The reality of modern investing is that external data is a critical resource asset managers need to help generate peak performance. To achieve your firm-wide goals, what data do you need and where are you getting it from?

As firms move through the complexities of managing diverse portfolios with an emphasis on multi-asset class strategies, creating a sophisticated data and operations framework is critical to navigating a data-driven investing model.

What separates data-fluent asset managers from the rest of the field?

Enhanced returns are an outcome of successful implementation of data integration, transformation, and observability, driving the current trend towards centralizing data for asset managers to streamline processes and improve performance. A centralized data repository can help enable the onboarding of vast amounts of data per year. And while some asset managers and larger financial institutions have the knowledge and experience to onboard thousands of datasets per year – which gives them a performance edge – firms that still use a traditional, decentralized, model are behind on onboarding data at scale.

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1. Establish thorough evaluation frameworks to bring in the right data

A basic evaluation framework should consider attributes across data quality, data uniqueness, and overall vendor relationship:

Data quality, which is indicative of reliability of the data for insights, is the most important attribute.
Data uniqueness, though often more of a front-office consideration, can indicate how much alpha or differentiated insight potential the data has.
Vendor relationships, when positive, can help offset potential shortcomings of the product through improved commercials or technical support.

Full breadth and depth of data coverage is necessary for the most accurate and reliable insights, especially for critical business operations. Sourcing multiple products in the same data category is a best practice for full history, geographic and demographic coverage, and granularity. A robust selection framework will ensure decision-makers select optimal products for their firm’s workflows and insights needs.

READ MORE: Framework for Data Sourcing

2. Build scalable legal and compliance frameworks

The importance of the legal and compliance operations that support onboarding new data can sometimes go unnoticed. Business leaders can mistakenly assume the risks and costs incurred from managing these workflows ad-hoc or in a decentralized way are natural and inevitable, unaware of the long-term damage and risk it exposes them to. A robust framework for legal and compliance when onboarding new data vendors is crucial for mitigating missed opportunities, non-compliance, and increased costs. Pitfalls of not having one are:

Ineffective approval workflows. Lack of standard target and fallback stances on key data compliance and legal issues can result in delays and bottlenecks in the approval process, leading to missed opportunities and delay or loss of revenue. Without a streamlined framework, critical contracts may languish, causing delays in closing deals and hindering growth.
Compliance issues. Inadequate data vetting procedures increase the risk of non-compliance with legal and regulatory requirements. Failing to adhere to industry standards and regulations for data provenance can result in costly penalties, reputational damage, and potential legal disputes that drain resources and hinder business operations.
Lackluster reporting. Organizations struggle to generate comprehensive and accurate data budget reporting in the absence of centralized contract management processes and tools. Lack of visibility into contract data hampers decision-making, inhibits strategic planning, and undermines operational efficiency
Lost revenue. Overlooked opportunities and mishandled renewals due to poor contract tracking contribute to lost revenue streams. Best case, you’re enabling a higher cost center; worst case, you’re affecting your firms bottom line.
Trapped data. Ineffective contract management can lead to data fragmentation and silos, making accessing and utilizing critical third-party data difficult for all relevant users in the organization. Trapped data inhibits effective research and collaboration. Unlocking the full potential of a data asset requires a centralized and easily accessible repository for efficient data management and governance.

3. Deploy appropriate tools and talent for agile data onboarding

In addition to a flexible, modern data platform that can accommodate diverse data types, investing in scalable tooling and skilled resources accelerates data throughput for diverse business requirements. Considerations for a successful integration program include:¹

From a tooling standpoint, low-code and no-code are here to stay. Self-service tools help put staged data in the hands of downstream users with fewer dependencies.
Investing in training and recruitment. Firms are investing heavily in their engineering recruitment pipelines and training programs and must increasingly leverage staff augmentation programs.
Investing in advanced cloud-based data science tools enable firms to extract new value out of their existing data assets. Continuously emerging data science techniques call for new requirements of data– for example, training data that supports LLMs and other ML applications. This inevitably raises scalability requirements for data platforms, renders many older systems as lacking transparency and prone to error, and accelerates re-platforming cycles.

