NEW YORK, LONDON AND SINGAPORE – September 13, 2016 – Xenomorph, the data and analytics people, has joined Microsoft’s Enterprise Cloud Alliance, further strengthening the long-standing relationship between the two companies. The new agreement will ensure additional support and deployment options for customers of Xenomorph’s flagship TimeScape EDM+ enterprise data management plaltform and TimeScape MarketPlace […]
One of the central tenets of EDM is that it should promote trust and confidence in a firm’s data assets by applying a consistent approach to assuring data quality. However, in order to achieve that goal (particularly with regards to consistency), it is important that EDM teams can service all of their stakeholders’ data requirements—from […]
Pay Attention to Proprietary DataPress Coverage
In an article for Inside Reference Data, Xenomorph’s Brian Sentance raises awareness about how to use derived data. FRTB and capital adequacy issues can be addressed with better understanding of proprietary or derived data. Financial institutions rely heavily on information sourced from vendors, whether that’s market or reference data. Yet their most valuable data sets tend to be those they create themselves. Proprietary or “derived” data gives these institutions an edge by providing the ability to spot mispricing of risk, capture alpha and outperform the market. Given the importance of proprietary data and analytics…
Utilizing Best Practices in Data Management to Avoid Regulatory Headaches and Market Risk LossesWebinar
Data management has become a serious subject for both regulators and capital market firms as they strive to avoid another financial crisis and P&L losses. It is increasingly difficult for large firms to manage complex, multi-asset class datasets across numerous trading and risk systems while maintaining data integrity, completeness and timeliness. Organizations expect fewer people to work on more projects with tighter deadlines, while expectations for data quality remain very high. For this webinar Xenomorph teamed up with Numerix to discuss the importance of proper data management to avoid regulatory issues and P&L losses.
NEW YORK, LONDON & SINGAPORE – July 25, 2016 – Xenomorph, a provider of data management technology to banks, investment managers and insurance companies, has announced a significant new funding agreement from HSBC. The funding has enabled the business to accelerate global go-to-market plans for its TimeScape EDM+ financial analytics and data management platform. TimeScape […]
Xenomorph CEO Brian Sentance Named Top Financial Technologist by Institutional InvestorPress Release
New York, London and Singapore – July 19, 2016 – Xenomorph Chief Executive Officer Brian Sentance has been named as one of the world’s top financial technologists by Institutional Investor. This is the second year running that Sentance has made the magazine’s Tech 50 hall of fame, validating the continued success of Xenomorph, the enterprise […]
This paper describes the ‘fundamental’ nature of changes associated with the revised Basel III market risk capital framework and the broad set of data management challenges that firms will face as a result of the new framework.
Briefing Note: TimeScape EDM+ FRTB Data Management SolutionProduct Literature
The Basel Committee’s Fundamental Review of the Trading Book (FRTB) will cause ‘fundamental’ changes to the industry’s market risk capital framework, and in turn, prompt a broad set of data management challenges. This briefing note outlines how TimeScape EDM+ addresses these data management challenges and describes the customer benefits from implementing our advanced enterprise data management solution.
It has been a couple of years since I last caught up with MarkLogic (click here for 2014 event blog post) and their brand of NoSQL database so given some of my colleagues recent work with NoSQL databases I thought I would have a catch up at their event in London this week. Many of […]
Data validation and cleansing is a methodical discipline. Start with a set of rules (or tests) to identify anomalies. For example, if a data item hasn’t changed (and is possibly stale); if it has changed beyond typical norms (and is possibly an error); or if two sources differ in their value for the same item […]