Traditional centralized data warehouses have been foundational to enterprise analytics for years, but they’re quickly becoming a constraint rather than an enabler. Centralized data creates bottlenecks that delay analytics initiatives, spawn duplicate datasets across silos, and obscure data ownership and accountability. Now, data mesh offers a transformative solution, providing investment firms with a decentralized architecture where business domains can own, govern, and publish data as products.
Leading institutions are already demonstrating the power of this approach. “Organizations can achieve up to 40% faster go-live for their data product initiatives,” CIO reports, based on their analysis of one such solution. For investment firms, data mesh offers a path to democratize access to critical data while maintaining the rigorous governance and compliance standards that fiduciary responsibilities demand.
The Data Bottleneck Constraining Investment Operations
The limitations of centralized data architectures are difficult to ignore. Investment operations are uniquely complex, with diverse teams—portfolio managers, traders, risk officers, compliance professionals, and operations specialists—all requiring immediate access to different data assets. When all data flows through a single central team, the friction can become unsustainable.
Consider the practical costs: organizations typically spend 80% of their data analytics effort on finding, cleaning, and preparing data rather than generating insights, according to an analysis in Forbes. When portfolio, trading, and risk data live apart, the fragmentation multiplies these preparation efforts. The response times of data teams responding to urgent data requests can cause problems for business users.
Meanwhile, data quality remains elusive. According to an EY survey, 79% of wealth and asset management executives identify poor data quality as a persistent pain point, even though 74% report their data is technically improving.
Fragmentation that Undermines Innovation
The result is an organizational dynamic that puts innovative initiatives on the back foot. That’s because when central IT teams become overwhelmed, a sequence of events unfolds:
- Business teams create unapproved workarounds.
- Shadow copies of data proliferate.
- Inconsistencies emerge between portfolio management, trading, and risk systems.
- This data quality problem becomes everyone’s problem and nobody’s responsibility.
This fragmentation also creates a form of “them-and-us” culture, where data transformation is viewed as an IT responsibility rather than a business imperative, EY suggests. On the other hand, placing people at the center of transformation—ensuring cross-functional buy-in and clear accountability—can drive outcomes that are 2.6 times more likely to succeed.
Understanding Data Mesh: A Paradigm Shift
Data mesh reimagines how organizations manage data. Gartner describes data mesh as “a cultural and organizational shift for data management focusing on federation technology that emphasizes the authority of localized data management.” Rather than centralizing data ownership, the model distributes it across domain-specific teams who know their data intimately and understand its business context.
The approach rests on four foundational principles:
- domain ownership
- data as a product
- self-service data infrastructure
- federated governance
This shift towards a decentralized, domain-federated architecture treats data not as a byproduct of operations but as a product—something created with intention, governed with clarity, and delivered with quality standards.
Why Data Mesh Works for Financial Institutions
Investment operations present an ideal use case for this architecture. The sector is characterized by rich, complex domains; for example:
- portfolio management
- trading
- risk
- compliance
- operations
Each domain has distinct data needs and requires its own subject matter expertise. Multiple stakeholders within each domain require different subsets of data with varying levels of urgency. Regulatory requirements demand both agility in innovation and rigor in control.
These characteristics make investment operations an excellent candidate for the governance flexibility and operational speed that data mesh enables. Indeed, 39% of FI respondents surveyed by Deloitte indicated high interest in data mesh architectures, particularly among firms seeking to optimize the balance between centralization and federation.
Empowering Domain Teams Through Data Ownership
The first and most transformative principle of data mesh is placing data ownership in the hands of the teams closest to it. For example:
- Portfolio managers must own portfolio data
- Trading teams must own trading datasets
- Risk professionals must own risk models and calculations
- Compliance teams must own regulatory reporting datasets
- Operations groups must own settlement and reconciliation data
This domain-driven ownership model transforms accountability. Each domain team becomes responsible for creating data products with clearly defined service-level agreements (SLAs) and quality metrics.
Domain expertise cannot be replicated in a central team; it must reside with the teams who understand the business context most deeply.
For example, a risk management team might publish a “Daily Portfolio Risk Metrics” data product with specific commitments to accuracy, documentation of methodology, daily refresh rates, and more. This specificity creates genuine accountability—when data quality issues arise, the teams with the deepest expertise are accountable and empowered to solve them.
UBS Data Mesh Supports Companywide Queryable Knowledge Base
UBS, the global banking giant, put this approach into action. In a case study from Microsoft, the bank implemented a data mesh alongside its transition to the Azure cloud. In doing so, the firm distributed data management responsibilities across divisions. Domain teams took responsibility for their data products while maintaining compliance with enterprise standards. UBS digitized approximately 60,000 investment advice and product documents into a queryable knowledge base, making this vast repository easily accessible to employees.
When data becomes a product, quality metrics move beyond aspirational targets to become contractual commitments, measured and monitored continuously. This creates a market dynamic where data producers compete to offer higher-quality, better-documented products.
Self-Service Data Platforms Eliminate IT Bottlenecks
Data ownership is only half the equation. Domain teams require their own self-service infrastructure to create, publish, and maintain data products without central IT dependency. Self-service platforms provide unified discovery and access, allowing portfolio managers, compliance officers, and analysts to find and combine data independently. Benefits include:
- Unified discovery and access. Self-service platforms offer business-friendly interfaces that abstract technical complexity. Portfolio managers search for risk datasets without IT intervention. Compliance officers can access regulatory datasets on demand. Analysts combine data from multiple domains to answer cross-functional questions.
- Rapid deployment. Automated access provisioning dramatically reduces time-to-value. Self-service provisioning shortens regulatory dataset deployment from months to days. According to CIO, organizations can achieve up to 40% faster go-live for data product initiatives through streamlined access controls.
