Coverage, Timeliness and Quality of Data the Key Challenge for Quants, Research Analysts and Data Scientists, according to Bloomberg Research Survey

Findings also indicate a preference for moving to the cloud for historical data management needs

NEW YORK, Jan. 14, 2025 /PRNewswire/ -- The adoption of quantitative and Artificial Intelligence (AI)/Machine Learning (ML) techniques, and the growth of systematic strategies have made investment research data especially important for firms seeking alpha. With these strategies on the rise, Bloomberg polled over 150 quants, research analysts and data scientists in a survey conducted during a global series of client workshops to understand key trends and challenges in investment research.

Data coverage, timeliness, and quality issues with historical data was cited as the top challenge in the industry, with nearly two-fifths (37%) of respondents selecting this option. This was followed by normalizing and wrangling data from multiple data providers (26%), and identifying which datasets to evaluate and research (15%).

In line with these challenges, Bloomberg's survey found that 72% of respondents could evaluate only three or fewer datasets at a time, despite the need from quants and research teams to continually harness more alpha-generating data in today's data deluge. The findings also show that the typical time it takes to evaluate a single dataset is one month or longer for more than half of respondents (65%).

Firms are still trying to figure out their optimal strategy for managing research data in the face of the aforementioned hurdles. 50% of respondents reported they currently manage the data centrally with proprietary solutions versus outsourcing to third party providers (8%), with more than six in ten (62%) of respondents preferring their research data to be made available in the cloud. Notably, 35% of respondents also would like their data to be made available via more traditional access methods such as REST API, On premise and SFTP, indicating they prefer flexibility in the choice of data delivery channels.

"From in-depth conversations with our research clients, it's clear there is a desire for new orthogonal datasets as well as a need to harness 'AI-ready' data. The journey from data sourcing to extracting alpha is difficult and the continuous ingestion, cleaning, modeling and testing of data is particularly challenging," said Angana Jacob, Global Head of Research Data, Bloomberg Enterprise Data. "That's why Bloomberg is committed to building out our multi-asset Investment Research Data product suite, targeted at quantitative and quantamental research, systematic strategies and AI workflows. Our datasets with modeled Python API access enable customers to reduce their time to alpha through deep granularity, point-in-time history, broad coverage and interoperability with traditional reference and pricing data."

The newest additions to Bloomberg's Investment Research Data suite include the following Company Research Data products:

• Industry Specific Company KPIs and Estimates: Bloomberg's Industry Specific Company KPIs and Estimates product provides point-in-time data on over 1,200 unique key performance indicators (KPIs) for a broad universe of companies, enabling deep sector and industry research. The data is also available via Per Security, giving customers the flexibility of customized universes.
• Equity Pricing Point-in-Time: Bloomberg's Equity Pricing Point-in-Time product provides point-in-time, daily end-of-day composite pricing with security master data for the global public company universe.

Clients can link these datasets with Bloomberg's other Company Research Data products to seamlessly build company and industry knowledge graphs that enable alpha discovery and extraction.

To view the full results of the survey, please click here.

About Bloomberg's Investment Research Data Solutions
Bloomberg's Enterprise Investment Research Data product suite provides end-to-end solutions to power research workflows. Solutions include Company Financials, Estimates, Pricing and Point in Time Data, Operating Segment Fundamentals Data and Industry Specific Company KPIs and Estimates Data products, covering a broad universe of companies and providing deep actionable insights. This product suite also includes Quant Pricing with cross-asset Tick History and Bars. Additional solutions such as Geographic Segment Fundamentals Data, Company Segments and Deep Estimates Data and Pharma Products & Brands Data products will be available in 2025. All of these data solutions are interoperable and can be seamlessly connected with other datasets, including alternative data, and are available through a number of delivery mechanisms, including in the Cloud and via API. More information on these solutions can be found here.

About Bloomberg
Bloomberg is a global leader in business and financial information, delivering trusted data, news, and insights that bring transparency, efficiency, and fairness to markets. The company helps connect influential communities across the global financial ecosystem via reliable technology solutions that enable our customers to make more informed decisions and foster better collaboration. For more information, visit Bloomberg.com/company or request a demo.

SOURCE Bloomberg