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9 Things to Know About How Technology and Data Help Your Investment Decisions
The challenge of keeping up is real, but so are the solutions.
Investment decision makers are voracious consumers of data – and today they consume it at greater speed and volume, and with greater efficiency, than ever before. But the momentum of technological advancements behind the improved use of data is unceasing. New tools and capabilities seem to constantly become available, helping to level the playing field between the largest and smallest firms and institutions.
In many cases, asset owners and allocators look to what might be called strategic data partners such as Northern Trust, that have the necessary combination of resources and expertise to help clients better capture their data and make use of it to drive performance. In a recent interview with Institutional Investor, Barb O’Malley, Senior Vice President, Head of Client Solutions Consulting, Northern Trust, shared her insights on what asset owners and managers should keep in mind as they try to keep up in the Age of Data.
Don’t ignore quick wins. There are still benefits to be gained from tools that have been around for a while, such as robotic process automation (RPA). Recently, through RPA, Northern Trust has been able to reduce by 2 million the false positives for one of its reconciliation processes. In other words, when it comes to leveraging technology, it isn’t always about the latest and greatest. “Don’t stop doing what you’re already doing,” say O’Malley.
Don’t act small. O’Malley says she often encounters the belief on the part of smaller shops that they can’t afford to keep up with big, enterprise-level technology. “But today there are more tools available that are targeted to smaller and mid-tier clients, particularly in the data management space,” she says. “It’s worth keeping up with what’s available, even if you begin your search on the internet – there are definitely strong capabilities out there for smaller shops.”
You don’t need massive infrastructure. If you’re an asset manager who has been unwilling to embrace the cloud, you may be leaving a lot of computing power on the table. “A huge amount of infrastructure isn’t necessary,” says O’Malley. “We’re seeing more desktop-type tools that are quite powerful. There are some capabilities that asset managers and owners may be able to access in the cloud that weren’t available to them even a few years ago.”
You’re not alone if you’re struggling to keep up. Asset owners are increasingly moving asset allocation responsibilities in-house, and in the process are discovering they either don’t have the necessary skill sets on their teams, or the appropriate technology – or lack both. For asset managers, the challenge centers around identifying a more robust data management solution. “Data analytics has become more important to an asset manager than perhaps they at first realized – or maybe even realize now,” says O’Malley. “And some of them are struggling because they built somewhat inflexible solutions based on the choices of vendors that they had at the time.”
According to O’Malley, this can lead to a “blindered” view of what insights emerge from data for asset owners and managers. “There are two coexisting views on technology – what’s needed to run the day-to-day business; and what’s needed to gain more insights into data around client behavior, various securities, and portfolio management,” says O’Malley. “Many of our clients feel as if these two needs fundamentally conflict – but they don’t. What does happen, however, is that they often limit their data consumption to only answer one set of questions – which makes it hard to gain a more holistic view. It doesn’t have to be that way.”
Team data scientists with the investment pros. With very few exceptions, investment professionals are not data scientists. But pairing up investment and data experts can be a game changer. And even if hiring a data scientist isn’t in the budget for some investment shops, their knowledge can be accessed through service and technology partners such as Northern Trust.
“Data scientists are really good at looking at the data and not being impaired by preconceived notions of what that data is going to say,” says O’Malley. “They can sometimes help surface insights that the investment professional hasn’t seen. And then the investment expert helps the data scientist either interpret the results or tweak them. When you take a pure data scientist and pair them with someone with strong investment acumen, you tend to get the best possible outcome.”
Powerful tools can help create trust. Perhaps the main driver of the need for transparency in investing has been asset owners becoming more involved in allocation. Certainly, in cases where those capabilities are brought in-house by an institution, there is a desire to see what’s happening anywhere in the process at any given moment. That demand is driving innovation around dashboarding at Northern Trust.
“Our clients want to understand our success in meeting data deliverables,” says O’Malley. “We will start to roll out a number of dashboards that track our success in meeting their expectations. We’ll also provide KPIs that will show trends over time and how we’re performing day-to-day – how we’re settling trades, how the cash is being processed, and so on. For example, they might see there is a trend with the timing of third-party data. With all the relevant information easily at hand, we can discuss with our client the component of the trading process that may not be working – and that kind of collaboration, plus the metrics to back up that we are delivering as promised, helps create trust.”
Small can be mighty, too. O’Malley and her colleagues have been creating tremendous efficiencies that benefit clients through the development of microservices. What exactly is a microservice? “Think of it as a Lego block,” says O’Malley. “You build little pieces of capability then assemble them in different ways to solve different problems. A good example is a transaction approval service. There are many different applications that initiate transactions. So, if we create a common transaction approval service that can be called on by any one of those applications, you don’t have to embed those approvals in every single application. As a result, any change to the approval service can be done in one place and all those applications will benefit. Essentially, it’s a new way of approaching application development.”
There’s a difference between real time and right time. Real time access to endless data flows isn’t necessarily a benefit to investment decision makers if the data and analytics provider doesn’t know the purpose for which the date is being used to optimize its delivery.
“We’ve seen this with clients globally,” says O’Malley. “For example, we’ve had collaborations where we were updating data in real time, but as it traveled across regions those on the receiving end were never sure when the data was ready for them to consume. This led to some paralysis by analysis because our clients thought the data might get better – should they wait? At some point you have to consume it and do something with it, so we put processes in place so they would know when the data was safe to consume. You really have to understand what the data is being used for to set the right level of real-time transparency – it could be 15 minutes, it could be sub-second, or anywhere in between.”
Data management can always be better. Asset owners and managers will continue to be faced with data management and analysis challenges and could be overwhelmed by the burden of needing the right data, with the right controls and quality checks, at the right time – not to mention the right technology itself. A likely next step is the emergence of sophisticated data-as-a-service offerings that are tailored to asset owners’ and managers’ specific needs and use cases, and which allow investment professionals and firms to focus more on their core responsibilities. “The goal would be for the client to know they are consuming a trustworthy and complete data set,” says O’Malley, “in effect lifting most of the onus of the heavy blocking and tackling of data management and analysis from firms and their people – with effective checks in place, and ongoing conversation to identify changes and new needs.”
As seen in Institutional Investor, February 2020.