Data as a Disruption Enabler
A recent survey of buy-side operations professionals demonstrates that as adoption rates of disruptive technologies grows, so too does the urgency for the laggards to take the steps necessary to facilitate transformation
By Steve Taylor, Chief Technology Officer, Eagle Investment Systems
The broader investment community has known for some time that disruption is coming. A recent survey of operations professionals, however, suggests it has already arrived. It may not be as obvious as the upheaval that occurred across the media landscape or even as dire as Amazon’s threat to traditional retail, but based on growing adoption rates of new technologies – from alternative data and robotics process automation (RPA) to artificial intelligence (AI) and machine learning (ML) – it’s evident that many of the most hyped innovations in recent years are now being deployed regularly across the investment landscape.
This sweeping change, however, isn’t necessarily obvious from the outside looking in. That’s because most buy-side operating professionals have a very realistic view as to how disruptive technologies can help their business today. Take robotic process automation or RPA: the technology isn’t necessarily changing the product set so much as it is altering how investment firms manage and perform back-office functions. Certain “swivel chair” responsibilities – such as reconciliations, data cleansing, trade processing or other fund-administration tasks – are increasingly being automated through RPA applications. The impact may not be immediately evident to clients, but the efficiencies gained are certainly material. And this will allow firms to redeploy valuable resources to client-facing functions or directly into their core investment operations.
The study, “Reaping the Benefits of Disruptive Technology,” polled approximately 100 decision makers at global investment firms. Even as the findings point to a pronounced trend in which a growing number organizations are leveraging disruptive technologies, many still face daunting obstacles, such as data hygiene, that stand in the way. This would hint at a future in which the haves and the have nots will soon be divided between those who are able to deploy disruptive technologies and those whose legacy infrastructure creates a distinct disadvantage that could begin to impede not only performance, but also their ability to address evolving client demands in the years ahead.
According to the research, conducted by both Eagle Investment Systems and strategic consulting firm Hegarty Group, a nearly unanimous 99% of respondents said their organization is already using alternative data in some form, with eight out of every ten identifying that it is either a core part of their investment process or a strong adjunct to it. RPA, similarly, has also experienced widespread adoption, although the most ardent users generally reside in operations, where COOs are deploying bots to keep static or minimize the back-office and IT footprints.
While the adoption rates of AI or ML are not yet as high as alternative data or RPA, the survey demonstrated that these technologies are being deployed more broadly to improve research and risk-management functions, create back-office efficiencies, or automate and optimize discrete tasks such as client onboarding or sales efforts. Among those polled, exactly half said their organization is using AI and ML in these or other related functions.
But how firms are currently deploying these technologies only represents a part of the story and, really, only offers a snapshot of a movement that is evolving rapidly in real time. Perhaps more important is what’s preventing those who may be behind the technology curve from keeping pace as adoption grows and new applications for these and other technologies emerge.
Addressing the Obstacles
In many ways, the growing deployment of RPA, AI and ML capabilities reflect business-transformation efforts in recent years that have been largely premised on reducing the complexity and cost of IT and gaining more value from the organization’s data. Nearly a third of respondents also noted that the overriding goal of digital transformation was to either accelerate their ability to deploy new innovations or improve the overall customer experience.
The caveat, however, is that the required groundwork to facilitate a digital transformation can itself be disruptive to the larger organization. Based on the survey, the biggest obstacles that stand in the way of adopting the aforementioned technologies are the current maturity of existing solutions, broader organizational interest, and the current state of data hygiene.
The first two obstacles are certainly related. With limited organizational interest, awareness around how these technologies can help buy-side firms will remain limited. The front office, in an era of shrinking margins, can also be hesitant to take on the costs that accompany large-scale transformations knowing that the payoff may be years down the road.
But the most daunting challenge, given the nature of the advancing technology, is the need for buy-side firms to address current data hygiene. This challenge only becomes more overwhelming the longer organizations sit on their hands. It typically entails the implementation of a centralized, next-generation data platform that can deliver auditable fit-for-purpose data across a global enterprise. A data governance program is also essential, particularly as front-office data needs often differ significantly from what’s required for back-office functions.
From a cultural perspective alone, there needs to be an organizational commitment to regard data as a true business asset. More importantly, data governance provides the foundation to meet the voluminous but strict data demands of AI and ML technologies.
Other considerations will also become paramount. To continually leverage these advances as AI and other new capabilities advance, buy-side firms will likely have to embrace the extensibility of the cloud. In addition to the requisite computing power to manage and process big data, the continual software delivery of cloud-native platforms will drive improved quality and system resiliency, facilitate seamless software upgrades, and allow for faster technology adoption.
Along these same lines, an extensive API framework will provide avenues into multiple technology ecosystems, while service-abstraction design principles allow critical business services to move out of single-data center environments to be absorbed by a set of providers and SaaS capabilities. In other words, the next business transformation may be the last, assuming the architects encourage flexibility and agility above all else.
The transition over time to a composable business paradigm will allow firms to co-create with vendors to tailor new, business-led applications and will enable a plug-and-play accessibility. This accessibility will help to open organizations up to the rapid innovation occurring across an expansive and growing FinTech universe. The range and scale of the opportunity set, however, is offset by the risk of falling so far behind the technology curve that the disadvantages become apparent to those outside the organization — in the form of lower profit margins, deficient client service, inadequate transparency, and, ultimately, underperformance. But once buy-side firms can get their data management and governance houses in order, they’re generally in position to move quickly to embrace disruption as it occurs.
As Chief Technology Officer of Eagle Investment Systems, Steve Taylor drives the software, technology and architecture decisions across Eagle’s investment management suite and the Eagle ACCESSSM private cloud platform.
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