RIFINITIV (THOMSON REUTERS) MiFID II-complaint data provenance platform to see where every piece of data had been and who handled it
Background
Thomson Reuters is one of the world’s largest collectors and sellers of 3rd party data.
The usually got fined for breaches of contract on data repackaging but paying the fines was worth the indiscretion.
MiFID II was an updated regulation introduced as a response to the 2008 financial crisis. A key feature of this regulation was transparency. It’s aim was to increase transparency in financial markets by requiring more pre- and post-trade transparency for various financial instruments.
Problem
MiFID II was a big issue for Thomson Reuters. Their practice of combining arbitrary data and repackaging it to sell, without clear transparency and accountability, would not be legal. it had to be fully auditable at every stage of its transformation from source to end product.
Goals
To design a system that could track every piece of data from when it entered the Thomson Reuters ecosystem to when a client bought it.
To create a service which allows Thomson Reuters, their suppliers and customers to better audit and manage their informational assets. A platform to transform the ways in which Thomson Reuters manages third party data and content, from sourcing and onboarding, to building products.
To enable the Finance & Risk department to adopt a more transparent approach to data management and in doing so become more efficient while gaining exposure to fresh insights.
Solution
We designed Revelation. A platform which transformed the way in which Thomson Reuters manages and audits digital data content and product.
The platform ingests all obtained Thomson Reuters and 3rd party data, breaks down contracts and rights to create specific supplier services, and provides capabilities to recombine these into new products and services whilst offering auditable information on each entity's aspects through digital interfaces.
Users can view information, from various datasets, and combine or manipulate them with new tools. Everyone, from product developers to salespeople and customers can gain transparent insights on every aspect of a product and its constituent parts.
The system can identify new opportunities, using machine learning algorithms, to suggest the best information and packages a client should have.