Welcome to the sixth part of our Credit Data Strategy blog series.
Based directly on conversations with our clients and other market participants, this special blog series will spotlight how the credit market is leveraging technology and automation to unlock greater performance. Whether your firm has already implemented a credit data strategy or is yet to begin, this series will help you benchmark your practices and offer resources and advice on how to succeed in an ever-shifting landscape.
The key components of a successful strategy
Crafting an effective credit data strategy requires careful consideration of several key components by investors to ensure success. Firstly, the specific benefits from the previous section (Advantages of implementing a credit data strategy) above should be identified and ranked, and other firm-specific objectives added if missing from that list. It’s essential to identify the goals being targeted and have in place a process to measure the impact post-completion.
Shaping this strategy can necessitate the involvement of diverse stakeholders within the organization, including investment professionals, data analysts, technologists, and senior management. Collaboration among these stakeholders ensures alignment of the credit data strategy with broader business goals, facilitating seamless integration.
Here's our list of recommended components to ensure a successful implementation.
1. Choose the best data providers for your business
Next, the required external data providers need to be identified, and any necessary in-house resources allocated to the project’s implementation. Typical key considerations for vendor selection include data quality, comprehensiveness, timeliness, customer service, and market reputation. For the purposes of this report, given the intended audience, we are predominantly focused on fundamental and market data when referring to “credit data strategy”, but this definition could certainly be expanded depending on the firm’s unique requirements. For example, macroeconomic and industry-specific inputs would be sensible additions to a credit data ingestion program once the initial set-up is completed and validated.
2. Make those providers long-term partners
Forging long-term data partnerships is equally crucial in this process, as external partners can provide access to specialized expertise and resources that complement internal capabilities. Identifying partners able to meet specific needs and aligning with the organization's strategic goals is essential for building sustainable relationships that drive value over time. Whereas it was common a decade ago for data procurement to aim for “one-stop shop” vendors providing multiple services in a bundled offering, best practice today is for data buyers to select specialist partners for specific datasets that offer the highest quality, premium service, and sourcing diversification so that the buyer is never too dependent on any one large provider in the market.
"Identifying partners able to meet specific needs and aligning with the organization's strategic goals is essential for building sustainable relationships that drive value over time."
3. Mandate someone within the research team to oversee the project
Dedicating a specific role within a given business unit to consider and manage the credit data strategy is essential for its success. This individual or small group needs to be responsible for overseeing all aspects of the strategy, from data procurement and analysis to governance and compliance, ensuring alignment with organizational objectives and regulatory requirements.
4. Ensure you have dedicated technical resources
Additionally, adding technical resources such as programmers to the investment team ensures that technical knowledge is not a barrier to leveraging data solutions effectively. These individuals play a vital role in implementing and maintaining data infrastructure, developing custom analytics tools, and translating technical considerations into actionable insights for investment professionals. We would go so far as to say that every credit investment team should have a minimum of one developer on-staff full-time, and that if that’s not the case today the business should ensure their next hire meets this requirement.
"We would go so far as to say that every credit investment team should have a minimum of one developer on-staff full-time."
5. Make data procurement part of your operating budget
Finally, allocating data procurement to the annual operating budgets of credit investment teams is essential, underscoring the importance of treating data and technology spend as distinct from traditional third-party research support services. This dedicated budget allocation ensures that sufficient resources are available to support the credit data strategy, enabling timely investment in data capabilities when necessary. On this point, we would also recommend that the relationship between the headcount budget and the data budget be clearly defined, so that there’s flexibility to grow the data budget in cases where it can reduce or eliminate incremental hiring requirements. While the data budget should be a separate line item, it’s vital for management to have spending flexibility intra-year when increased data spend can be additive to the team’s bottom line.
Coming up: Five examples showcasing best-in-class
Next week, we share some real-world examples from our clients that showcase a "best-in-class" approach to credit data strategy, highlighting how they are leveraging data to maximize their research effort and deliver better performance.
As always, if you'd like to discuss your own strategy, contact us if we can be of further assistance.