Investment Data Platform Solution

The Vendor is required to provide information regarding modern data platform capabilities that can support the trust company’s investment, accounting, operations, performance, and risk reporting needs through a centralized, governed investment data platform.
- Company intends to modernize its investment data environment by implementing a centralized Investment Data Platform that aggregates, harmonizes, governs, and distributes both structured and unstructured data sourced from multiple systems of record, including but not limited to investment systems, accounting systems, emails, market data vendors, other datalakes, data warehouses, etc.
- Platform vendors to provide platform-aligned architecture guidance, investment-data design, best practices and domain-specific implementation considerations to support safe and effective adoption, while the trust company retains ownership of all tool selection, governance, and strategic decisions.
- These include, but are not limited to:
• SimCorp – investment book of record (IBOR), accounting, transactions, positions
• Clearwater Analytics – accounting, performance, LP / GP data
• Bloomberg – trading, market data, pricing, reference data, analytics
• RiskMetrics – analytics, ex-ante risk, attribution
• Backstop Solutions – alternatives data, CRM, research
• Internal or third-party operational and investment systems
- Centralized Investment Data Platform Solution to:
• Aggregate and harmonize both structured and unstructured data
• Apply governed investment data models
• Maintain a trusted golden copy for reporting and analytics
• Provide data lineage and audit traceability required of fiduciaries
• Enable consistent cross-organizational reporting
• Improve transparency and data quality
• Reduce manual reconciliation and duplicated efforts across teams
- Objectives
• Investment-grade data platforms with proven capital markets deployments.
• Approaches for designing and operating a consolidated data hub that can aggregate structured, and unstructured data from authoritative systems of record (e.g., SimCorp, Clearwater, Bloomberg, Backstop, and internal platforms) and support data flows where the enterprise data platform serves as an intermediary or transit layer for downstream systems (for example, scenarios such as Bloomberg SMF → SimCorp Dimension). The solution should accommodate mixed patterns—including direct sourcing, pass-through routing, enrichment, and standardization—while ensuring consistency, quality, lineage, and governance across all data movement paths.
• Capabilities supporting governance, lineage, quality, and audit controls expected of a fiduciary institution.
• Solutions that can be operated by a small internal Information technology team.
• Platform-aligned vendor guidance on architecture, operating models, and recommended implementation sequencing.
• Implementation considerations, including:
o Description of the vendor’s recommended implementation approach for their platform
o Typical implementation phases for institutional investment clients
o Required staffing, skillsets, and environmental prerequisites
o Expected involvement from The Trust Company and the vendor during implementation
o Options for vendor-supported implementation services or certified implementation partners
o Expected timelines for onboarding investment data sources and enabling core capabilities
• Pricing, licensing, and operational considerations relevant to The Trust Company.
- Defining Data Strategy (Platform-Aligned Guidance Only)
• Architecture best practices.
• Governance and data-quality models suitable for the platform.
• Integration patterns for investment data sources.
• Sequencing and roadmap recommendations aligned to the platform.
- Platform Architecture & Integration
• Overview of the vendor’s recommended architecture for The Trust Company.
• Integration patterns for SimCorp, Clearwater, bloomberg, Backstop, Risk Metrics, Custodians, and private-markets systems.
• Support for batch and streaming ingestion, scheduling, schema evolution, metadata harvesting, and error handling.
• Frameworks for harmonization and construction of a Golden Copy.
- Data Quality
• Profiling tools, data-quality rule management, monitoring, alerts, and workflows for exceptions and remediation.
• Support for time-series, transactional, pricing, benchmark, and alternatives datasets.
• Automated/Machine Learning assisted anomaly detection (if applicable).
- Data Lineage & Metadata
• End-to-end lineage including column-level traceability.
• Metadata management for business, technical, and operational metadata.
• Impact analysis and version-controlled transformation histories.
- Master Data Management (If Applicable)
• Creation and maintenance of golden records for securities, issuers, accounts, funds, positions, performance, risk and other relevant domains.
• Match/merge logic, survivorship rules, and workflows.