The Benefits of Mature Data Management
August 25, 2008
In today's stringent regulatory climate, a greater number of firms are struggling with data management at a time when it has never been more critical. Organizations that adopt and deploy a mature approach to data management dramatically improve their ability to meet increasingly complex compliance requirements. In addition, they improve the data content quality of risk management efforts; achieve a sophisticated level of insight into corporate performance; and open themselves to gaining a competitive advantage through real-time predictive analytics, which simply are not possible on tactical data management platforms.
In recent years, the Financial Industry Regulatory Authority's Order Audit Trail System (Oats) and the Securities and Exchange Commission's Regulation National Market System (NMS) have generated an uptick in data processing requirements around equity transactions. Now that firms are accountable for providing visibility, auditability and proof of best execution for these transactions, data volumes have increased by as much as 300 percent at some institutions. And while many firms have put systems in place to meet these regulations, they are often not managing their data as efficiently or effectively as they could.
The data management and reporting systems typically put in place to address compliance requirements can often be categorized as:
Redundant. Due to fragmented responsibility and budgeting for compliance solutions, firms frequently build multiple systems in response to a single requirement. One Wall Street firm, for example, developed Oats solutions for every equity trading system they employed, effectively putting their data into silos as well as overspending on the response.
Manual. Some firms have chosen to outsource their compliance reporting solutions or deploy internal teams to comb through the relevant data and respond to exceptions--a brute-force approach that may address the immediate compliance requirement but provides little downstream value to the firm.
Suspect. The real danger of not employing a mature data management approach in response to regulatory requirements is underreporting, reporting inaccurately or missing reporting deadlines--all of which expose firms to fines and, more importantly, reputational risk.
Meanwhile, the capital markets regulatory environment is only getting more complex. Whereas Oats and Reg NMS primarily address equities, Europe's Markets in Financial Instruments Directive (MiFID) requires auditability and proof of best execution across all asset classes.
Data Management Maturity
There is an upside to a mature approach to compliance data management that exceeds the regulatory response itself. For example, when tracked and stored in a centralized manner, the data required for Oats and Reg NMS reporting provides an end-to-end view of the complete life cycle of an equity transaction. MiFID requires much of that same information--and as a result, Oats and NMS infrastructure can be leveraged for MiFID responses-and adds party information and other asset classes to the picture.
The combined result provides: insight into sales effectiveness by desk, trader and division; real-time insight into operational efficiency; visibility into product performance across lines of business; greater visibility into customer behavior; and faster insight into fraudulent activity.
Firms at the forefront of data management leverage this same body of information for predictive analytics, modeling customer behavior and product performance to maximize efficiency and profitability.
In our data management capability model, Capgemini describes five levels of maturity. Level one--"firefighting"--is distinguished by ad-hoc reporting, little to no workflow management and minimal data management controls, among other characteristics. At the opposite end of the spectrum is "decision driving," characterized by systems that can perform analysis and provide strategic direction; customized views of "golden copy" data based on user roles; and the ability to analyze data from past outcomes, identify trends on a widespread basis throughout the firm, and even predict trends and outcomes on a reliable, consistent basis.
