There are two ways to address the perennial pain posed by any number of data management deficiencies: Firms can choose to do it on a case-by-case, incremental basis, or they can take the long-term and often prohibitively expensive approach and build and maintain their own data warehouse. The second option is generally more effective when it comes to managing and sharing data on an enterprise-wide basis, but it's also massively disruptive and time consuming.
Data quality has a significant impact on firms' abilities to manage their trading operations and risk on an intra-day basis, challenges that have become more acute with the advent of high-frequency trading strategies. Clearly, data managers have their work cut out for themselves, and the only thing worse than having a poorly defined, inefficient data management strategy, is not having one at all.
- Are the tools currently available for data management keeping pace with the growth of data for which they are designed to manage?
- What key operational areas of the buy side and sell side are adversely affected by poor data quality and incomplete data?
-Is building a single, in-house data warehouse a practical option for capital markets participants?
Norman Brower, Executive Director, Reference Data, Morgan Stanley
Philip Little, Managing Director, Wells Fargo
Michael McMorrow, Head of Data Management Services, Enterprise Information, AIB Bank
David Wiseman, Director of Business Development, Sybase
Tine Thoresen, Special Projects Editor, WatersTechnology (Moderator)
Waters Wavelength Podcast Episode 75: An Update on the Julia Programming Language; AI & Alternative Data; Digital Currencies
Julia Computing's Viral Shah talks about the programming language he helped create and what's ahead for it. Then James and Anthony talk about the pairing of AI & alternative data, digital currencies, and Game of Thrones.Subscribe to Weekly Wrap emails