Data Management

Reference data spans multiple interconnected categories, all of which must be synchronised to ensure consistency and accuracy:

Reference Data

• Unique instrument identifiers (ISIN, CUSIP, FIGI, SEDOL)
• Issuer metadata including legal name, sector, domicile, and ownership hierarchy
• Instrument attributes such as asset class, currency, and trading status

Market Data

• End-of-day and intraday pricing data
• Independent price verification (IPV) sources and vendor hierarchies
• Fair value levels and pricing models across jurisdictions and asset types

ESG & Ratings Data

• ESG performance metrics (e.g. carbon intensity, board diversity, human rights controversies)
• Credit ratings from global rating agencies (e.g. S&P, Moody’s, Fitch)
• Mapped rating scales and consensus ratings across providers

Index & ETF Data

• Benchmark index membership and eligibility criteria
• ETF constituent holdings with full look-through capabilities
• Sector, factor, and geographic classifications aligned with index methodologies

Risk Data

• Historical time series data for risk factor modeling
• Interest rate curves, volatility surfaces, and other derivatives inputs
• Derived values for stress testing and capital adequacy models

Corporate Actions

• Mastered event data (e.g. dividends, splits, redemptions, mergers)
• Impacted instruments and related reference updates
• Effective dates and pricing adjustments for portfolios and NAV calculations

Trade & Transaction Data

• Executed trade and lifecycle event records across asset classes, including timestamps, venues, and counterparties for compliance, reconciliation, and analytics

These datasets are used by multiple downstream systems, from trading platforms to risk engines and regulatory reporting frameworks. Within the security master, the volume and diversity of data required can be vast — ranging from simple identifiers to thousands of data points for structured products like MBS, CDOs, or CMOs.

Across both the sell side and buy side, reference data is foundational to operational efficiency, regulatory compliance, and competitive performance, while often shared requirements we see high quality reference data supporting:

On the sell side:

• Ensures accurate order validation and trade routing
• Supports real-time risk aggregation by linking instruments to issuers, ratings, and classifications
• Drives transaction reporting under MiFID II, SFTR, EMIR, and other regimes
• Enables fast corporate action processing, reducing risk in intraday valuation and P&L reporting

On the buy side:

• Builds portfolio hierarchies that reflect fund structures, index strategies, or ESG mandates
• Enables exposure tracking across country, sector, credit rating, or ESG dimension
• Powers NAV calculations and compliance checks, with accurate reference points for instruments and issuers
• Aggregates positions and classifications for client, regulatory, and board-level reporting

In both environments, managing the interdependencies between instruments, issuers, benchmarks, events, and risk factors is what makes reference data genuinely fit for purpose.

Regulatory frameworks increasingly require financial institutions to submit standardised, high-quality, and traceable data. Reference data is often at the heart of these obligations.

Key regulations include:

• MiFID II: Accurate use of ISINs, LEIs, MICs, and CFI codes in trade and transaction reports
• EMIR & SFTR: Granular trade and product reporting, requiring alignment across counterparty and instrument datasets
• FRTB: Categorisation of instruments impacts capital treatment and internal model eligibility
• AIFMD, Form PF, Basel: Require structured exposure reporting based on credit ratings, asset class, region, and sector

Poor quality reference data not only results in reporting errors — it can lead to audits, reputational damage, and regulatory fines. As such, reference data management is now a key pillar of enterprise data governance.

At Gresham, our approach to reference data management is built on three principles: connectivity, control, and confidence. Using our Prime EDM platform, financial institutions can master their reference data across all asset classes and operational domains — all within a fully normalised, extensible data model designed to eliminate fragmentation and duplication.

We help firms:

• Ingest and harmonise data from exchanges, vendors (e.g. Bloomberg, Refinitiv), public feeds, and internal systems into a common model
• Enrich records by linking instruments to issuers, credit ratings, ESG scores, benchmarks, and risk factors
• Automate validations and reconciliation, reducing manual intervention and exception handling
• Track lineage and change history, ensuring auditability and transparency for compliance teams
• Empower data owners, enabling business users to configure rules and onboard new sources with low IT dependency
• Distribute golden-source data through APIs, real-time dashboards, or scheduled batch outputs to all consuming systems

By leveraging a prebuilt, industry-aligned data model, Gresham helps firms avoid reinventing the wheel — enabling faster deployment, greater consistency, and a truly scalable foundation for enterprise-wide data integrity.

• Fewer trade breaks and failed settlements
• Faster onboarding of new instruments, data sources, and regulations
• Improved risk and performance analytics through consistent, validated data
• Streamlined reporting to regulators, clients, and internal stakeholders
• Enhanced governance and operational resilience

In today’s data-driven markets, reference data is not a back-office concern — it’s a strategic enabler of financial stability, growth, and agility.