Key Insights from Bloomberg's Survey on Challenges for Quants and Analysts in Accessing Quality Data

Understanding the Challenges Faced by Quants and Data Scientists

In the ever-evolving landscape of financial markets, quants, research analysts, and data scientists are increasingly relying on sophisticated quantitative techniques and machine learning algorithms to gain a competitive edge. A recent survey conducted by Bloomberg sheds light on the key challenges these professionals face in their quest for high-quality investment data.

Survey Overview

Bloomberg's survey, which gathered insights from over 150 professionals during a series of global client workshops, identified critical trends and obstacles that these individuals encounter while navigating the complexities of investment research. The findings indicate that the demand for robust data is more crucial than ever, particularly in an age characterized by a deluge of information.

Top Challenges Identified

The survey revealed that data coverage, timeliness, and quality are significant concerns for nearly 37% of the respondents. Specifically, respondents pointed out that historical data issues jeopardize their ability to make informed investment decisions. Following closely, 26% highlighted difficulties in normalizing and managing data sourced from multiple providers, while 15% expressed challenges in determining the datasets worth evaluating for their research.

Interestingly, a staggering 72% of respondents reported being able to assess only three or fewer datasets simultaneously—an alarming statistic given the increasing need for access to numerous alpha-generating data sources. Moreover, more than half of the respondents indicated that it typically takes them one month or longer to evaluate a single dataset, thereby stalling their research efforts.

Data Management Strategies

Amidst these challenges, firms still face the ongoing task of optimizing their data management strategies. The survey highlighted that half of the participants currently manage their data centrally through in-house solutions, while only 8% resort to third-party services. The survey also uncovered a strong preference among 62% of participants for cloud-based data solutions, indicating a push towards more accessible and scalable data management tools. However, 35% of respondents also expressed the need for traditional access methods, like REST APIs and on-premise solutions, reflecting an appetite for flexibility in data delivery options.

"From discussions with our research clients, it's evident there's a growing need for innovative datasets and AI-ready data," comments Angana Jacob, Bloomberg's Global Head of Research Data.

Bloomberg's Commitment to Data Solutions

To address the evolving requirements of the financial community, Bloomberg is dedicated to enhancing its multi-asset Investment Research Data product suite. This suite aims to cater specifically to the challenges faced by quantitative and quantamental researchers. Among the suite's newest offerings are tailored products such as Industry Specific Company KPIs and Estimates, which provide point-in-time data for more than 1,200 key performance indicators across various industries. Additionally, the Equity Pricing Point-in-Time product enables daily, end-of-day pricing access for global public companies, further enriching the depth of analytics available to clients.

Bloomberg’s proactive development of these resources underscores its commitment to advancing the capabilities of its users in extracting valuable insights from complex datasets. With enhanced interoperability between various datasets and the introduction of adaptable data access methods, the road from data sourcing to alpha extraction appears to be easing, albeit with ongoing challenges.

The Path Forward

The results of Bloomberg's survey highlight a critical juncture for quants and research analysts in grappling with the complexities of investment data. As the need for timely, accurate, and comprehensive data continues to grow, it becomes imperative for firms to adopt effective strategies for data management and retrieval. By investing in innovative solutions that assist in the normalization, evaluation, and accessibility of research data, these professionals can better navigate the data-rich environment’s challenges and ultimately enhance their capability to generate alpha in an increasingly competitive marketplace.

To explore the full survey results and find additional insights into Bloomberg's solutions, please visit Bloomberg.com.

Conclusion

As financial markets become more data-driven, the insights gained from surveys like Bloomberg's serve as a compass, guiding quants and analysts through the uncharted waters of investment research. With a concerted focus on addressing quality, coverage, and timeliness of data, professionals are better equipped to seize opportunities in a landscape defined by change and innovation.