In Financial Services, there has been a longstanding need to improve analytics in areas where people, products, processes, and counterparties are highly connected and involve a high level of interaction and engagement. These include information/transaction supply and demand chain, identity and access management, fraud detection, asset metadata management. These use cases require more versatile data structures for management and analysis.
According to leading analysts, graph analysis is the single most effective competitive differentiator in enterprise organizations. Graphs are the most versatile of all data structures. In fact, other shapes in the polyglot persistence paradigm are weak projections on graphs -- tables, documents, and key-values all have their own unique limitations that dictate business capability (and strategy) instead of the inverse. By treating data relationships as first-class citizens, graph databases allow for flexible modeling of the complex information topology prevalent in the modern financial enterprise.
Join fellow IT Leaders from throughout the financial services industry for an intimate roundtable discussion on the current challenges in data management, and the future of business applications as we head down this pathway of digital transformation. Graph databases allow questions that have been difficult or impossible such as "tell me everything you know about this product (as opposed to n-way joins related to peripheral dimension connections)," or "tell me everyone who has had access to the information in the report at any point upstream (lineage and provenance)" or "what is the impact of a change in a product (dependency impact), or show me all my suppliers who are not also my customers (contextual relationships).
The roundtable will be moderated by Mayank Gupta, who comes from an extensive enterprise data management and distribution background, leading teams at Morgan Stanley, UBS and most recently as a product manager at Bloomberg's Enterprise Solutions.
Topics will be Financial Services specific, and will include:
- Managing data management
- Logic as data via state machines
- Linked data is all about the graph