- Real-time data sharing. Live data shares provide immediate access to source systems rather than stale overnight extracts. Portfolio managers identifying market opportunities get up-to-the-minute risk assessments instead of hours-old batches.
- Multi-cloud flexibility. Modern platforms support hybrid and multi-cloud environments, enabling investment firms to work across different infrastructure providers while maintaining consistent governance.
Through self-service, domain teams can govern their own data shares without logging into multiple governance platforms—a critical feature for distributed management.
Federated Governance Balances Autonomy and Compliance
Investment firms operate under intense regulatory scrutiny. Using a data mesh architecture, federated governance models resolve the tension between speed and compliance by establishing central policy frameworks while enabling local implementation flexibility.
- Policy-as-code automation. Rather than manual compliance reviews, policy-as-code embeds governance rules directly into data pipelines and infrastructure. Encryption policies, access controls, and data retention rules execute automatically against every data product, regardless of domain. Automated governance solutions can reduce manual audit workloads while improving accuracy and consistency.
- Consistent standards across heterogeneous infrastructure. While central teams can define enterprise standards such as encryption requirements, data classification frameworks, and regulatory reporting obligations, domain teams can implement them using appropriate tools for their environment. A trading domain and portfolio management domain can use different cloud providers but apply identical encryption policies, ensuring consistent compliance across infrastructure.
- Real-time compliance enforcement. Federated governance enables sophisticated compliance scenarios specific to investment operations. Real-time trading compliance executes automatically before trades occur. Pre-trade and post-trade surveillance rules run against live trading data. Regulatory reporting datasets automatically include all required fields, transformation logic, and audit trails.
In this environment, individual domains retain autonomy in how they source, transform, and optimize their data. Domains can innovate without waiting for central approval, provided they operate within established governance guardrails.
Real-Time Data Products Powering Investment Decisions
Investment operations need data to be extraordinarily fresh. Market volatility, shifting client behavior, fluctuating risk exposures, and others put business success at risk. Data mesh architectures support streaming data products that deliver real-time insights to operational and strategic systems simultaneously.
Streaming Data Products Across Investment Operations
Real-time data products move investment firms beyond end-of-day reporting toward continuous analytics. Market analytics data products deliver real-time price feeds, sentiment analysis, and risk factor updates. Portfolio monitoring products track live positions, profit-and-loss, and exposures across asset classes. Client insight products analyze behavior patterns and engagement metrics in near real-time. Risk monitoring products calculate value-at-risk, stress testing, and limit monitoring as positions change.
Immediate Decision-Making and Response
Portfolio managers accessing live risk assessments respond to market conditions immediately rather than waiting for overnight reports. Compliance systems detect regulatory breaches as trades occur rather than discovering them during post-trade analysis. Risk committees have updated exposure visibility for board meetings rather than stale, hours-old snapshots.
Pre-Trade Compliance Prevention
Real-time data products support pre-trade compliance checks that prevent problematic trades before execution. Firms can catch position limit breaches, concentration risk violations, and regulatory restrictions automatically. This proactive approach reduces regulatory risk, operational friction, and costly trade breaks.
Event-Driven Architecture
This capability combines event-driven design with distributed data platforms. As trades execute, portfolio positions change, or client interactions occur, events flow into streaming platforms. Domain teams subscribe to relevant events and create derived data products. This architecture scales to thousands of simultaneous data products while maintaining governance and quality standards.
Executing the Data Mesh Transition
Organizational alignment is essential for implementing your data mesh architecture. According to EY, transformation programs are 2.6 times more likely to succeed when organizational and human aspects receive proper attention. The following steps guide investment firms through a phased approach to data mesh adoption.
Step 1: Define Strategic Vision and Secure Executive Sponsorship
Begin with a clear vision: what data-driven outcomes matter most to driving a competitive advantage? Common priorities include:
- enhanced client-centric decision-making
- improved risk management visibility
- faster regulatory compliance
Securing executive sponsorship at this stage is critical. It signals organizational commitment and removes implementation barriers.
Step 2: Launch a Pilot Data Mesh Project
A pilot project approach reduces risk while building organizational understanding. For example, environmental, social, and governance (ESG) data management serves as an ideal pilot in the asset management sector, addressing both pain points and regulatory requirements. The right pilot will demonstrate feasibility, quantify benefits, and build momentum for broader adoption.
Step 3: Invest in Data Mesh Platform Infrastructure
When investing in a platform, firms should prioritize self-service data discovery and access to automation alongside governance frameworks. Cloud-based infrastructure supports multi-domain, multi-cloud flexibility that modern investment operations require.
Step 4: Expand Domain by Domain
Gradual domain expansion allows the organization to learn and refine governance. Firms can begin with operations or portfolio management and extend governance through trading, risk, and compliance. This phased rollout builds organizational capability while reducing disruption risk.
Conclusion: Unlocking Data’s Potential with Data Mesh
Data mesh transforms how investment firms balance speed and governance. By distributing data ownership to domain teams while enforcing policy-as-code governance, firms achieve faster analytics deployment without sacrificing regulatory compliance. Self-service platforms eliminate IT bottlenecks, while real-time data products power immediate decision-making. Investment firms should evaluate their current data architecture against data mesh principles and consider how domain-driven ownership, federated governance, and self-service platforms could unlock their data-driven potential.
Choose Option One Technologies for Your Next Data Initiative
Option One Technologies specializes in democratizing best-in-class data mesh implementations for investment firms, hedge funds, and asset managers. From architecture assessment through deployment of cloud-based data platforms, Option One delivers the infrastructure and expertise that enable your teams to hyper-scale analytics capabilities while maintaining rigorous governance and compliance standards. Contact one of our experts today to learn more.
