Exposing the Myths of Data Management- Part 1

Guest Contributors: Phil Lynch, president and chief executive officer and John Mitchell, vice president, Asset Control

Over the past ten years, front-end systems have attracted the lion’s share of IT investment. Low-latency, high-speed automation has been the big-money game. Trading has gone electronic, international, multi-asset and cross-venue. As returns from commoditized long-only investments decrease, firms are looking to more complex trading and investment strategies in the search for higher yields. At the same time, regulatory change is firmly on the agenda and transparency and risk management have become the watch words of the financial markets.

Greater data demand

The amount of data needed by the average firm has exploded on every front. More venues, portfolios, customization, indices and data-dependent asset classes have driven up volumes. Valuations are now needed daily or even intraday – not monthly and quarterly. Balance sheet information, sales reports, regional economic projections and staff track records are becoming as important as fundamental and technical data. Even if the big-name aggregators could provide all that, other new sources would still be essential to gain competitive edge.

Hedge funds have recognized this for years: they regard non-traditional information as a major asset. But what has really changed is the swell of operational complexity in processing these increased volumes. There is much greater demand for real-time understanding of valuations, exposures and risk. Both investors and regulators want more transparency and proof that management has put adequate procedures, controls and risk checks in place – along with robust audit trails, operational oversight, and accurate and timely reporting. Regulatory arbitrage is out of the question; demonstrating a consistent approach, whether to pricing or risk management, is unavoidable.

The problem of volumes & static solutions

In short, firms have to get more data, do more with it, more often, and in a shorter timeframe. It is no longer something that can be avoided, ignored or delegated down the chain of command. A culture of data governance is needed to create robust processes around every aspect of data sourcing, selection and deployment, and to make sure they are adhered to. And just like corporate governance, it needs to go all the way to the top of the organization.

But here’s the first problem: fundamentally, investment systems were never built for this volume of data and complexity. Risk management, trade processing or accounting platforms weren’t built to cope with the daily information onslaught. After a decade of spending on the glamorous front end, the investment focus has to switch back-stage to data management solutions that are needed to power trading, risk, compliance, modeling and accounting platforms.

And here’s the second – and by far the bigger – problem: the one-size-fits-all, static solution that many vendors prescribe will not solve all data-related problems across the enterprise. These are not solutions, they are another problem waiting to happen.

Please check back next week for Part 2- “Exposing the myths of data management: From one size fits all to fit-for-purpose”

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