What is cognitive search?
Enterprise data is vast. Studies estimated enterprise data to be over two petabytes in 2022, with an average annual growth of 42 percent. This data is historical and real-time, coming from consumers, employees, and machines. It is structured and unstructured, across systems located in the cloud or on-premises. Enterprises are producing more and more data.
Data sitting idly in the servers doesn’t serve its purpose. Data must be accessible for you at the right time. However, traditional search mechanisms fall flat here. They face several challenges in bringing relevant and reliable information faster for users. They have several limitations, such as keyword dependency, limited semantic understanding, inability to handle unstructured data, scalability, etc.
If you explore two-decade-old information, how good can a traditional search tool be?
NHS Foundation Trust also had a similar situation. They wanted to leverage their 23 years of enterprise data, part of which was in handwritten notes. For a traditional search engine to read the unstructured data, they would have to use optical character recognition (OCR) to make it structured. That’s an overhead. Also, they have millions of documents through which searching for information using traditional methods is not reliable. But for the trust, 23-year-old data had a lot to offer.
NHS Foundation Trust’s challenge was not unusual. Most companies face challenges like this. Traditional search doesn’t work well when the data is huge, old, and unstructured. This is where cognitive search comes into the picture.
Cognitive search uses artificial intelligence technologies, such as natural language processing, optical character recognition, and machine learning, to provide more relevant and personalized search results. Cognitive search tools can index structured and unstructured data, enabling you to leverage your data completely.
NHS Foundation Trust too turned to cognitive search. They used Azure Cognitive Search, which enabled their employees to pull handwritten PDFs effortlessly. They didn’t have to convert the unstructured data into structured data as Azure searched everything everywhere and gave them more relevant information. That’s the beauty of cognitive search. Let’s understand a few of the many limitations of traditional search tools that cognitive search overcomes.
Keyword dependency
Lack of contextual understanding
When you discuss last quarter’s performance with your colleague, the conversation naturally flows. You won’t underline ‘last quarter’s performance’ in every dialog. The conversation flows naturally. Traditional search tools cannot come anywhere close to human interaction. They lack contextual understanding. If you simply ask, ‘what’s the price?’, it won’t understand. You have to provide complete context for each query.
Cognitive search leverages natural language processing and semantic search techniques to understand the intent behind your query. It can interpret synonyms and linguistic variations and are late a query to the previous one to provide more accurate and contextually relevant results.
Information overload
Inability to handle unstructured data
New-age cognitive search with enterprise-grade Generative AI
Cognitive search overcomes the limitations of traditional enterprise search and enhances the search experience with more relevant, personalized results. However, the incumbent cognitive search lacks generative AI capabilities. Generative AI makes life easier for users. With existing cognitive search models, you can fetch results from structured and unstructured data sources. However, you may have to go through multiple results to get the right answer to your question. This is time-consuming. In some cases, it is also ineffective.
On the other hand, cognitive search with Generative AI goes one step further, generating relevant answers to your queries. The AI tool goes through all the available data, structured and unstructured, and generates the most relevant answers in your preferred language.
For example, if you are using Azure Cognitive Search, you can integrate ChatGPT with the cognitive search using Azure OpenAI Service. This combination enables you to interact with your enterprise data, from across data sources, in natural language. Just like we have ChatGPT in Bing, you can have ChatGPT in your enterprise search, acting on your own enterprise data.
How will Cognitive Search with Generative AI revolutionize enterprise data?
Get answers, not references
Break language barriers
Enhance search
Incumbent cognitive search learns the context. However, this learning is based on machine learning algorithms that you cannot change. However, in the generative AI-powered cognitive search, you can always update the prompt patterns. So, the search engine learns more effectively and provides more relevant information.
With generative AI-powered search, your employees can access information more easily and make better decisions. You can incorporate this modern cognitive search into your customer support pages so that your customers can find solutions to their problems much faster, enhancing customer experience. Thus, enterprise-grade generative AI will help you unlock the full potential of your enterprise data.