Overcoming The Corporate Discoverability Challenge

Enterprise Search Enhancement

At Pingar, we believe that every piece of information is valuable. That’s why we’re passionate about making it easy for companies to access and utilise their data. And one of the most important aspects of data utilisation is discoverability.

Let us share a story with you. John was a great salesman, but he had only one day to prepare for a presentation with a hot lead. He quickly dived into the CRM to find any information that would help him sell. He found most of the documents that he was looking for, but he still lost the deal. He would have won if he knew that useful insights about the lead were recorded by a customer support specialist, business analysts, and a colleague in a different department. This is a perfect example of the challenge of corporate discoverability - the ability to find documents that are useful to a user, but they have no knowledge of their existence.

This is where Natural Language Processing (NLP) comes in. NLP is a tool that enables us to read the content within an Enterprise Content Management System (ECMS) such as SharePoint, and understand the context of each document. Keyword search alone simply can’t understand the relationships within documents to enable corporate discoverability.

Improving Findability

The most common corporate challenge is finding information that you need. It might be a simple email lost in Outlook or a document that was created a year ago. At Pingar, we enrich your knowledge assets to ensure easy document findability. We apply Natural Language Processing methods to enable an ECMS to quickly locate information that you need.


Providing Discoverability

Corporate discoverability is a process when a Natural Language Processing tool is trained to find relevant content to populate your search inquiry. We deploy knowledge structures that are enriched with alternative labels (a form of synonyms) and relationship identification functionality. These structures allow employees to discover content that they are unaware of, not only the specific content they are looking for. This enables multiple users to quickly discover the same content despite them having different ways to describe it.


Limitless Document Auto-Categorisation Capabilities

Various research shows that approximately 25% of corporate data is structured within databases or similar systems. The other 75% of the corporate data is unstructured such as the many types of documents. It can contain sensitive information that must be protected, contracts that must be retained and documents that should be utilised. DiscoveryOne reads, analyses, tags, and categorises unstructured data and therefore gives insights and visibility over your data. It simplifies content management, increases security, and simplifies retention practices.


Using artificial intelligence to drive a change

At Pingar, we use supervised learning to help users categorise their documents. Users can provide a sample set of documents that represent a particular category of documents to be created. Our technology then reads and analyses the sample documents and is able to recognise additional documents for the designated category. We analyse sample documents to identify patterns that reflect the category and create them as rules. Each rule is given a weight depending on how important it is. Then artificial intelligence will use these rules to identify documents that should belong to a category. It will also identify new rules to advance itself.

We believe that every piece of information is valuable, and that’s why we strive to make it easy for companies to access and utilise their data. Our focus on discoverability and Natural Language Processing allows us to make information that was previously hidden, accessible to you. We also use supervised learning and artificial intelligence to ensure that categorisation is easy and accurate. With Pingar, you’ll be discovering valuable information every day.