Vice President Jeff Deyerle and Principal Sajid Shaikh, two Business Information Management executives with Capgemini Financial Services, recently shared their thoughts on leveraging a data warehousing approach to address regulatory compliance in the capital markets industry.

Jeff Deyerle	Sajid Shaikh

Kathy Kramer: First, let's set the context for this interview. How important are compliance mandates to doing business today?

Jeff Deyerle: The market is constantly evolving. New avenues for investors to access the market appear almost monthly, and new, complex investment vehicles are being introduced with greater frequency. All of this newness certainly creates opportunity, but it also carries risk.

Compliance mandates help mitigate that risk and, as a result, are critical to the continued success of the capital markets industry. Rules and regulations enforce fair market access, equitable pricing practices, price transparency and trade surveillance, to name a few benefits. Examples of such rules and regulations are:

	Order Audit Trail System (OATS)
	Trade Reporting and Compliance Engine (TRACE)
	Derivative Program Trade Reporting (DPTR)
	Municipal Securities Rulemaking Board (MSRB)
	Regulation National Market System (RegNMS)

So, not only do compliance mandates level the playing field by keeping investors informed and protected, but they also inject a level of transparency into the environment that garners investors' trust in the system. Companies that neglect to comply risk heavy fines and, more importantly, damage to their reputations.

KK: How has the complexity increased, and what impact is that having on business decision making?

JD: It's primarily a matter of volume and frequency. There is increased pressure on broker-dealers, for example, to collect all relevant transaction and indicative data, going back as far as seven years of history in some cases. And the RegNMS guidelines for market access and trade-through processing requires proof of best execution, which means broker-dealers have to take a snapshot of the market at the time of execution and prove they provided their customer with the best possible execution price. This results in a tremendous increase in data volumes; we've seen it grow as much as 300 percent.

Conversely, as data volumes are increasing, reporting deadlines are getting shorter and shorter. OATS guidelines, for example, stipulate that the NASD [National Association of Securities Dealers] has to receive compliance reports for equity transactions by 5 a.m. every day. And TRACE-eligible fixed-income transactions must be reported within 15 minutes of execution, until next year when that time interval decreases to seven minutes.

A real-time compliance data warehouse can serve as a remedy to both the volume and frequency issues, because it can monitor transactions as they occur and can distribute data to downstream recipients as quickly as technically possible. Not only does the real-time capability facilitate compliance with some of the timing requirements, but it also helps to spread data processing loads over an entire day, rather than over-burdening already tight batch cycles.

KK: How have businesses traditionally managed regulatory compliance? Has regulatory compliance management long been part of the data warehouse—or segmented from it?

Sajid Shaikh: The data warehouse has not always played a key role in the compliance arena. Traditionally, most firms embedded their regulatory and compliance applications within specific front- and middle-office applications. For example, trading systems supported pre-trade compliance, middle-office systems addressed post-trade compliance, and so on. The reporting requirement was at best end-of-day or next-day.

KK: Why does keeping regulatory compliance management separate from data warehousing solutions no longer work?

JD: Regulators require increasingly sophisticated tool sets to perform supervisory controls and demand complex governance reports to ensure compliance. Many compliance reports must combine near real-time transaction information with historical scenario simulations. Siloed, overnight reporting solutions that are linked to transactional systems simply can't handle the data volumes and reporting frequency requirements, much less the scenario modeling and analytical needs of a firm's compliance analysts.

Additionally, separating compliance management from the data warehouse creates overlap, inconsistency and unnecessary overhead. Basically, it's more time-consuming and expensive not to have the data warehouse. Why would I manage one compliance reporting system per trading system and per regulation, when I can leverage a single data warehouse for all of my compliance reporting needs and across all trading platforms?

KK: How can a data warehouse help address multiple regulations?

SS: Well, to start, it wouldn't be possible to address multiple regulations without the significant strides that have been made in the area of data warehouse technology. Solutions such as those from Teradata make it possible to quickly store, manage, access and analyze the vast amounts of data required of a compliance data warehouse.

