01 Jun Dynamic Data Management
Date: June 1, 2002
By: Alex Tsigutkin
Each good dealer, fund manager or banker knows his or her positions. They also have different technology tools to help them find out what to do next and to record what they have done. But problems arise when upper management wants to know what everyone under them has done. And they don’t want it in a couple of days; they want it now.
Traditional approaches to enterprise risk data management technology try to define every possible deal type and portfolio as well as design a one-size single static universal data model, and then try to manage the transformation of source data into the new data model. Here’s the problem: dealing with changes. In the same way that business and technology is constantly changing, so is the source data model. This produces vast initial implementation efforts and the pain of ongoing maintenance. Failure is inevitable.
Time to market, dynamic portfolio views, proactive risk management and high performance are the key success factors of any buy-side firm. A dynamic enterprise requires a dynamic solution, and it starts with seamless data integration.
The first priority is to quickly integrate existing administration and market data systems with the results of analytical applications. This can be achieved if the system has the ability to learn, accept and maintain the data and communication structure of existing systems and dynamically store source data when necessary or directly link to the source itself. It must then be able to construct portfolios on the fly using visual business rules. Finally, it must be able to perform real-time forward-looking portfolio risk measurements.
A dynamic data management solution saves time and money if it can embrace what already exists, supplement what is needed, and accommodate changes through time. Real-time data access and delivery methods can be ODBC, XML, middleware messages and even various files formats, with plug-in capability to transform, enrich and validate source data.
Additional data sources can then be quickly defined, captured and used to extend existing data models and thus provide full data coverage and analysis. If visual business rules can provide source data mapping, create analytical data models, sophisticated business logic, aggregation hierarchy and operational workflow, the application programming log jam is removed.
Reprinted with the permission of Buy-Side Technology