Bloomberg’s Data Management group and the Sustainable Finance Data team: Driving high-impact innovation together

The Data department at Bloomberg is dedicated to advancing data management capabilities across the organization. This department plays a crucial role in ensuring data integrity, accessibility, and interoperability, providing the foundation for informed decision-making across Bloomberg’s various business units. By using cutting-edge technology and innovative strategies, the Data department continuously enhances the quality, usability, and efficiency of Bloomberg’s vast data resources. Both the Data Management group and the Sustainable Finance Data team sit within Bloomberg’s larger Data department. Together, they collaborate to drive forward Bloomberg’s data strategy by applying shared principles of quality, discoverability, and user-centered design.

Under the leadership of Marvin Ward Jr., Head of the Data Management group, the team is composed of individuals who are experts in their technical fields and highly attuned to the needs of Bloomberg’s customers. This dual expertise enables them to tailor Bloomberg’s data production process to improve relevance and availability for the user and enhance decision making.

Marvin explains the transformative nature of the work: “We’re talking about having the business operate in new and non-traditional ways. Much of the work that we’re doing in the Data department is about accelerating innovation by working with folks in specific domain teams and helping them expand their investments in data quality, data modeling and business intelligence … all for the purpose of creating a more easily maintained data product that can be accessed more efficiently by our customers in a way that is consistent with their use cases.”

Committed to ‘ready-to-use-data for all’

The Data department approach emphasizes “ready-to-use data for all,” following a set of core principles that are deeply embedded in the daily operations and strategic initiatives of the group.

• Discoverability is fundamentally about making it easy and efficient for users to identify relevant data for their unique decision-making needs. This involves sophisticated systems that facilitate straightforward data retrieval and guide users to the most pertinent information.
• Accessibility ensures that data can be integrated into varied user workflows. This means that regardless of the technical acumen of the end-user, the data provided by Bloomberg is ready to be deployed in decision-making processes without extensive wrangling or interpretation.
• Appraisability refers to the rigorous processes used to evaluate data’s fitness for use in client scenarios. This involves a detailed assessment of data quality, looking at factors like accuracy, timeliness, and completeness — attributes critical when using data for informed decision making in the financial markets. 
• Interoperability focuses on the integration of datasets and the exchange of information between them. The group’s efforts ensure that different datasets can communicate and work together, uncovering and maximizing linkages that drive deeper insights and more robust data-driven strategies, while also reducing the time it takes to make the data usable.
• Analysis-ready data is integral to Bloomberg’s customers, where the emphasis isn’t just on the availability of data but on its readiness to be directly applied in analytical processes without additional preparation. This approach saves valuable time and resources for customers, enabling them to make quicker, more informed decisions.

The Data Management Group in action: Improving the quality of Sustainable Finance data

One recent collaboration between the Companies department and the Sustainable Finance Data team—both part of Bloomberg’s broader Data department—offers a clear example of how cross-functional efforts within the Data Management group lead to improved quality in company-reported Sustainable Finance data.

Understand Sustainable Finance data’s unique challenges

Sustainable Finance investment strategies must account for a constant influx of regulation, new disclosure requirements, and other variables, all of which need to be accurately measured and presented through high-quality data. 

That meant that the first step in the Sustainable Finance project was to establish what “quality” meant for Sustainable Finance data. This involved identifying key clients and use cases and defining requirements for accuracy, timeliness and coverage. The team developed these quality metrics, classifying them into categories: essential, sufficient and best-in-class. This rigorous process allowed for the data product to meet the highest standards of reliability and usability.

“We had to frame out what we thought was required versus nice to have along all these different dimensions,” Marvin explains. “The team then figured out how to collect these metrics regularly and set up a roadmap for continuous improvement.”

For example, active asset managers use Bloomberg Sustainable Finance data to identify opportunities, circumvent risks, conduct fundamental research and to devise and deliver sustainable investments. To conduct this analysis and prove how their investments meet stated sustainability mandates, asset managers require quantifiably transparent ESG data that is recent, accurate and complete. 

Overcoming obstacles through collaboration

One of the biggest challenges the team faced was ensuring data observability and access. The existing infrastructure needed to fully support the level of observability needed, prompting the Data Management group and Sustainable Finance Data teams to collaborate with engineering counterparts. 

Rosy Bitar, Global Head of Equities Data at Bloomberg—which includes the Sustainable Finance Data team—noted, “The teams didn’t let anything get them stuck. They understood what we needed to do from a long-term perspective and found short-term solutions to keep moving forward.”

Designing a cross-functional blueprint for the future

Implementing the assessment approach became a catalyst for change, breaking down silos and fostering unprecedented levels of cross-functional teams. This project created a blueprint for future data management initiatives, demonstrating the immense value of marrying technical prowess with deep domain knowledge.

Through this project, teams streamlined workflows, reduced redundancies and improved overall data quality, proving that a well-aligned strategy can yield exceptional results. Collaboration between the Data Management group and Sustainable Finance Data teams highlighted the importance of cross-functional partnerships, ultimately leading to more robust and reliable data products that better meet customer needs.

Join the Data team

Working on Bloomberg’s Data team offers a unique and rewarding career path for data professionals. Positions within the Data team demand a combination of technical expertise, domain knowledge and a passion for innovation. 

Interested in learning more about open roles within Bloomberg Data? Explore opportunities.