All businesses have to deal with a large number of documents and make sense of the data contained within. From emails, reports, invoices, contracts and more, the amount of documentation a business needs to handle and can make use of is continually growing.
Unfortunately, this means a lot of employees time and effort is wasted looking for the right piece of information or answer contained within a specific document. The amount and disconnected nature of the information also makes it difficult for businesses to find links and gather insights from their data.
This is where an AI-powered intelligent search and text analytics platform can help businesses to eliminate data silos and uncover the hidden value of enterprise data - by combing through documents, both structured and unstructured, pinpointing and directing users to the correct information.
It can uncover patterns, trends and relationships otherwise hidden in your data in near real-time.
By using text analytics, it can also gain insights into customer concerns, changing policies or regulations,
enhance your chatbot answers to questions or update company policy documents
increase user satisfaction, find the root cause of problems and help businesses make informed decisions.
There are various use cases of intelligent search and we are showcasing one of them in the video above.
In this demo, we can see an insurance firm is using intelligent search to help them with the risk assessments of areas that might have been flagged as at risk in the latest climate change report.
Firstly the platform ingests the documents you wish it to search. You can do this by scraping the information from web pages or from uploading documents in a centralised location. Here we can see the latest climate report being uploaded
Using Smart Document Understanding, which uses object character recognition we can now teach the system about the different structures of documents. This, along with the pre-learnt models of recognition will help identify a variety of document structures.
You can teach it about the domain or industry-specific topics or terms it might come across. In this example, we are teaching it that Net Zero Carbon Emissions is the same as Carbon Neutrality.
We can now put it through relevancy training to improve the finding it returns for users. To do this you can ask Watson some common relevant test questions and rate its answer on the relevance. Watson can then learn from the feedback for later questions.
The tool is now ready to be asked questions through an interface of your choice. In this example, we are using a custom search engine, although many businesses like to use chatbots.
The tool will take the question ask and return with the answer
Data discovery and enterprise search can help businesses:
- Reduce the cost of data integrations and management
- Vastly improve their customer experience and meet customer response time SLA’s
- Save employees time and reduce workloads
- Increase productivity and increase revenue.