As we've discussed, the majority of the regulations we're currently helping customers with are based on transactions. So, much of the information required for OATS reporting can also be used for RegNMS reporting. In turn, we're finding that the work we've done around RegNMS compliance reporting is reusable in responding to MiFID [Markets in Financial Instruments Directive] requirements, which will increasingly become a more important part of our work.

It is crucial to note that the information required for a compliance data warehouse can be of great value outside of regulatory compliance efforts. We're helping our customers use this information to obtain management analytics, such as insight on the efficiency of trading platforms and performance of a specific trading desk; to feed fraud detection systems; to support risk-management reporting relative to credit exposure, market, and operational risk. And we're getting closer and closer to an infrastructure and approach for achieving insight and analysis across all products and all asset classes.

KK: If an organization has regulatory data in multiple data marts, what first steps should it take to centralize that data into a single data warehouse?

SS: It's not uncommon in large organizations to have multiple compliance-oriented data marts. In fact, we often see firms that have multiple data marts serving a single regulation. And while it is common to look at the technical cost efficiencies of converging multiple data marts—hardware consolidation and elimination of software maintenance costs, for example—we feel that the business benefits of a consolidated approach to information analysis far outweigh the technology benefits.

We recommend that the first step in centralizing regulatory data should be a hard look at the systems of record for each trading operation and a determination of where the most compliance-related failures are occurring, especially when multiple platforms are executing the same asset classes. Once that assessment has been performed, it is very straightforward to build a business case that can justify rate-of-failure reductions in line with an overall expected spend on a consolidated warehouse. It is often easiest to start with equities and equity options, as those systems tend to have the most robust data publication capabilities.

Next, we develop blueprints, starting with subject area models that are typically focused on a specific asset class, to ensure that we have a complete view across the relevant transactions for that particular asset class. In developing a blueprint, we map the data contained in each existing mart to determine information overlaps, redundancies and gaps. Finally, we develop roadmaps, identifying the relevant sources of information, highlighting opportunities to streamline data feeds, and designing structures to facilitate both real-time data acquisition and efficient reporting.

Once we've completed the blueprint and roadmap exercises, we're ready to begin the work of consolidating data models, data feeds and downstream data distribution and reporting processes.

KK: What are the benefits of having regulatory compliance data in a data warehouse?

SS: There are many. In addition to some of the items we've discussed previously, such as accuracy and timeliness of reporting, risk mitigation and operational efficiencies, some other benefits include:

	Proactive management and monitoring of items such as trade-through analysis, best executions analysis, short sells reporting and market data analysis
	Full transparency and audit trails across the life span of any transaction
	A single source for all global compliance reporting for OATS, RegNMS, MiFID, TRACE, MSRB, DPTR, AML [anti-money laundering] and trade surveillance
	Improved management analytics relative to the trading process—business metrics aggregated by desk, trader, product, account, client and so on

KK: Above and beyond compliance, how can having regulatory compliance data help a company in other ways—such as in competitiveness, customer loyalty, and bottom-line benefits?

JD: Once a compliance data warehouse goes live, one of the primary benefits becomes the ability to analyze the life cycle of a transaction. Regulatory compliance facilitates the collection and analysis of information regarding daily trade and fill-rate statistics, trade performance, price improvement and average time to execute via ECN [electronic communication network], exchange and broker. It facilitates both historical and real-time trend analyses. All of this information, when combined in a single environment that can be easily analyzed and reported on, can help the firm improve the efficiency and productivity of its trading environment, at people, process and technology levels.

Additionally, the level of data management maturity required to build these real-time warehouses may help crack one of the bigger data problems experienced in the capital markets industry. It is commonly accepted that about 5% of all trades fail due to data quality issues. An effective compliance data warehouse sits on a foundation of data mastery, which in our view encompasses data governance, metadata management, data integration, and data quality. When you consider that a data quality error could affect a seven- or eight-figure transaction, that becomes a tremendous impact on the bottom line. T

Kathy Kramer is director of strategic partnerships for Teradata.

Teradata Magazine-June 2008