At Bloomberg, our work begins and ends with data

Data fuels global financial markets, informs important decisions, and is central to everything we do at Bloomberg. As the data landscape grows and evolves, and clients demand more data for their own analysis, the ways in which we harness and evaluate this information for the Bloomberg Terminal and our Enterprise Data products are always evolving.

As a piece of data is gathered, evaluated, enriched and made available to clients, the Data team, comprised of over 1,000 employees in offices around the world, is key to every step of the process. They work as analysts, problem solvers, and innovators. Aside from exhibiting technical and data analytic skills, members of this team become experts in their respective areas of focus, gaining deep knowledge of the landscape and trends. And given the breadth and depth of data made available across all Bloomberg products, there are many opportunities for specialization.

By following a day in the life of Bloomberg data, it’s easy to see the broad reaching impact of this team’s responsibilities.

Sourcing

For a piece of data to exist within the Bloomberg Terminal or delivered via a Bloomberg data feed product, a member of Data needs to gather it from a trusted source and verify it. Whether it’s a company’s earnings, stock dividend, or corporate sustainability rating, all information has its own standards for inclusion and processing through Data.

As Jonathan Gardiner, a member of the Debt Capital Markets Team in London, explains, this information can come from a variety of places. “We could receive our data directly, in the form of term sheets or prospectuses, or we could get it from a news story or press release or from one of the many web sites we regularly scrape for information, searching for key words in the public domain.”

Using all the information already at Bloomberg’s disposal, from company filings to news reports to historical data, members of the Data team can give their findings context and evaluate how best to surface them.

Normalization

Gathering a wide variety of content is only the beginning of the process for Bloomberg. After sourcing the content, Bloomberg’s goal is to provide it to customers as fast as possible in its raw or, “as-reported” format. This allows customers to see the data as fast, if not faster, than if they obtained it themselves. But it’s not enough for clients just to have the raw data. They also want easily compare and contrast this data across many companies.

To make this possible, Data standardizes or “normalizes” the data across the many disparate formats, languages and standards in which they receive it. This is one the primary differentiating factors of Bloomberg – by combining the domain expertise of employees and  utilizing cutting edge technology Bloomberg is able to make this content available so that clients can make apples to apples comparisons and analysis regardless of how companies report the data.

“Every exchange has its own disclosure style,” explains Zhen Hao Toh, who monitors Equity Capital Markets data in Bloomberg’s Singapore office. “In order to standardize the data, we require certain level of market expertise that cannot be replicated by machines. This human attention ultimately allows clients to make meaningful comparisons when looking across different capital markets.” This process also includes validation of raw data by stringent standards, ensuring Bloomberg clients are accessing information that meets important criteria.

For Lauren Cope, who works in the relatively new field of ESG (Environmental, Social, and Governance) data from the New York office, the rules and standards of normalization are sometimes still being written. When evaluating everything from emissions to corporate governance to diversity on boards, “There aren’t a ton of rules,” Cope says. The nature of ESG data allows analysts to make nuanced decisions based on their own expertise, helping to shape how this data is assessed and shared.

“Analysts on our team are always asking questions,” says Cope. “There’s the opportunity to define data fields, create tailored metrics, and prioritize what’s important.” Clients rely on Bloomberg to forge the way with new data sets, given the level of transparency, we can provide, and that they have come to expect.

Harnessing AI technology

As the global financial landscape shifts and changes, Bloomberg clients want trustworthy data, faster than ever before. This increased demand for volume and speed necessitates new tools to automate and simplify larger, more complex data sets.

AI proves especially useful to the Data team in its extraction capabilities. This technology can eliminate repetitive and mundane tasks that were previously time-consuming and susceptible to human error. “In the past, a lot of our team’s time would be spent validating, checking, or manually entering data,” says Gardiner. “Now, we’re able to bring in subject matter experts who are able to analyze and offer additional insights into data, which we can then offer to a client.”

Data evolution

“Depending on where and how and in what format we receive data depends on how we enrich or transform it,” explains Gardiner. “That determines what the end product is going to look like.” The resulting data points, standardized, verified, and enriched by the Data team, are populated into their appropriate fields within the Terminal and the appropriate data products, available for client access and assessment increasingly faster, and, in some cases, almost immediately.

Unsurprisingly, this process is constantly shifting. “With ESG data, we’re constantly responding to subtle and significant changes in the space,” says Cope. Being in touch with ever-evolving data means members of the Data team need to be nimble not only in how they evaluate information, but in a larger sense as well. Analysts often take part in critical decisions, evolve their department’s strategies and practices, and bring changes or irregularities to light.

Working within Data isn’t limited to spreadsheet analysis and writing code. Members of the team have the potential to interact with Bloomberg clients – if a member of the team has interest and takes initiative; there is an array of potential opportunities. As Data’s purview and capabilities change, members of the team are growing and adapting with it.

“The role of a data analyst has evolved with time. It is no longer how good you are at processing data. We have to keep ourselves abreast of market events, especially regulatory changes, which may impact the way we are capturing data, thus, affecting clients’ investment decisions,” says Toh. “We need to be prepared, to make sure our business is able to handle these changes.”

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