Opening Cross: Convergence Part 2, and Why It’s Crucial in an Age of Data Divergence

Divergence of datasets and data types will inevitably create convergence of data management functions.


When I sat down to write last week’s column on the topic of convergence, I had no idea that it would be even more timely than I first imagined. Last week, I discussed how the evolution of the role of chief data officer within many financial firms is forcing previously separate market data and reference data functions into a common organization. About time, some readers responded, though the reasons for separating the two a decade or so ago were certainly sound at the time.

But the fact is, the industry has evolved, and so have trading styles and the regulatory environment in which traders operate. As a result, the demands placed on data—for trading, valuations, and reporting have changed, as has the essence of what constitutes market data. So, inevitably, the way we manage data must also change.

So perhaps it shouldn’t be surprising to see more examples of data convergence this week, too. For example, enterprise data management software platform vendor GoldenSource has fully integrated its market data management module with its core suite of EDM capabilities. According to the vendor, this will not only help centralize overall data management, but will also make it easier to add coverage of new datasets, and to manage a firm’s response to regulatory requirements—such as the Fundamental Review of the Trading Book (FRTB) proposals from the Bank for International Settlements’ Basel Committee on Banking Supervision, which will take effect in 2019, which GoldenSource managing director of sales and client operations Neill Vanlint says “will change forever the way that risk and finance manage data”—centrally, where those regulatory demands require access to market data and reference data.

In a different kind of convergence, pan-European exchange Euronext is bringing together derivatives and index data in its hitherto equities-only historical data product, NextHistory. Officials say it made sense to include all markets that the exchange trades within the product, especially since clients had been asking for historical derivatives data since the exchange diverged from Intercontinental Exchange, leaving its former Liffe derivatives business under ICE’s ownership. In addition, Euronext is converging the way in which clients can access the data by making it available via the same connections they might use to access corporate actions information and other types of reference data, for example.

Similarly, Moody’s Analytics—the data and software stablemate of credit ratings agency Moody’s Investors Service—is converging content from its key databases of private company loans information into a single product, dubbed RiskBench, that provides dashboard views of private companies’ credit health and likelihood of default or loss.

Taking the opposite approach is startup data and analytics as-a-service provider Intrinio, which is unbundling content and making its proprietary and third-party datasets available separately via a new Fintech Marketplace of purchasable content, allowing users to be the masters of how converged they want their data to be—i.e., allowing them to choose only the datasets they need, rather than a bundle of things that they mostly don’t want.

Meanwhile, traders thrive on divergence between the data itself. Hence, crowd-sourced earnings estimates provider Estimize is releasing a factor model that makes it easier for traders at hedge funds to spot where alpha exists in the divergence between the consensus of Wall Street earnings estimates and the crowd-sourced “consensus” of analysts and investors, and is expanding the range of crowd-sourced economic indicators that it provides: because divergence is where traders find opportunities.

As we see greater divergence of datasets themselves as new types of data evolve, and others are separated from one another for practical and budgetary purposes, it will become even more important that the management functions that govern that data must do the opposite, and converge in order to manage this ever-broadening array of data assets.

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