Technology Category
- Application Infrastructure & Middleware - Data Visualization
- Cybersecurity & Privacy - Endpoint Security
Applicable Industries
- Finance & Insurance
- Mining
Applicable Functions
- Procurement
Use Cases
- Time Sensitive Networking
Services
- System Integration
About The Customer
The customer in this case is AYLIEN, a data analysis company. AYLIEN is a company that is passionate about data and uses world events like the Panama Leaks to showcase their technology and solutions. They are interested in mining large data sets for interesting data points and insights. They have developed a News API that allows them to scan thousands of monitored news sources for articles related to specific topics. The API also has text analysis capabilities that allow them to identify and index key data points in those articles for further analysis.
The Challenge
The Panama Leaks, the largest leak of its kind, revealed how the world's rich and famous were moving and hiding money across the globe. The leak consisted of over 11 million documents showing how money was laundered through offshore accounts and entities. The documents implicated a wide range of individuals, from world leaders to soccer stars. However, the sheer volume of data presented a significant challenge. The documents, dating back to the 70s, included emails, contracts, transcripts, photos, and even passports. The challenge was to mine this massive data set for interesting data points such as people mentioned, organizations, locations, and topics discussed.
The Solution
AYLIEN, a data analysis company, decided to leverage their News API to mine the reports. They started by building a simple search using their News API to scan thousands of monitored news sources for articles related to the leak. They collected over 4,000 articles which were then indexed automatically using their text analysis capabilities in the News API. This allowed them to identify and index key data points in those articles for further analysis. They focused on keywords, entities, concepts, and topics. They also used the Time Series endpoint in the News API to graph the volume of stories over time, showing how the volume of stories increased as the story spread. They also used the API to extract any mentions of Entities and Concepts in the articles indexed. The main entities they focused on included; keywords, people, organizations, and countries.
Operational Impact
Quantitative Benefit
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