Data is the most valuable asset for any enterprise today. That’s why knowing what’s where and how to access it in time is super critical for any company’s business success.

ThirdEye’s client is a multinational financial services bank headquartered in Washington DC that funds social development projects in Latin America and the Caribbeans by providing financial and technical support. The bank boasts of 100+ offices sprawled across the globe and supports a multilingual workforce. The bank’s officers have worked on various projects by which they have gained a lot of real-world experiences. They now possess a variety of skills, gotten a lot of operational knowledge and have developed themselves into ‘Subject Matter Experts’ over certain key topics areas. These officers work from geographically spread locations using documents that are in English, Spanish, Portuguese and other languages.

However, geographical and cultural diversity among employees results in broad knowledge management issues. Finding relevant information at the right time is critical for business success. But the reality is that this information is tucked away in documents, spreadsheets, databases, web portals, etc. and is in many silos making it impossible to access, analyze and search through them as and when required.

Therefore the bank felt the need to create a single integrated knowledge management platform for all its employees to access, identify and reach out to individual SME’s seeking help on current projects and tasks at hand.

ThirdEye worked with the client to implement a cognitive computing system leveraging IBM Watson services on the IBM Bluemix cloud infrastructure. The overall solution included virtual agents that understood natural language query inputs and used cognitive deductions to respond to the conversation; simulating human-like conversations between users and the system.

ThirdEye leveraged IBM Cognitive Services for advanced text analytics including, keyword extraction, entity extraction, sentiment analysis, emotion analysis, concept tagging, relation extraction, taxonomy classification, author/person extraction and relevance score extraction. The data sources included enterprise, social and web data. The output was further processed to eventually create JSON structures on a per individual SME’s profile basis with all relevant metadata and associated information. SMEs were retrieved and ranked in real time as per the user’s queries.

By deploying this system, the bank believes that productivity across the globe has significantly increased for identifying Subject Matter Experts. Such enhancements have improved operational efficiencies by a margin of 30% to 40% on all funding and technical project evaluations.

In the future, the scope of the system will be further expanded to include multiple uses cases including but not limited to knowledge management of subject expertise, improving cross-border collaboration and employee engagement across project implementations.

Thus, knowing one’s data and more importantly being able to access it on time is truly rewarding many times over the initial investment.

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