search and traditional enterprise search have become inadequate due to the increasing variety and amount of data used within organizations. The two methodologies impair search processes and employee productivity by returning irrelevant or incomplete results that users must sort through to find the information they need.

With cognitive search, the AI technologies that are introduced enable enterprise search to extract advanced meaning from content as well as learn from users' searches to provide increasingly relevant and complete results. Some overall benefits of cognitive search include:

• Maximized productivity. A single search functionality removes the necessity of switching between apps and eliminates time wasted on tasks like re-entering credentials multiple times. Furthermore, the unification of data tools allows organizations to streamline their business processes.
• Improved employee experience and engagement. Employee loyalty is promoted through the elimination of wasted time and the increase in productivity. Machine learning (ML) algorithms that provide personalized suggestions help users find relevant data more quickly and the flexibility of cognitive search creates an improved user experience through personalization. Since an employee's search experience is improved, they're more likely to use the tools consistently.
• Lower operational costs. Maximized productivity decreases an organization's operational costs since less time and resources are needed for gathering information and knowledge discovery. This is especially beneficial to industries such as healthcare and legal services that work with massive amounts of data.

As companies grow and acquire new customers, the need to run and analyze large amounts of data increases as well. If a company is bringing in thousands of new customers every day, then their data growth is exponential, making it almost impossible to keep up with the new information. Cognitive search makes it feasible to decipher a consistently growing collection of data for use within different departments of the company.

How does cognitive search work?

The design elements used in enterprise search form the foundation of cognitive search. This means that organizations do not need to entirely rebuild their information technology (IT) department when implementing cognitive search. AI technologies then build on top of this foundation to find relevant information across all available enterprise data sources.

NLP is used to understand what unstructured data from emails, documents, market research, videos and recordings mean. ML algorithms continuously improve the relevancy of results. Some of the most common ML algorithms found in cognitive search include:

• Clustering. This is an unsupervised learning algorithm that groups subsets of data based on similarities. Clustering is employed when users do not want to run a search through the entire search index. Its goal is to limit searches to specific groups of documents in each cluster.
• Classification. This is a supervised learning algorithm that creates a model to predict labels for new data using a training set comprised of pre-labeled data.
• Regression. This is another supervised learning algorithm that uses the relationship between input and output variables to predict continuous numeric values from data.
• Recommendation. This often combines various basic algorithms to generate a recommendation engine that offers potentially helpful content to users. Also called content-based recommendation, it offers personalized recommendations based on the relationship between a user's interest and the description and attributes of a document.

In addition to these ML algorithms, a heavy computing process, referred to as similarity, builds a matrix that synthesizes the interactions between data samples.

Cognitive search tools

Cognitive search is still in its infancy, but multiple companies have jumped on the opportunity to create and market cognitive search tools. They include the following:

• IBM with Watson Explorer.
• Coveo.
• Attivio with their Cognitive Search and Insight Platform.
• Lucidworks.
• Mindbreeze.
• Sinequa with their Insight Platform.
• Microsoft Azure Cognitive Search.
• Algolia.

Examples of cognitive search

Legal practices with international exposure are finding cognitive search useful by implementing legal industry-specific add-ons which help find experts in specific areas of law. These experts can then be organized into specialized teams across a firm's international offices.

Cognitive search is also finding beneficial applications within customer service. Representatives can access multiple applications and widespread digital content simultaneously, including everything from shipping information to product details. This allows them to respond to customer requests and resolve problems more efficiently.

Within IT operations, cognitive systems can consistently monitor log files that indicate faulty builds or misuse of the network. Telemetry data can also be scanned to find irregular activity that could warn of a potential outage.