Anthony Malakian: Getting Schooled in Data Quality

The coolest thing about this job is that I get to learn new things every day. Prior to joining the world of financial technology, I was a sports journalist. I don’t feel that I’m blowing my own horn when I say that I am a certified genius and that my opinions are infallible when it comes to sports—you know, just like every other sports fan alive.

But I have to do my homework to do this job. And the best part about that is that I get to have true geniuses in the field school me. Some people scoff when they hear that I left the world of sports for this. What they don’t understand is that it’s far more stimulating for me to interview very bright people and take away a new perspective.

For instance, this month I wrote a feature on data quality (see page 18). While I do miss chatting with professional athletes, talking to C-level execs from RBS, Fannie Mae and Rohatyn Group requires that I bring my A-game every time, lest I sound stupid—which has probably happened more times than I’d like to acknowledge.

I’m two-and-a-half years into my career covering Wall Street and there are many things that I feel I have a strong grasp of—and data quality management was not one of those things, as we tend to leave that to the pros over on Inside Market Data and Inside Reference Data. But after having spoken with some truly intelligent people—and not just from the banks and hedge funds, but from the vendor and analyst communities—I have some thoughts on why data quality remains an elusive end-state.

This is truly an issue that starts at the top. As Rohatyn Group COO Lee Bocker told me, “When technology is developed and imposed upon business it doesn’t work—it’s destined to fail.”

Something like data quality management isn’t a quick fix; it’s a long—at times painful—process that requires constant investment and manpower.

Furthermore, the non-IT C-level decision-makers have to follow through and keep on monitoring the situation to keep the ship on course. It is all too tempting to want to throw a project at IT and say, “Fix it,” but without a clearly stated direction, the project will fail. And something like data quality management isn’t a quick fix; it’s a long—at times painful—process that requires constant investment and manpower.

These aren’t my words; these are the words of many men and women who are far smarter than I am.

Standardization is another important issue affecting this industry, but I keep hearing contrasting views on this point. The industry wants standardization—but what one wants and what one is willing to actually fight for can often be at odds. I want an Apple iPad—it would be good for me both professionally and personally—but I am not willing to patiently save for such an expense.

The Legal Entity Identifier (LEI) is a great push that will help. And as Scott Marcar, RBS’ head of risk and finance technology, notes, rules around central counterparty clearing will help to improve the system as a whole, as well. But even Marcar says that we’re “a million, million miles away” from the industry adopting a common data architecture and lingua franca that can be adopted across the industry.

Efforts toward standardization have been excruciatingly slow, despite the light that was shone on the issue after the financial collapse that took hold in 2007 and 2008. Firms seem to know this is a problem, but are still struggling with how to address it.

According to a recent SimCorp survey, which polled nearly 100 buy-side firms, many are still struggling to improve their data systems. The survey found that 40 percent did not have confidence that the data they receive from disparate systems is “consistent and of high quality.”

The upside of this is that two-thirds of respondents said there was a “significant effort” under way to “reconcile data between disparate systems and sources.” But this is tempered somewhat by the fact that one-third called efforts at their firms “minimal.”

The scary thing about 33 percent of respondents answering “No” is this: I fully believe that firms that are truly able to efficiently reconcile their data probably answered “Yes” to that question; it’s either the laggards or arrogant or delusional ones that likely answered “No.”