The importance of measurement and attribution for optimized vendor management

Post onboarding, establishing clear criteria to measure the success of your ongoing vendor relationships is crucial. With well-defined metrics, you can better steer the commercial dialogue and drive product improvements, while also creating benchmarks for assessing potential future vendors.

When it comes to gauging a vendor’s effectiveness, several key metrics come into play:

Utilization: Is the vendor’s offering being successfully utilized by your internal users? What is the impact?
ROI: Are the vendor’s pricing structures competitive and transparent? Is the pricing multiple appropriate relative to how you measure utilization and impact?
Innovation: Does the vendor incorporate continuous product enhancement into their roadmap?
Responsiveness: Is the vendor responsive and do they adhere to SLAs? Does the vendor generally prioritize the needs of your firm?

Even if some of these metrics aren’t optimal or measurable at the start of the relationship, they provide a clear path forward for engagement. Strong vendor relationships are built on continuous communication, collaboration, and alignment of goals and expectations.

It’s important to recognize that these metrics are interconnected, with trade-offs to consider, and dependencies on your firm’s internal resources. For instance, prioritizing innovation might come with a slower path to ROI, while optimizing for low costs could compromise partnership quality. As you implement processes to measure and enhance vendor performance, be mindful of these potential trade-offs.

Specific metrics you may want to consider include:

Team- or user-specific measurement (data cost and utilization)
Hours required for pipeline maintenance
Production-grade data issues or SLA breaches
Calculating quarter-over-quarter or year-over-year changes for the above

Vendor measurement often also yields significant insights into internal teams’ levels of data-fluency and the steps that need to be taken to evangelize data throughout the firm.

4. Maximize stakeholder engagement

Internal user groups ultimately own the perception of value in onboarded data. Building out comprehensive feedback loops with internal users, in addition to your vendors, supports successful outcomes. Reporting on vendor benchmarks and firm onboarding goals enhances accountability, optimizes resource allocation, and creates a roadmap for continuous improvement of your onboarding program. Similarly, identifying particular processes or deliverables that have significantly benefited your firm can influence the direction of service from your vendors and multiply your success.

5. Select the right technology partner(s)

Solving for data throughput, availability, and measurement isn’t possible without a strong data management foundation. With a mature ecosystem of partners offering data management solutions, there is less need for firms to build what is now a less-differentiated capability amongst peers. When a partner has domain expertise – and both tools and talent to integrate data for specific workflows onto a purpose-built platform – firms have more operating leverage to focus in-house resources on the most differentiated proprietary data and technologies.

Firms that want to consider partnering as a key part of their strategy for technology and data initiatives need to have the right framework to analyze and select prospective partners — the same way a framework would exist for data vendor evaluation or investment selection. A good benchmark for consideration would be to find a reputable partner that can offer up to 70%-80% of the required solution.

Pros of partnering include:

Quicker time to market depending on customization needs and feasibility
Reduced operational footprint
Built-in adaptability via partner R&D
Retention of control and oversight
Product roadmap influence

BUILD, BUY, PARTNER: A Framework for Optimizing Time to Value of Your Data Initiatives

Conclusion

Accelerating data onboarding is essential for asset managers to gain a competitive edge and achieve peak performance in today’s data-driven investing landscape. It’s key to establish thorough evaluation frameworks, build scalable legal and compliance processes, deploy the right tools and talent, implement robust measurement and feedback loops, and select technology partners that give your firm’s resources leverage. This enables firms to unlock the full enterprise value of their data assets.

A centralized data management strategy empowers organizations to onboard and manage vast amounts of high-quality data seamlessly to future-proof their operations and maintain a sustainable competitive advantage in an ever-evolving market.

The best practices for data throughput acceleration are straightforward, but require dedicated time and attention to achieve the desired effects on operational efficiency and profitability. By prioritizing best practices for data onboarding, firms can navigate the complexities of multi-asset class strategies with confidence, leveraging data fluency to generate superior insights and deliver exceptional returns for their clients. Working with a trusted partner like Arcesium can help build strong, productive relationships, and allows your firm to focus on the higher-value work that comes after data onboarding.

Sources:

1 Raw to Refined: A Framework for Data Sourcing, Arcesium, October 9, 2023.

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