Unleashing the power of data and technology in an age of digital transformation

Bloomberg is a household name in the global capital markets, with a long history of providing high‑quality data and technologies for the front, middle and back offices in buy-side and sell-side firms. SC Yau explains what the firm’s strategy means in practice and how it is helping its Asia-Pacific (Apac) clients’ digital transformation journeys across a number of key areas, including system interoperability, enhancing decision-making abilities and increasing automation across the business.

Digital transformation projects are nothing new in the capital markets, with buy-side and sell-side firms aiming to optimize their technology and data stacks to transform downstream business processes. However, what exactly constitutes a digital transformation project differs from firm to firm, and tends to be based on a number of variables, especially where firms are positioned in their respective journeys.

SC Yau, head of BQuant enterprise sales at Bloomberg, explains that the firm sees digital transformation as making it as easy as possible for its clients to consume and manage their data, so it can be disseminated across the enterprise.

“For us, digital transformation means making information consistently available so it can be used easily,” Yau explains. “Additionally, we are exploring ways to automate this process to facilitate easier distribution and retrieval of information by users. Essentially, digital transformation is the formalized term for the standardization and normalization of digital information across the business so it can be shared, stored and surfaced to all downstream systems from the front to the middle and the back office.”

Data, system and application interoperability in an Apac context

One of the key benefits for firms undertaking digital transformation is how correctly stored and formatted data enables system and application interoperability. This challenge has been part of the financial services industry for decades, and one that most firms have been forced to address at some point in their history, regardless of their location, size or level of sophistication.

“Apac consists of many different markets, and they’re all in different phases of the digitization process,” Yau explains. “The Apac marketplace is very diverse; some regions still exchange a lot of data via voice chats or instant messaging, and some still need to go through electronification. At the same time, data quality is a big thing, so when it comes to interoperability, one of the key challenges we encounter is data inconsistency. For example, what is the communication protocol and what are the related compliance issues around how data is stored within the organization? How do the different systems link together and talk to one another? Those are always a challenge.”

A key Bloomberg focus is making sure its clients can access the information they require, which hinges on data consistency and quality. This ensures that, regardless of the source, insightful data is delivered in a consistent format and user interface that firms can trust because of its quality, and that it is timely and accurate.

Enhancing investment decisions with insightful data

One of the key challenges facing buy-side and sell-side firms across the Apac region—and indeed other regions—is how they can use the data available to them to make the most judicious and transparent business, investment and trading decisions. It’s an industry challenge Bloomberg is intimately familiar with.

Data volumes, speed and complexity have increased significantly over the past decade, which has become a double-edged sword for financial services firms. On the one hand, it provides them with more “intelligence” and granularity to support their decision-making while, on the other, it adds significant noise and scale, which increases complexity.

“In the space of sustainable finance, a lot of that data didn’t exist before,” Yau continues. “But now we offer customers carbon emissions, climate risk, biodiversity and other kinds of data people didn’t consider before. The availability of data is almost unlimited. But the questions our clients always ask are: what’s the use of it, and how do we consume it?”

Growing data volumes and complexity

Linking data to the right security or entity is an enormous challenge and one of the top issues for customers grappling with growing volumes of complex datasets, particularly with data in the sustainable finance space. To this end, Bloomberg leans on its long history of excellence in reference data and other foundational datasets, as well as sustainability data, linking and normalizing it to create high-quality interconnected data solutions. This enables customers to get more value and faster insights from their data.

The firm also looks to break down language barriers that exist across the Apac region, ensuring that insights provided for decision support are available in multiple languages. “We provide artificial intelligence-enhanced tools for users to really look into the data,” Yau explains. “For example, we have been using generative AI technology to summarize information from earnings calls, so people can quickly and easily gain insights. They can look into the data regardless of whether it’s structured, non-structured, qualitative or quantitative. That’s the key,” he says.

Empowering a high-performance workforce via automation

Automation, especially across the front, middle and back offices, is another area of focus for Bloomberg and the buy-side and sell-side firms it partners with across Apac, as they look to optimize the various business processes and workflows that have traditionally been manually intensive. In this context, automation is less about introducing a “big bang” and more about identifying the specific business processes that would be more beneficial if they were automated.

It is an iterative undertaking designed to enhance processes, workflows and productivity by assigning machines to tasks humans had undertaken and managed previously. “We have a practical understanding of what automation means across the industry,” Yau explains.

Bloomberg’s strategy centers on automating firms’ information flows and empowering them with the tools that allow sales personnel to have more effective conversations with their clients. One such example is Instant Bloomberg, or IB, the instant messaging function within the Bloomberg Terminal, which the firm has been enhancing by offering users additional solutions to gain actionable insights. For example, users used to have to copy and paste tickers into the Terminal. Now, Bloomberg uses natural language processing (NLP) to recognize tickers and display the relevant information.

“Our goal is to find the right information, provide the right tools and empower users,” says Yau.

What’s on Bloomberg’s radar?

Bloomberg’s experts speak to large numbers of firms across the region about their respective data and technology pain points. Unsurprisingly, they largely mirror the issues facing firms in the North American and European capital markets. According to Yau, one of the most acute challenges right now relates to the explosion of data across the industry and how firms determine the value of the data available to them. On top of that, there are ongoing data management concerns associated with large volumes of fast-moving and complex data produced by an ever-growing number of sources.

The other perennial challenge relates to the build versus buy conundrum, which has been prevalent in the industry for decades. The technology might have changed over the years, but the challenge for firms about whether to build or buy it is still pertinent.

“When it comes to the evolution of technology, people talk about cloud, open source, machine learning, NLP and other buzzwords, but the really difficult decisions people have to make are around whether to build or buy,” says Yau.

Buy versus build or both?

A decade ago, the default position for large numbers of firms might have been the build option. However, the consensus today is increasingly that firms cannot make a solid business case for developing technology in-house unless it has the ability to deliver a competitive advantage, and they can build it at least as well as or better than a third-party provider.

The managed service model has further eroded the potential value of the build proposition to the extent that few financial services firms can now legitimately justify the time, expense and resources needed to manage internal projects.

“We provide hybrid integration-friendly technology platforms and solutions that allow clients to tackle their business problems without needing domain expertise,” Yau explains. “What we do is sit down with them and talk about their data and technology stacks and maybe also about the competitive advantage they get from their own internally developed technology, and how we can work together.”

In other words, Yau proposes something of a middle way, where firms’ proprietary technology sits alongside Bloomberg’s offerings. Interoperability is a prerequisite for this to be a practical solution.

Consistent, standardized data

Yau attests that Bloomberg’s data strategy focuses on providing high-quality data that is consistent, standardized and cleaned so clients can use it with minimum data wrangling. The firm also focuses on the delivery of that data via application programming interfaces and accessibility in the cloud, which crucially supports downstream interoperability.

Finally, it allows users to “test-drive” data subsets in a secure sandbox environment to assess their value to the business before they commit to buying them. “They need to know whether it’s useful or not before deciding whether to purchase the full enterprise dataset,” he says.

To that end, Bloomberg allows users to assess a data sample where they can see how it works. The company’s next-
generation programmatic analytics solution, Bloomberg Lab, allows users to build, test and share research and models using a variety of data sources. Users can access and analyze Bloomberg’s high-quality data in a secure Python-based environment to turn ideas into strategies at speed.

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