Data Management Best Practices in Financial Services

Historically, the financial services industry predominantly relied on readily available public-security and company-level data (i.e., regulatory filings, broker research, pricing data) to make investment decisions.

The industry depended on security-level identifiers and siloed databases to consume and manage this content, making it a challenge to aggregate and connect data in meaningful ways. Over time, two major shifts have drastically changed the investment landscape:

Using this data governance framework, machine learning (ML) and artificial intelligence (AI) can drastically expand financial analysis and modeling capabilities. Some examples of this include leveraging ML and AI to recommend investment opportunities, automate trade decisions, enhance quantitative algorithmic trading models, and apply natural language processing to analyze earnings calls and other
corporate events for sentiment analysis.

Given the increased sophistication of these trends, the underlying database infrastructure and identifiers used to represent securities, business entities, funds, and their corresponding relationships have failed to keep pace. The financial industry is in a data connectivity “arms race.” The financial institutions that can effectively leverage and connect conventional market and reference data with alternative
and unstructured content assets—and aggregate these datasets at an enterprise level—will be at a significant advantage compared to their peers. The successful execution of a data and technology strategy that exploits connectivity and uncovers alpha generation  opportunities provides more accurate risk exposure analysis and can ease compliance by effortlessly supporting regulatory reporting requirements. For these reasons, the use of connected content sets, or “smart data,” can result in a significant competitive advantage.

Read more in the eBook: Data Management Best Practices in Financial Services.