Why Is Cognitive Search a Must-Have for Your Company?
We are going to make a bold statement. Cognitive search is a “must-have” technology for any serious, sizable business. Perhaps you’re thinking, why, of course! Or, you may ask, what is cognitive search, and why would I want it? The simple answer is that cognitive search is a new generation of enterprise search that improves customer experience and drives an increase in purchasing. At the same time, cognitive search makes employees more productive by connecting them with the information they need. This article explains how cognitive search works and explores why it’s a wise technology investment for a business.
The Forrester technology analyst firm offers a straightforward definition of cognitive search. To Forrester, cognitive search is “[a] next-generation search engine software that employs AI technologies such as machine learning (ML) to ingest, understand, and organize information from multiple disparate data sources to enable humans to find content, answers, insights and/or explore a large corpus of information.”
This definition is helpful, but a further explanation could be useful. The basic idea is to add cognition, which means acquiring knowledge through thought, to a search solution. It contrasts with traditional search, which matches a search term with words in a dataset. Instead, cognitive search seeks to understand what’s in the dataset and finds the best match for what the user is actually seeking.
For example, imagine a customer of an online bookstore is looking for a book of recipes. If the customer types “recipes” into a traditional search tool, it will find all books with the word “recipe” in the title. This might include books like “Recipe for Disaster: American Highway Safety in 2022.” A cognitive search tool, in contrast, would use AI and ML to “know” that the highway safety book is not a cookbook and is, therefore, not suitable to include in the search results.
How would the cognitive search tool know that the user wants to see cookbooks? A cognitive search engine seeks to learn as much about the user as possible. In this case, the tool might look at the user’s previous search history, websites visited, products clicked on, and more. The tool can develop a profile of the user that lets it know that he’s interested in cooking, not highway safety.
Law firms are a great example of how businesses are using cognitive search. With a growing number of legal organizations, there is an increase in competition and pressure to do more with less. This has forced many firms to assess how technology solutions can help them perform more effectively and more effciently.
One of the challenges many law firms are facing is retrieving information from various sources. This challenge is even greater for firms that are geographically distributed. There are thousands of lawyers that need to quickly find information that’s buried within millions of documents spread over disparate systems.
A cognitive search solution will thoroughly index a legal organization’s client profiles and case information, including unstructured data like documents, images, and videos. That way, lawyers can easily access the most appropriate documents or media to use as needed.
Cognitive search will also use AI to narrow down search results based on the information it has about the client and the questions they are seeking. Narrowing search results matters because offering a seamless for a legal team will lead to a higher rate of efficiency and and success.
Customer service is a cognitive use case that is both employee- and customer-facing. Cognitive search can provide a customer service representative with a complete, 360-degree view of the customer. The representative might be able to know immediately, what products the customer has purchased, as well as browsed. They might have access to unstructured documents related to the customer, such as legal letters, warranties, and so forth. As a result, the employee’s job gets easier and the customer enjoys a better experience with customer service.
Other employee-facing use cases include cognitive search applications in research and development (R&D). In this case, cognitive search can streamline the process of finding research data, which speeds up the R&D process. Compliance is another use case wherein cognitive search helps stakeholders discover previous hard-to-find data about financial transactions and regulatory filings, for example.
Cognitive search may sound like a “nice to have” technology in a business. The reason to see it as a “must have” relates to the many positive impacts it can have on your business. Consider customer experience. Given the intense competition for online customers’ attention and money, the better the experience at an eCommerce site, the more likely the customer will be to convert from browsing to buying. Making personalized product recommendations, for example, can drive buying decisions. This is a matter of revenue and growth—a good reason for cognitive search to be “must-have.”
Employee experience, inclusive of system user experience (UX), is no less important. In the digital workplace, for example, good search experience can improve the overall functioning of the environment. So much of the employee experience in the digital workplace depends on being able to find information quickly and easily. Cognitive search makes this possible by developing sophisticated profiles of the data while also parsing the employee’s work role and organizational context. UX proceeds to gets better.
Today, especially, with a major labor shortage in effect and employees rethinking their life plans and relationships with employers, factors that make the work experience more fulfilling are definitely a plus. In addition, better productivity is also positively affected by cognitive search. Having more productive people translates into lower operating costs and high earnings.
If you’re considering acquiring a cognitive search solution, here are some selection factors to keep in mind. Security and scalability are assumed to be part of the package. Other factors include:
- Intelligence - Cognitive search runs on AI and ML. To be effective, a cognitive search solution’s AI engine must be adept as what’s known as “signal capture.” This refers to ingesting and analysis of signals, comprising events such as clicks, user queries and so forth. User profile information will also factor into the AI process. On the indexing side, the AI and ML algorithms have to be able to interpret and parse data from a variety of sources. The tool must be able to recognize “entities,” e.g., distinguishing a person from a thing. Both areas of functionality require strong language interpretation technologies, such as Natural Language Processing (NLP).
- Information - A cognitive search solution has to access as many data sources as possible. This is a matter of data connectors. In addition, it’s not just breadth of data. Cognitive search has to be able to ingest and interpret different types of data, ranging from structured database data to unstructured documents, emails, social media and rich media.
- Operation - Cognitive search is another IT workload, so the easier it is to manage, the better off everyone will be.
- Application and architecture - The quality of the cognitive search application and its architecture are important considerations when choosing a solution. Ease and breadth of data connectors is part of this story. The application should also be as non-disruptive as possible. It cannot get in the way of other systems, nor should it slow them down.
Innovation is the underlying element of success in each of these focus areas. An innovative cognitive search vendor is one that will continually look for ways to be the best in intelligence, information and the like. It will also never stop trying to get better.
The corporate world has entered an era when traditional search will no longer suffice. Competitive pressures make it essential for companies to employ cognitive search technologies to drive ever-better outcomes in customer and employee experiences. The right cognitive search solution will be one that combines deep AI and ML with manageability, scalability, security and an efficient architecture.
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