A Data-First Approach to Enhancing Data Governance
February 8, 2024
Read Time: 7 minutes
Data Management
Market volatility, data mining for new investment ideas, evolving regulations, and more are forcing hedge funds to take a hard look at their data management strategy and platform resiliency. The notion of technology as merely a cost center has been replaced by firms strategizing how to align their technical wealth with business performance goals.
In this digital age, however, investment firms can find their business knee-deep in a multitude of challenges and questions to consider when it comes to governing their data.
The sheer volume of data, combined with the need for real-time analysis and insights, puts immense pressure on firms to modernize their data management strategies and systems. Exponential volumes of data — that are expected to be cleansed, normalized, and digestible — require a scalable domain-aware data model and well-curated data governance framework to empower decision making across the investment lifecycle.
As you evaluate your own data governance needs, taking a data-first approach will be critical as you look to:
Improving transparency is at the heart of data governance. Hedge funds are seeing demands for greater transparency from an ecosystem of regulators, investors, broker dealers, law firms, central banks, and the investment itself. Each entity has its own individual agenda, yet they all share a common dependence on the larger financial system.
In recent years, stricter reporting requirements have put hedge funds on alert for greater transparency. Each entity in the ecosystem contributes to a healthy financial system and must be ready to work collaboratively to encourage transparency.
One of the primary challenges hedge funds are up against is the complexity of their data landscape. With multiple sources of data coming in from various systems and platforms, it can be incredibly challenging to ensure data accuracy, consistency, and integrity. Data governance becomes critical as it provides a framework for managing and controlling data across the entire organization.
By implementing robust data governance practices, investment firms can establish clear data ownership, define data quality standards, and maintain data lineage to ensure their data remains reliable and trustworthy.
Another challenge is the regulatory landscape. With stringent regulations such as Form PF or the upcoming requirements of T+1, investment firms must comply with complex requirements that can often feel like they shift quickly.
As an example, the fast-approaching May 2024 compliance date to comply with the T+1 shortened settlement cycle presents a pressing need for impeccable data governance. Successfully transitioning to T+1 hinges on access to high-quality, real-time data on trades, affirmations, securities custody, cash positions, and more.
To address the challenges of managing complex data and multiple sources, funds must implement policies and procedures and regularly conduct audits. A streamlined approach to data governance, supported by the right tools and reliable sources of information, empowers compliance teams to better comprehend and efficiently meet their regulatory and reporting obligations. The added benefit is that teams are no longer scrambling as new regulations come into effect.
RELATED READING: Are You Ready for the T+1 Settlement Cycle?
With disparate systems and surging volumes of data, investment firms may find they are grappling with data quality issues. Inaccurate, incomplete, or inconsistent data can have negative consequences, leading to reporting errors, flawed investment decisions, missed opportunities, and reputational damage. Robust data quality management practices, including data cleansing, quality checks, and data validation, help maintain data integrity as soon as it enters your system.
Well-defined processes and techniques enable firms to ensure their data meets accuracy, completeness, consistency, and relevancy standards. It involves data profiling, data cleansing, data validation, and data monitoring to identify and rectify errors or inconsistencies. By proactively monitoring and improving data quality, investment firms can ensure their data remains accurate, reliable, and fit for purpose.
Clear guidelines and procedures that outline the rules and regulations governing the acquisition, storage, sharing, and disposal of data are critical. Well-defined policies enable organizations to maintain consistency, transparency, and compliance in their data management practices.
Data governance must be ingrained in your organization’s culture, with employees understanding the importance of data governance and their role in maintaining data integrity. Training programs and awareness campaigns help employees learn about data governance and educate them on best practices. A strong data governance culture also helps ensure that data governance becomes a shared responsibility across your organization.
Data stewardship encompasses the responsibility and accountability for data throughout its lifecycle, including managing and overseeing data assets to ensure integrity, accuracy, and security. Effective data stewardship ensures that data is properly classified, documented, trusted, and accessible to authorized individuals while safeguarding it from unauthorized access or misuse.
While annual technology spending is increasing, a significant portion is often allocated to maintaining and repairing outdated systems. A 2021 survey published by AIMA found that firms devote an estimated 80% of their technology budgets to simply ‘keeping the lights on’ and maintaining, repairing, and updating old technology systems.[1]
A commitment to modernization is crucial for investment firms looking to stay competitive and relevant in the digital era. Modern systems will also be key to ensuring good data quality and enforcing your firm’s data governance standards.
One of the leading challenges related to maintaining a consistent data governance structure is that firms often struggle with data integration and interoperability. As data sources continue to multiply and evolve, funds must have the confidence that they can seamlessly integrate data from disparate sources. Legacy systems and dated technologies can pose significant challenges in this regard, as they may not be designed to handle the complexities of modern data integration.
Adopting modern data integration platforms and tools can help funds streamline your data integration processes, reduce manual effort, and enable real-time data exchange across systems.
A robust project plan and governance are imperative to meet milestones. A detailed migration plan with defined scope, objectives, and deliverables helps teams stay focused, aligned, and prepared for potential risks.
To ensure successful legacy migration and maximize cloud-based system benefits, we recommend several key steps:
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By implementing robust data governance practices, investing in data security measures, adopting modern data integration platforms, ensuring data quality, and fostering a data governance culture, investment firms can overcome and unmask the full potential of their data. Strong data governance standards enable firms to make informed investment decisions, drive operational efficiency, and gain a competitive edge in the market.
To enhance efficiency and foster growth, hedge funds must transition beyond defensively managing technology debt. Firms willing to embrace a mindset focused on building technical wealth open the door to greater opportunities for advancement.
For a comprehensive view on how to evaluate and migrate your systems, read Arcesium’s Best Practice Guide: Migration to Modern Technology Platforms.
Author:
Neil Visnapuu
Neil is Vice President of Product Management at Arcesium. He describes his role as focused on product with a splash of engineering. Neil has spent more than two decades in FinTech building compliance strategies and solutions for the financial services industry. Follow him on LinkedIn.
Sources:
[1] Tech debt: The vicious cycle of legacy tech in asset management, AIMA, September 20, 2021
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