Data Management

02:30 PM
Paul Rowady, Tabb Group
Paul Rowady, Tabb Group

Managing the Bottom Line Means Managing Data

Real-time reporting and risk management requirements are driving reference data improvements, says Paul Rowady, Tabb Group.

The events of the past decade have galvanized market awareness of, and action around, reference data issues. The real-time reporting requirements for swaps under the recent Dodd-Frank legislation and the Office of Financial Research's (OFR) role in establishing a global legal entity identifier (LEI) for systemic risk management are just two of the many catalysts that are driving incremental development in the reference data arena.

One of the primary problems with reference data management is that different datasets -- mainly security (UPI or equivalent) and entity (LEI or equivalent) -- are usually managed by different systems. Moreover, different systems almost always have different data governance processes. A mixed approach to reference data management (RDM) breeds unnecessary complexity, avoids placing processing standards around all reference data domains, and risks undermining the functionality of the entire risk and reporting platform at a time when output requirements are going up significantly.

The role of the LEI in this discussion is potentially important enough to cause the largest firms to upgrade their RDM systems. Risk and regulatory reporting for counterparty exposures and OTC derivatives trading among the world's largest banks place a huge burden on RDM platforms. From Dodd-Frank to Basel III and beyond, transparency around counterparty credit risks is the primary driver for LEI. That said, the road from where we are today to a state of greater clarity about these risks is a challenging one.

Inconsistencies and other quality issues with existing counterparty data are just the beginning. The central issue is that a lot of reference data, particularly for customized products, is generated through manual inputs. If there isn't a detailed and rigorous process for managing the life cycle of this data, it can cause all sorts of problems. Seamless onboarding of new instruments by business users, with minimal changes to the overall system, is one very common scenario that causes inaccuracies, incomplete records and inconsistencies. These deficiencies impact all of the downstream applications to which they are connected.

Data Is Revenue

Enhanced data management is a primary component of revenue generation. In other words, data is revenue. The sooner data managers and their IT accomplices adopt this mantra, the sooner they will give their firms a chance at harvesting opportunities ahead of the pack. Every market-leading firm is exceptional at data management, by default.

There are four areas in which improved RDM generates material downstream impacts: risk measurement, regulatory reporting requirements, trade strategy innovation and operational alpha. Each of these impact zones offers opportunities to develop competitive advantages. While risk analytics and the broad spectrum of stakeholder reporting tend to receive the lion's share of the headlines these days, RDM's role in operational efficiency and trade strategy innovation is grossly underappreciated. These two areas are the offensive components of the arsenal and have a significant bearing on the "data is revenue" mantra.

Virtual centralization of reference data management, without a requirement for physical centralization of the data, would help foster a consistent lifecycle management process around these datasets and would further support completion of the datasets. In short, these status factors feed on one another.

Another thing that can influence both the perception of legacy fragmentation and the decisions that are made about next-generation solutions is infrastructure on demand, or cloud solutions. Next-generation solutions enabled for cloud-like infrastructure is an exceedingly disruptive concept that could influence the economics and intangibles around adoption. This is another arrow in the quiver of operationally embedded alpha that can be seen in numerous infrastructure migrations.

Paul Rowady is a senior analyst with Tabb Group. He has 20 years of capital markets experience, with a background in research, risk management, trading technology, software development, hedge fund operations & derivatives and enterprise data management. Email him at

Comment  | 
Print  | 
More Insights
More Commentary
Could Intel Lose Data Center Market Share to ARM Chips?
ARM chips could be an alternative for certain purposes in the datacenter, but many questions have to be answered before they pose a threat to Intel's market dominance.
Cost to Trade: Hey, Banks, Itís Time to Face the Music
Why is calculating the cost to trade so difficult for banks? The answer is as complex as the calculations themselves.
M&A Activity Will Continue to Grow in 2015
Data shows that the M&A market continues to improve, and forecasts indicate deal making will be healthy in 2015.
Chief Data Officers: Organization Strategy & Cultural Change
Chief data officers are new to the financial services C-suite, but they are facing a number of challenges, including the need for new data governance and execution strategies, staffing, and new organizational structures to enable cultural change.
New York FinTech Innovation Lab Calls for New Entrepreneurial Applicants
Wells Fargo joins 14 other major financial institutions providing mentoring and guidance to the six chosen startups.
Register for Wall Street & Technology Newsletters
White Papers
Current Issue
Wall Street & Technology - Elite 8, October 2014
The in-depth profiles of this year's Elite 8 honorees focus on leadership, talent recruitment, big data, analytics, mobile, and more.
5 Things to Look For Before Accepting Terms & Conditions
5 Things to Look For Before Accepting Terms & Conditions
Is your corporate data at risk? Before uploading sensitive information to cloud services be sure to review these terms.