The financial market data landscape is undergoing a transformation with increasing data volumes, expanded data types, and the need for real-time insights.
Because traditional data management approaches are often siloed, inefficient, and costly, the Data-as-a-Service (DaaS) framework has emerged as a solution. It alleviates the operational burden of managing data and enables firms to focus on their core competencies.
This article summarizes today’s financial market data challenges, explores the key considerations for implementing a DaaS strategy, and provides a framework for firms to navigate the evolving landscape.
Historically, the management of financial market data—across sourcing and ingestion, processing, quality assurance, and downstream distribution—has been hindered by slow delivery times, high costs, and limited flexibility. And some workflows rely heavily on manual processes and Excel spreadsheet add-ins for enterprise data integrations and usage.
Today, the industry is characterized by:
Proliferation of data sources: Firms need to integrate data from a growing number of vendors, as well as incorporate proprietary and third-party sources.
Data variety: The need to process structured, unstructured, and semi-structured data, including ESG data, private markets data, research reports, and traditional market data.
Desire to uncover new relationships and insights near real-time: The need to combine disparate datasets for new insights and achieve faster decision-making with near real-time access to information.
Technological advancements: Cloud computing, APIs, and AI are enabling new ways to access, process, and analyze data.
Increased regulatory compliance: Firms face growing pressure to comply with complex data-related regulations.
These trends are driving the adoption of DaaS, which offers a more agile, scalable, and cost-effective approach to data management. It alleviates the burden of managing data, regardless of location or data infrastructure.
DaaS connects services to streamline a firm’s data pipeline. By using open and flexible technology, DaaS works with third-party and proprietary sources to ensure data is complete, connected, and tailored to specific needs and downstream workflows.
DaaS - Data Operations Workflow
The benefits of DaaS for financial market participants include:
Improved data quality and consistency helps firms stem data quality issues.
Increased speed to market enables faster access to data and reduces the time required for data integration and processing.
Enhanced transparency provides greater visibility into data usage and costs, addressing a key concern for market data teams.
Greater efficiency and scalability enable firms to scale their data operations and optimize resource allocation.
Streamlined data and technology offers seamless integration and universal data connectivity to all downstream workflows.
Empowered users gain easy data discovery and democratized access to data insights.
Here are five key steps for firms considering a DaaS solution:
Step 1: Clarify objectives
DaaS solutions can be tailored to a wide range of needs, so begin by clearly defining the specific problems and outcomes you are targeting. This involves identifying the data, technology, and use cases to drive adoption of DaaS in your organization.
Step 2: Evaluate existing infrastructure
A thorough assessment of the firm’s current data infrastructure is crucial, including strengths, limitations, and associated costs. Data management teams can determine the readiness of existing systems and workflows to support a DaaS integration that aligns to the firm’s data strategy and challenges that may arise during a transition.
Step 3: Identify DaaS requirements
This stage involves a careful evaluation of DaaS services to determine fit with the firm’s specific needs. Consider leveraging hybrid and best-of-breed models to optimize flexibility and functionality. Key areas to evaluate include latency requirements, solutions for symbology linkages, prioritization of data security, privacy, and governance.
Step 4: Design and implement
This stage begins with a proof of concept and serves as a crucial step to test feasibility and demonstrate the benefits of DaaS within a controlled environment. Following this initial validation, a phased approach to implementation across the enterprise is essential, ensuring integration of DaaS with existing systems and workflows. Throughout the design and engineering process, it is paramount to establish the proper data governance and change management processes.
Step 5: Optimize and evaluate
A DaaS solution is not a static implementation. It’s a dynamic system that requires continuous attention and refinement to maximize its value. This involves monitoring DaaS performance and the impacts on downstream workflows as well as transparency on data usage and cost.
DaaS is a key enabler of data pipelines. As the industry continues to evolve, DaaS will play an increasingly important role in helping firms:
Build connected data ecosystems, break down data silos, and enable seamless data usage across the enterprise.
Establish the foundation for integrating AI and machine learning into data workflows, which enable advanced analytics and automation.
Innovate and develop new products and services with seamless access to high-quality data.
By adopting a well-defined framework and addressing key considerations, firms can leverage DaaS to modernize their data infrastructure, improve efficiency, and drive innovation.
If you’re considering a data-management solution for your firm, visit FactSet Data as a Service to learn how we can help you. Our solution combines our leading technology and data-management capabilities to facilitate and support numerous workflows, trusted entity and security mastering, and seamless vendor consolidation.
This blog post is for informational purposes only. The information contained in this blog post is not legal, tax, or investment advice. FactSet does not endorse or recommend any investments and assumes no liability for any consequence relating directly or indirectly to any action or inaction taken based on the information contained in this article.