A lot of organizations have begun making significant investments in digital transformation in order to fill their operational gaps. One of the areas seeing this transformation is search, mainstream search is broken. Data volumes are growing at an exponential rate – the digital world is expected to create 163 zettabytes of data in 2025, a 10x increase compared to 2016. The concern for a lot of companies will be making information easily accessible to employees and customers. Employees already spend too much time searching for content. According to one study, knowledge workers spend 20 percent or more of their day searching for relevant and timely content. Employees should have the ability to find information, and gain insight, via a spoken question, an image, a natural language text input, or virtually any other way that feels intuitive and natural. Traditional enterprise search functions have shortcomings that make it difficult or at times impossible for users to find the information they seek. Modern, machine-learning based search is capable of transforming the way employees find answers and gain insights. This approach is commonly referred to as ‘cognitive search’, an increasingly powerful way to handle the data and knowledge-sharing challenges that modern enterprises commonly face.
Cognitive search is radically transforming the process of retrieving files. Search has now transcended basic keyword matching; it has evolved to become ‘cognitive’ – the ability to provide relevant answers to natural language questions. Manually searching for documents and files within enterprise systems is declining. Large enterprises have begun showing a dire inclination towards this disruptive technology. With all the hype around cognitive search and artificial intelligence (AI) in general, it’s seemingly difficult to grasp how to actually apply these new technologies to improve the workplace. Having a basic understanding of cognitive search and how it relates to traditional enterprise search is the first step towards establishing an effective cognitive search system and setting it up for ongoing growth.
Enterprise Search Vs Cognitive Search
In a recent brief, Forrester, research firm, defined cognitive search as – “the generation of enterprise search solutions that utilize AI technologies like machine learning and natural language processing (NLP) to ingest, understand, organize, and query digital content from several data sources”. Cognitive software mimics human behavior like perceiving, inferring, reasoning, and making hypotheses. And when coupled with advanced automation, these systems can further be trained to perform judgment-intensive tasks. Enterprise platforms with cognitive computing abilities are capable of interacting with users in a natural manner. With time, they can learn user behavioral patterns and preferences. This allows them to establish links between related data from both external and internal sources.
The major drawback of traditional enterprise search is that information is typically poorly defined and datasets are dispersed across multiple systems. Although it allows for in-depth indexing, tagging and keyword implementation, this is not always sufficient when making data based decisions. Cognitive search fills in the gaps, and augments what enterprise search is capable of doing.
Cognitive search offers the potential for phenomenal improvements in the efficiency, relevance, and accuracy of insight discovery. While some may view cognitive search as simply traditional search augmented by artificial intelligence and machine learning, there is actually a complex combination of capabilities that distinguishes, and makes it superior to traditional enterprise search. Cognitive search transcends search engines to amalgamate a vast array of data sources, along with avant-garde tagging automation, greatly improving how an organization’s employees find, discover and access the information they require to complete their tasks.
Most of the design elements used to build enterprise search can be utilized as the foundation for implementing cognitive search. While enterprise simply locates the data, cognitive applies user analytics to it in order or enhance understanding while also unearthing deeper trends that may have otherwise been missed.
The Impact of Cognitive Search
The workflow of an estimated 54 percent of global information workers is interrupted a few times or more per month, when trying to get access to answers, insights and information. Cognitive search can shift that paradigm by extracting the most relevant piece of information from large sets of varied and intricate data sources. According to the Economist, while content doubles every 90 days, 80 percent of the content information workers rely on for core revenue generation activities remains unstructured. This dramatic growth of unstructured content has become a challenge for several enterprises.
With cognitive search, knowledge worker searching internal systems are more likely to find the information they need. Customers looking through a company’s website can more easily find answers to their queries online. From a customer service and marketing perspective, this is a huge plus since it directly translates to a reduction in call center volumes while increasing overall customer satisfaction. Like humans, cognitive systems learn on the job, as more information is made available to them. That’s excellent news, given the rate at which the digital universe is growing each year.
Most companies are already using cognitive applications to target marketing campaigns; however, cognitive search is yet to be widely adopted. This is starting to change, as NLP – which previously required complex hardware, approaches mainstream appeal, primarily via the cloud. Cognitive search will likely have a greater impact on enterprise operations.