10 years ago, the events surrounding the collapse of Lehman Brothers brought the world’s financial system to a halt. The crisis exposed operational inefficiencies and a disconnect between data and asset managers making critical decisions. 10 years later, the asset management industry is still working on a solution.

I, like most who worked through the difficult Lehman crisis period in 2008, have paid close attention to the many recently published 10th anniversary pieces. That period left a lasting impression on many of us who thought “I never want to go through that again.”

Reflecting on the coverage I’ve seen, I’m struck by the focus on central bank actions and macroeconomic policy decisions. Very little, unfortunately, has been said about the lessons learned and responses by institutional players on the buy-side of the market.

Where we were

On that day back in September 2008, when the news of the Lehman collapse broke, good custodians and administrators raced into their offices to run reports on holdings by issuer, valuations, cash balances, and open and failing trades. Asset managers wanted to understand their exposure to Lehman and were left to compile that information from each of their external providers and piece it together with disparate data sets gleaned from their own network of systems. In the early days of the crisis, asset managers manually compiled spreadsheets or reports and used them to close out what exposures they could. Later, they needed those same ad-hoc reports, and much more, to value their remaining holdings, create client and fund board reporting, and complete regulatory and bankruptcy filings. Building and maintaining a consistent and integrated view of all that information was an issue that continued to sting the industry for years.

As the impact of the crisis wound down, with their heads back above water, many asset managers began to invest in their operating models, technology platforms, and precursor data strategies (“information systems” is probably a more accurate term) to better manage major market events in the future. Since this commitment to modernization, managers and their providers have made significant investments and advancements. Regulations helped, too: The industry led some of the progress by imposing new collateralization rules, the use of Legal Entity Identifiers (LEI), and new requirements for price transparency. New vended technology offerings, tools, and infrastructure have also moved the needle in a positive direction. 

After all this progress, though, have managers solved for bringing internal and external data together in an integrated manner to sufficiently support their front and middle offices? Would asset managers manage a similar crisis any better today? Most would agree they’ve made a lot of changes for the better, but in reality, it’s still a work in progress.

Competing Priorities Have Altered the Course

If asset managers have spent the last ten years installing new front office technology tools, building out their investment operations platforms, and creating new data infrastructure, how can it be that the challenges presented by the Lehman crisis may still exist?

It’s about priorities. As firms moved down the modernization path, other challenges have emerged along the way. It’s not that managers have chosen the wrong things to focus on – evolving regulations, the need to lower costs in response to fee compression, and developing new investment capabilities in response to competition have been critical, but they have added twists and turns in the path forward. There is no doubt that operating models have evolved for the better since 2008, but asset managers are trying to solve so many different challenges at the same time, it’s tough to stay focused.

Some solutions fix one problem but create a new gap in integrated data somewhere else. Managers have installed new tools to help organize their data, but in many cases, are still operating their old data platforms because of legacy processes. Since the crisis, the end game was always to build a single, integrated platform where all data resides in a well-governed way. The perverse reality is that new technologies and other investments have often compounded the original problem of fragmented data.

Building a Data Strategy for the Future

Before asset managers put the 10th anniversary of the Lehman crisis in their rearview mirror, most would be wise to revisit their long-term strategy. Ultimately, better managing the next crisis will be about getting data right. Here are some key considerations for building a data strategy of the future:

Set the foundation. A building is only as strong as its foundation. The same can be said for data architecture. For asset managers, that means creating an integrated operating model where processing and data storage takes place within scalable applications. Work done offline, in spreadsheets or external systems, will be difficult to integrate back into a data strategy. Timely processing and real-time access to data are critical. If an asset manager is going to service provider websites for core information, working off reports that come via email, or creating data in spreadsheets, they should review their operating model. Managers need to be able to trust their data model to solve diverse problems as they arise. Finally, managers need to think about security, control, and lineage of their data.

Standardize. Since data comes from a variety of external sources, it’s important for asset managers to set and communicate standards to their providers. Without standard definitions, every source can be unique and require manipulation or reconciliation. Even seemingly simple data, like positions or cash, can have major variations like traded vs settled, contractual vs actual, pledged vs collateral, etc. A good data governance model is paramount to ensuring data is consistent and valid.

Connect the data dots. Know where and how the front office consumes data for trading, risk management, performance measurement, client reporting, and other critical functions. It’s important to rationalize data sources and their uses. Think about enterprise information holistically – if a manager solves for specific problems in silos, they could inadvertently introduce new problems. For example, if an asset manager takes positions data directly from their administrators into some applications and take positions data from their order management system in others, they’ll end up with discrepancies.

Consider flexibility. The landscape for technology and service providers seems to be settling into two camps: those seeking standardized and highly scalable closed architecture platforms and those offering modular, more flexible solutions. There are benefits and challenges to both approaches. If managers want to maintain control or have differentiating elements embedded in their operating model, they should think modular. Choosing a standardized approach could lead to duplicative capabilities and fragmented data as their teams would still need to run separate, parallel processes to account for the unique instances or exceptions in their business.

Focus on the long-term. New critical, short-term priorities will always arise and, of course, asset managers will have to respond and adapt. This may well result in making trade-offs like decentralized data stores, off-line processes, operating partially redundant applications, or supporting the needs of discrete front office activities with unique workflows. When that happens, it’s important to consciously recognize those occasions and catalogue them. While it’s best to work from a simplified data platform, contemporary technology can integrate data held externally if the relationships are properly mapped and governed.

Building an integrated data strategy on top of a scalable operating model is critical for asset managers to manage the next financial crisis and all the other challenges that will arise along the way. 

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