Why are asset managers still struggling with fragmented data?
Asset managers are generating more data than ever, but many still struggle to access timely, trusted insights across front-, middle-, and back-office systems.
Across our clients, Snowflake’s AI Data Cloud is increasingly being used to unify data across platforms that typically don’t integrate well, such as order management systems (OMS), accounting platforms, trading, risk systems, reporting and market data providers. This allows firms to move beyond fragmented data environments and toward more consistent, accessible investment data.
Because it is cloud-based, Snowflake scales with fluctuating data demands without requiring large upfront infrastructure investments. Its architecture separates compute costs from storage costs, allowing firms to scale each independently based on usage, and supports horizontal scaling without the need to provision or manage additional infrastructure. It also enables secure data sharing, allowing firms to collaborate with custodians, data providers, and clients without duplicating data.
Industry research reinforces this challenge. According to a recent InvestOps and SimCorp report, 63% of firms still lack a unified, real-time data layer across front, middle, and back office, highlighting how widespread fragmentation remains.
As part of our commitment to helping clients get the most out of their Snowflake investments, Grandview Analytics has joined the Snowflake Partner Network. This reflects our focus on helping asset managers operationalize Snowflake in ways that align with real-world investment workflows and data challenges.
The business pressures driving data modernization in asset management
Financial services firms are under increasing pressure to operate more efficiently, deliver transparency, and provide faster insights to both the front office and clients. However, most firms are constrained by longstanding structural challenges.
In practice, these challenges show up in very tangible ways:
- Fragmented data ecosystems: Specialized platforms across trading desks, operations, risk, compliance, and client reporting create silos—often requiring manual reconciliation across systems.
- Data inconsistencies across platforms: Disparate systems such as Aladdin, Geneva, or SimCorp can introduce breaks in data alignment
- Legacy infrastructure limitations: On-premises data warehouses and custom-built systems struggle to support today’s scale, time to market, performance, and analytics requirements.
- Delays in reporting cycles: Manual data aggregation delays operational processes and client reporting timelines
- Heightened regulatory pressure: Transparency, lineage, and auditability are non-negotiable.
- Demand for real-time insights: Portfolio managers, risk teams, and client-facing groups increasingly expect immediate access to trusted data.
- Rising cost pressures: Firms must modernize without inflating total cost of ownership.
These challenges are not just structural; they’re operational. Many firms continue to rely on manual processes and workarounds, with 49% still depending on manual reconciliation and 43% reporting fragmentation across systems, according to InvestOps and SimCorp research.
These realities are driving firms to adopt modern, cloud-native platforms that can unify data and provide a single, trusted foundation for scalable analytics and reporting.
How Snowflake enables scalable, governed investment data
Snowflake’s AI Data Cloud is emerging as a strong foundation for addressing these challenges, particularly when implemented with a clear data strategy and governance model.
Unlike traditional data warehouses, Snowflake enables multiple workloads (analytics, reporting, and data sharing) to operate on the same underlying data foundation without duplication. This allows firms to centralize, govern, and analyze data more efficiently while reducing the burden of maintaining legacy infrastructure.
Key benefits include:
- One platform, many workloads: Consolidates reporting, analytics, AI/ML, compliance, and client dashboards.
- Unified investment data: Breaks down silos to create a consistent, trusted view across asset classes and business functions.
- Elastic scalability: Handles compute-intensive data workloads like risk modeling, back-testing, and scenario analysis
- Secure data sharing without friction: Enables governed collaboration with custodians, counterparties, and clients.
- Built-in governance: Supports regulatory requirements through security, lineage, and access controls.
What are the most valuable Snowflake use cases for asset managers?
Across our work with asset managers, several high-impact use cases consistently emerge:
- Front-to-back data integration: Unifying data across OMS, accounting, and risk systems to create consistent views of positions and exposures
- Client and regulatory reporting hubs: Centralizing data to streamline reporting processes and improve accuracy and auditability
- Golden copy data management: Establishing consistent reference, security master, and transaction datasets across the organization
- External data integration: Leveraging Snowflake’s data sharing and marketplace capabilities to incorporate market, ESG, and alternative datasets
- Advanced analytics enablement: Supporting use cases such as risk modeling, liquidity analysis, and performance attribution
In recent client engagements, these use cases have helped improve data accessibility, reduce reporting timelines, and enhance operational efficiency.
Grandview’s perspective: What separates successful Snowflake implementations
While Snowflake is a powerful platform, its impact depends heavily on how it is implemented within the context of investment data workflows.
From our experience, several factors consistently differentiate successful implementations:
- Snowflake is a foundation, not a complete solution: Firms still need well-defined data models, governance, and integration strategies
- Data standardization is often underestimated: Normalizing data across systems is one of the most critical and resource-intensive steps
- Business alignment drives success: The most effective implementations are tightly aligned to front-, middle-, and back-office use cases
- Value comes from activation, not just consolidation: Centralizing data alone is not enough. Firms must enable analytics, reporting, and decision-making.
How Grandview Analytics helps firms maximize Snowflake
While Snowflake provides the technological foundation, realizing its full value requires industry-specific expertise to design the right approach, migrate data efficiently, and ensure business users can trust and act on the data.
Grandview Analytics combines deep knowledge of investment data workflows with technical expertise to help firms integrate Snowflake into their environments and use their data to its fullest potential.
- Integration
We design data architectures and migration roadmaps aligned to investment workflows, integrating platforms such as Aladdin, SimCorp, Geneva, and other internal and external data sources. We establish governance frameworks that ensure data is consistent, high-quality, and trusted across the organization. - Expansion
We help firms build out investment data warehouses and golden copies for securities, reference, and transaction data. We enable analytics across portfolio management, risk, compliance, and client reporting, and implement secure data-sharing solutions to improve collaboration. - Enhancement
We support advanced analytics and AI/ML use cases, such as predictive risk modeling or liquidity forecasting. We also automate data pipelines, develop dashboards and self-service tools for business users, and optimize Snowflake usage to balance cost, performance, and scalability.
Is your data environment ready for what comes next?
As asset managers continue to invest in data as a strategic differentiator, platforms like Snowflake are becoming central to how firms scale analytics, improve transparency, and support more timely decision-making.
Adoption of advanced technologies is accelerating—70% of firms are now actively deploying AI solutions—but many remain constrained by fragmented or inconsistent data.
Technology alone doesn’t deliver results. The investment firms seeing the most success are those that align Snowflake to their investment workflows, establish strong data governance, and focus on enabling real business use cases from day one.
As you evaluate your approach, consider:
- How easily can your teams access trusted data across systems today?
- Are your current platforms enabling real-time insights—or delaying them?
- Is your data environment ready to support emerging AI and analytics use cases?
Grandview Analytics helps financial services firms translate Snowflake’s capabilities into practical, high-impact solutions aligned to their investment operations to unlock new opportunities for growth.
Ready to modernize your data with Snowflake? Reach out to Arun Krishnamurthy to discuss how Grandview Analytics can support your Snowflake initiatives and broader investment data strategy.
