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Data-as-a-service: Enabling a radical shift in market and reference data management

Traditional approaches to market and reference data management can be expensive to implement and run, which means more firms are now looking at alternative ways to handle data. At the same time, clients want to avoid having to re-invent the wheel and would like to follow standard practices – which is where Data-as-a-Service comes into play.
Data-as-a-service: Enabling a radical shift in market and reference data management

As part of the ongoing search for higher returns, investment professionals are now looking for services that enable greater automation of routine and/or highly technical work, by outsourcing specialist expertise for knowledge that isn’t unique to the business but is critical to success. According to SimCorp, Data-as-a-Service (DaaS) allows clients to leverage the benefits brought by quantities of scale - allowing clients to have affordable access to a high degree of expertise that functions like an extension to their inhouse team, who can focus on differentiated value creation enabled by the latest in data science. In a recent webinar held by AsianInvestor, in partnership with SimCorp, industry veterans compared notes on best practices in handling market and reference data.

Maximizing value through streamlined processes

For many, the digital journey is less about cost, and more about optimising an operating model –by outsourcing certain standard activities while retaining responsibility for services that are differentiating, and strategically upskilling an in-house workforce to allow the firm to keep up to speed of change as required by the front office. “The question (for us) was to create the capacity in the team in order to build new capabilities,” said Ruchir Verma, Head of Global Services, Investment Management at Zurich. “Here we found a solution by offloading repetitive tasks with no competitive advantages to create that capacity. And without compromising on any integrity, any control, any sort of downstream value add that we have in our company.”

EnHao Chua, Head of Digitalisation and Data for UOB Asset Management (UOBAM), says that the agility brought by a service-based approach can allow firms to adapt and pivot more quickly to changes in market practices and customer requirements. “ESG, for example, is still in flux; this is not something that is cast in stone yet, and (can) be able to provide the typical IT requirements,” he says.

As the one responsible for driving digital transformation, adoption of emerging technologies and implementation of core digital capabilities for the firm, Chua has seen an accelerating need for adaptability. “We need to be able to adjust and fine-tune strategies as we move forward; so a service-based approach is more robust compared to an EDM solution.”

Capital markets fintech specialist Virginie O'Shea also sees limitations to the traditional data management model. The founder of Firebrand Research, she has spent the last two decades tracking financial technology developments in the sector. “I see a lot of firms falling way behind on their data management upgrade path; that has a huge impact on keeping pace with global market requirements,” says O’Shea, noting one of the biggest failings of today’s firms is an over-dependence on technology that becomes quickly outdated. “Change is hard to keep pace with – we are constantly playing catch-up.”

Empowering specialisation through collaboration

The right solution for today’s data professionals can’t be static, according to Josef Sommeregger, Vice President at SimCorp – rather, it needs to follow an agile methodology that makes use of an ongoing feedback loop which is responsive and adaptable, yet based on certain core standard elements, which can accommodate client-specific requests. “We believe standards absolutely need to be at the core,” he says, “but we don’t believe in the one-size fits all approach.”

At Zurich, setting up the right success criteria for the solution freed up resources to allow their people to get deeper into data analytics. Leaving the formatting and collection of data to DaaS, which freed up resources for downstream activities. “The main concern for us was opportunity cost – and this is where we’ve seen a lot of positive impacts,” says Verma.

Verma says that firms should look for a DaaS provider which not only offers expertise, but can help enable a shift in mindset - moving towards managing an outcome, rather than managing a process. “We always approach it as a collaboration; we’re not just looking for a service provider or vendor; we need someone we can work closely with.”

“Just like a marriage, it has to be built on common goals, and trust”, says O’Shea. “In the context of this, it’s a good service-level agreement that you can fall back on, with roles and responsibilities laid out.” Key points to consider when choosing a data partner, she says, are operational resilience, transparency and explainability for regulators to see, and having the sufficient expertise on hand to support your business over the long-term, while not over-promising and under-delivering.

When choosing a solution, it is important to understand current costs, what a new structure and onboarding timeline would look like, and the level of comfort the business has with letting go of control of data management processes. In considering obstacles to implementation, firms should also look beyond mere technical and logistical challenges. “People and culture are important – there’s still a human element to consider,” says Chua. “You need to have (stakeholder) buy-in and management support, to be able to bring them on the journey.”



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