Data-Driven Business Growth with ECM & Data Science Implementation

The challenge

Risk Intelligence is a world leader in risk assessment and planning, specializing in threat analysis at sea, in port and on land with a global risk alert system and 24-hour watch team. Risk Intelligence has headquarters in Denmark and operates in seven countries. The company offers its intelligence through a digital platform, the Risk Intelligence System (MaRisk, PortRisk and LandRisk), which presents a comprehensive global view of real-time environment risks for maritime, port and land areas.

Risk Intelligence has deployed a number of SaaS solutions such as Salesforce, Microsoft Dynamics, AdminControl and Computershare. The company kept a large amount of “residual data” — on threat types, conflicts, customer projects, historical corporate data, geographic areas and much more. This information is crucial to analysts in developing risk assessments. Much of their information was stored in an Atlassian Confluence solution — where information had become harder to find, also with some instability issues. 

Furthermore, the company handles and analyses huge amounts of full-text data from different sources on a daily basis, where high-quality analysis and time-to-marked is essential to continue to be best in class in delivering relevant and timely risk analysis to the customers in the Risk Intelligence System.

The solution

The solution to the transformation happened in two phases. First modernizing the structure and access to ‘residual data’ through a new metadata model and migration of data from Confluence to M-files to break the data silos, and structuring data for efficient sharing and retrieval. 

After ensuring the knowledge foundation for the customer’s analysts, we went through an ideation phase to identify new ideas for optimization and innovation. The ideas became part of a backlog of products, that all aim to optimize and innovate Risk Intelligence through new tech solutions like AI, machine learning, RPA etc. 

The first product implemented a major optimization of the processes for handling data in an AI-enabled data pipeline, enabling the analysts to process more information, faster and with higher quality.