Back to basics: Data management woes continue for the buy side

For all of financial services’ progressiveness with technologies such as as generative AI capabilities and cloud or exploring the realm of quantum computers, there’s one thing they have yet to master: data management.

There are many challenges when it comes to data management: data quality, data timeliness, data lineage, and data completeness, but one of the biggest is the ability to consolidate data from various counterparties.

As a CEO of a Singapore-based fund management firm tells WatersTechnology, when a firm starts to gain some scale, it becomes difficult to consolidate data from multiple counterparties, like fund administrators and custodians.

“It’s hard enough pulling data out from one fund administrator, for example, that sends data in their own format, for client holdings or risk positions, or the regulatory returns we have to do. If it’s from 10 or more different providers, that’s a really difficult job. Not only is it time-consuming, but it increases the chances of error if you’re manually doing that,” they say.

Every new counterparty is a new data source, which the CEO says can sometimes make for the same datasets “cut 16 different ways.” With regulatory obligations on top of that, it can be hard for a buy side to manage internally.

We’re fund managers, we’re not technologists

CEO of buy-side firm

 “Once you start getting bigger, you need some sort of methodology. If you’re doing it manually, it means you’re often a long way behind where the data is—you’re waiting weeks for the data to come in a spreadsheet and then more weeks to consolidate it. All of a sudden, you’re looking at a position that’s two months old,” they say.

Consolidating data manually on spreadsheets may be possible for a boutique firm that has one or two funds. But once the firm increases not only in fund size but also in the number of funds, things can get unwieldy.

“Rather than having to manipulate the data manually, we wanted a solution that was scalable for us,” says the CEO, who also has previous experience working at large asset management firms.

Enter the experts

The buy-side firm has a strict policy against creating in-house systems: “We’re fund managers, we’re not technologists.”

“I’d rather find the right person who focuses on this. We can never give it the same amount of focus that a specialist technology provider could do,” they add.

But even then, it took a while for the firm to find the right provider that could, as the CEO says, grow with it. What it initially found in the market were “big and expensive” systems that were difficult to customize.

The firm’s real challenge was data consolidation, and eventually, it found a solution in Fencore, a data management provider that caters to the investment management industry.

Today, the firm uses Fencore’s DataHub product, a no-code, cloud-native data management platform. “We rely on Fencore to do all the programming work. We only want the output and not to be involved so much in the input. They worked with our fund administrators and other counterparties to get the data into a format that works for us,” the buy-side firm’s CEO says.

James Crosby, founder and CEO of Fencore, explains that the idea behind Fencore is to provide a cloud-native, business-user-friendly data management platform.

Crosby, who has worked at incumbent data management companies like Markit EDM, which was bought by IHS, and later absorbed by S&P, says he saw most of the data management industry dominated by technology that was as much as three decades old. 

“I dreamt of a data management platform that is user-friendly and caters to business users to help them understand the data and to use data,” he says.

Fencore, which has been incorporated for six years and is headquartered in Singapore, provides investment management firms with a no-code data management platform.

Apart from DataHub, Fencore offers FenDQ, a no-code data quality engine, and FenRecon, a no-code reconciliation engine.

Fencore’s no-code philosophy stems from Crosby’s firsthand experience dealing with the incumbent data management platform providers that required users to use SQL scripting or other programming skills within their platform.

“That really opens a can of worms because people end up—while you can try and advise them not to—putting complicated SQL or coding in. Ultimately, users will do whatever they have to do to get it up and running. And that really complicated SQL that they... put in might mean it can be problematic and difficult to maintain implementation of that platform,” he says.

By eliminating that and going full no-code through drag-and-drop style implementation, there’s no way users can mess up the implementation. “It has to stay simple. And by providing a very flexible, nimble configuration interface, they can do all the stuff they need to do but there’s just no way it can go wrong. It’s like guardrails to make sure the implementation stays simple, non-technical, and easy to understand even for future people looking at it as well,” Crosby says.  

He says there are cases where data analysts or data scientists might want to use Python or R, but often that will sit on top of the data analytics layer rather than the enterprise data management or operational data layer.

Fencore services a range of buy-side clients, from boutique managers to large ones. These include asset managers and asset owners, wealth managers and family offices, service providers and fund administrators and alternative investment managers.

It has clients across Singapore and Europe, and it is branching out into the UK market.

Treating the symptoms

Crosby sees a few challenges that the buy side faces today. The first has to do with onboarding new sources of data. “For various reasons, firms might want to bring in a new source of data, for example, sustainability data. Typically, onboarding takes far too long and is too large a project,” he says.

Another challenge is dealing with too many manual processes, which may lead to bad or unknown data quality. “This could be not doing enough [quality] checks on the data. Often, they realize that through some error in a functional system, or a wrong number in a downstream report, which, once traced back, could be due to a missing mandatory field or inconsistency. For example, a fixed-income instrument that’s supposed to have a maturity date, and it’s blank,” he says.

The third challenge is a “bloated” data management team. Crosby points to a case Fencore dealt with in Australia where a firm had five full-time staff, whose job was to manually look at thousands of price exceptions raised by multiple price sources to establish the right prices for the different securities in their security master.

Using a data management platform, that firm could make the process more efficient and move those staff to focus on more value-adding roles rather than dealing with price exceptions daily.

Another issue is the frequency with which data is updated, for example, from a custodian. Crosby says this is typically limited to once per day, or at specific times during the day. But the modern data-centric asset manager requires real-time access to all investment data and an in-house understanding of how it feeds into investment decisions and asset allocation.

The illness

These challenges are symptoms of lacking a central data layer or, particularly for larger buy-sides, an agile central data layer.

Crosby says that onboarding new data sources would take three to five weeks at incumbent platforms, whereas Fencore’s can complete the process in two to three days.

That’s because, at the incumbents, the analyst wanting to onboard a new dataset or vendor would need programming skills and necessary knowledge. “That’s just the nature of those platforms being complicated to configure—over 30 years of technology, it can become unwieldy and difficult to configure, and that translates into long and complicated implementation processes,” he says.

In starting Fencore, he wanted to incorporate 30 years of data management domain expertise into the intelligence and core of the platform, but with a “lightning” focus on minimizing implementation time.

From his experience at multiple incumbent data management providers and working on some long implementation data management projects, Crosby understood the “why” of the lengthy process.

“Why are junior implementation consultants taking a week to do a task that would take an experienced implementation consultants four hours? But the length of time to learn the platform and the complexity of it, the junior implementation consultants who might have just joined a month or so ago, would spend a week doing a small task. And then a senior person would have to unravel it all because it was done wrong with bad SQL, wrong mappings to all sorts of tables. Those are the things I saw firsthand,” he says.

When Crosby started designing the Fencore platform, the central question was how to reduce implementation time. More specifically, how could something that takes days be reduced to an hour?

“It was the shortness of implementations that was 99% of our focus, and that’s why we’ve achieved those results,” he says.

According to the buy-side CEO, since starting to use Fencore’s platform, it has added features that now allow its chief investment officer to look at data from a portfolio perspective regarding positions, risk, and other metrics.

“With any database, the trick is to get the data in there in a format that can be sucked out, rather than spending the bulk of our time getting the data into a structure where we could add, for example, reports on top of it,” the CEO says.

They add that using Fencore’s data platform has been helpful as it has the “layering” of data and how it should be ingested.

“It just makes adding things on later much easier if the data is structured in a way that you can just run those reports. That was expertise we did not have, but James and his team had, which was very helpful to us,” they add.