How Cognitive Search Can Help Your Enterprise Become Information-Driven

Without intelligent search tools, organizations can end up drowning in data but thirsting for insights. According to a McKinsey report, enterprise employees spend 19 percent of their work week searching for and gathering up the information and data they need to do their jobs. To make crucial decisions, information-driven enterprises need access to the right data at the right time. The most efficient and effective way to achieve that is through cognitive search.

Today, enterprises around the world are gathering data faster than ever before. But having more data available isn’t a solution in itself. Data only adds value to an organization if it can be used with efficiency.

Effective search capabilities raise the intelligence of an organization at every level from streamlining operations to fueling digital transformation efforts to driving greater growth. Making informed business decisions and developing successful strategies depends on intelligence search.

The Evolution of Enterprise Search

Big data is becoming bigger and more interconnected every day. From 2020 to 2022, the total volume of enterprise data worldwide is expected to grow by an annual 42.2 percent. In that time, total data volume is projected to rise from approximately one petabyte (PB) to 2.02 petabytes.

Keyword-based search engines won’t be able to deliver the insights that enterprises need. These older search platforms serve up results based on the use and frequency of keywords. Users are unlikely to find the results they want unless they enter the right keywords or key phrases.

Even if users do enter the right keywords, it’s still inefficient. That’s because they must spend time sifting through piles of unstructured information. It’s an inefficient and time-wasting way to find the answers they need.

The average enterprise manages more than 10 billion files a year, and that volume grows daily. Finding meaning in that data is increasingly difficult. To address that challenge, enterprises need a more efficient and effective search platform.

The need to provide relevant results consistently in response to queries is changing the entire search landscape. Intelligent search platforms now utilize natural language processing and machine learning capabilities. Enterprise search is now smarter than ever before with the advent of cognitive search.

What Is Cognitive Search?

Forrester defines cognitive search as “a new generation of enterprise search solutions that employ artificial intelligence (AI) technologies such as natural language processing and machine learning to ingest, understand, organize, and query digital content from multiple data sources.“

Cognitive search isn’t driven by keywords. It uses artificial intelligence to analyze the relationships between words. In plain language, cognitive search has a better understanding of the user’s intent. That leads to results that are much more meaningful and useful.

Compared to keyword-based enterprise search engines, cognitive search is an evolutionary leap forward. That’s because it looks for patterns and relationships within the data. It infers the user’s intent and delivers contextually aware information. That ensures the results are highly relevant to the user’s search request.

How Does Cognitive Search Work?

Queries often lack the context that’s necessary to find relevant information. That’s why cognitive search uses a more intelligent approach to better understand every query and tries to predict what each user is trying to find.

Cognitive search leverages the power of AI, specifically natural language understanding and deep learning. These capabilities enable the search engine to operate with more context than typical search engines. This is why Gartner calls these “insight engines.”

“In contrast to search engines that provide links to original source materials such as documents and videos, insight engines can also provide contextual information about the fact or entity in question,” Gartner analysts wrote in a Magic Quadrant report in September 2019.

Benefits of Cognitive Search

Cognitive search works to better understand the query. It also strives to understand the person making the query. Because of this, cognitive search delivers more useful results. But it also offers a multitude of other advantages.

Cognitive search delivers a better user experience.

Intelligent search solutions use natural language processing (NLP) that allows them to map the semantic relationships of words.

For example, when a user types in a search term, a cognitive search engine will also look at related words and synonyms. It may suggest relevant terms while the user is still typing. These auto-complete suggestions help users find and discover information almost at the speed of thought!.

Over time, a cognitive search engine becomes more aligned to individual users. It can automatically tune the results of future searches to become more relevant for each user. The platform can also learn from past interactions and provide an increasingly personalized experience for each user.

Cognitive search strives to understand more than the query itself. It also tries to understand the user’s intent behind the query. As a result, it directs users more immediately to the content they want. Users get a more effective and satisfying user experience. This is particularly effective for applications in customer-facing search.

Cognitive search powers other applications.

Enterprises that build virtual digital assistants and other applications need powerful search tools. Cognitive search solutions operate behind the scenes. They give developers the power to embed highly effective search in applications using SDKs, APIs, and other visual design tools.

Cognitive search platforms make better sense of data.

Cognitive search platforms do more than connect data sources together. They also organize the data so that it’s more efficiently discovered by queries.

They use a combination of techniques including indexing, text analytics, and AI technologies. For that reason, cognitive search can scale to handle almost any type of data, not just text and databases, also images, audio, video, and other types of structured or unstructured data.

Cognitive search extracts hidden value from your data.

On average, as much as 80% of an enterprise’s data is unstructured and won’t fit into a database. Yet it may still contain a wealth of hidden value.

Cognitive search quickly and accurately gauges the relevance of content and data in relation to a given query. That allows for more in-depth research. It enables businesses to unlock tremendous value that might otherwise go undiscovered.

Cognitive search delivers more insightful results.

Cognitive search works to understand the natural language of the user. It uses machine learning algorithms and Natural Language Processing (NLP) for meaningful results.

It can predict the user’s intent, even if they don’t use the exact keywords. It can help them find more insightful results than they would have found on their own.

From the user’s perspective, it’s like talking to an experienced librarian. The platform can point them toward helpful resources even if they don’t know exactly what they are looking for.

Cognitive search offers personalized recommendations.

Over time, a cognitive search platform can personalize results for individual users. It can factor in previous search history and usage patterns. It begins to learn the preferences and interests of each user. Then it delivers content more relevant to their needs.

Cognitive search learns to improve itself over time.

Cognitive search platforms have the benefit of machine learning algorithms. Unlike keyword-driven search engines, cognitive search becomes more powerful over time.

The platform draws on implicit and explicit feedback. It can continually learn and become increasingly effective. By fine-tuning its own search results, it teaches itself how to improve performance. That’s how it produces more insightful, more relevant, and more meaningful results.