The increasing reliance on data-driven investment strategies has brought significant challenges to the forefront. Data coverage, timeliness, and quality issues with historical data are bundled as the chief challenge faced by research analysts, quants, and data scientists, according to a recent Bloomberg Research Survey.
The “Investment Research Data Trends Survey 2024” was based on data collected from a series of client workshops around the globe in 2024. Bloomberg organized eight in-person events in cities across North America, EMEA, and APAC. During these events, 166 clients participated in live surveys to share their insights on key trends and challenges in investment research.
The challenge of not having adequate data coverage, timeliness, and quality could be the result of several factors, including inconsistent data due to poor data management practices, inadequate data governance, and a lack of proper data validation procedures.
The Bloomberg survey revealed that organizations are also struggling with normalizing and wrangling data from multiple data providers and identifying which datasets to evaluate and research.
These very challenges likely contribute to another key finding that nearly three-quarters (72%) of respondents can only evaluate a maximum of three datasets concurrently. In addition, nearly two-thirds (65%)require at least a month to assess just one dataset. This suggests severe constraints on the ability of these investment research firms to effectively use available data.
So how are firms planning on tackling these challenges? The survey indicates that firms are still determining the best approach to managing research data. According to the survey, 50% of respondents said they currently manage data internally using proprietary solutions, while only 8% outsource to third-party providers. This suggests a strong preference for retaining control over their data management processes.
With more than six in ten (62%) of respondents preferring their research data to be made available in the cloud, it shows that there is a significant shift towards scalable and easily accessible data storage options.
More than one-third (35%) also want their data to be accessible through more traditional routes including on-premise, Secure File Transfer Protocol (SFTP), and REST API. This means that while investment research firms are comfortable with having their data on the cloud, they would also prefer to have 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,” said Angana Jacob, Global Head of Research Data, Bloomberg Enterprise Data. “The journey from data sourcing to extracting alpha is difficult and the continuous ingestion, cleaning, modeling, and testing of data is particularly challenging.”
“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.”
While firms are exploring various management strategies, including a growing interest in cloud solutions, the need for comprehensive and flexible data solutions is undeniable. Bloomberg claims that its new data products directly address key challenges revealed in its survey of investment professionals.
Bloomberg highlights its Equity Pricing Point-in-Time product, which offers daily end-of-day composite pricing data for global public companies, as a solution to address challenges related to data timeliness and quality. Additionally, the Industry Specific Company KPIs and Estimates product can assist in deep sector and industry research with its point-in-time data that covers over 1,200 unique KPIs.
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