1. Identify and Catalog All Data Sources

Establish a comprehensive inventory of all sources contributing to the security reference data. These include:

• Market Data: Real-time and historical data from exchanges (e.g., pricing, volumes, corporate actions).
• Internal Data: Proprietary analytics, pricing models, analyst notes, and derived attributes.
• Derived Data: Calculated values like yield, duration, or risk metrics.
• Override Data: Adjusted data entries, typically user-defined or exception-based (e.g., overriding vendor data).
• Manual Data: Ad hoc or one-time entries not sourced from automated feeds (e.g., private deals, new issues).

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2. Identify Consumers and Use Cases for the Data

Determine the internal stakeholders and systems that rely on reference data, such as:

• Portfolio managers and traders
• Risk and compliance teams
• Operations and reconciliation teams
• Regulatory reporting systems
• Performance and attribution platforms

Each consumer may require different timeliness, formats, and completeness.

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3. Define and Classify the Types of Reference Attributes

Group and define the data fields that describe each instrument. These may include:

• Prices and Analytics: Last price, bid/ask, yields, durations.
• Classification: Asset class, sector, industry, instrument subtype.
• Identifiers: ISIN, CUSIP, SEDOL, internal IDs, exchange codes.
• Ratings: Credit ratings from agencies (S&P, Moody’s, Fitch).
• Countries and Regions: Country of issuance, risk country, trading venue.
• Reference Data: Ticker, name, currency, issue date, maturity, coupon.

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4. Capture Issuer, Counterparty, and Company Hierarchy Data

Include legal entity hierarchies and relationships:

• Link securities to issuers, guarantors, counterparties
• Maintain parent-subsidiary relationships
• Identify ultimate parents for risk aggregation (important for credit and regulatory reporting)
• Track changes due to mergers, acquisitions, spin-offs

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5. Define and Standardize Instrument Types

Clearly categorize all instruments into consistent types to enable rule-based processing:

• Equities, Corporate Bonds, Government Bonds, Derivatives, ETFs, Structured Products, Loans, etc.

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6. Create Instrument Identification and Classification Rules

Define rules for categorizing and validating securities:

• Use issuer type, maturity, coupon, asset class to determine instrument category
• Develop normalization logic for naming conventions
• Handle complex products (convertibles, hybrids) carefully
• Establish fallback rules when data is missing or ambiguous

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7. Design New Security Onboarding and Update Processes

Create a structured workflow for how new securities are created and maintained:

• Intake via automated feeds, manual entry, or request forms
• Validation and enrichment
• Real-time (intraday) updates vs. batch (overnight) processing
• Versioning and audit trails
• Exception handling and approval workflow

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8. Integrate External Data Providers and Enrichment Processes

Manage data flow from vendors and enrich internal records:

• Integrate with Bloomberg, Refinitiv, ICE, IDC, etc.
• Configure intra-day and overnight refresh schedules
• Map vendor fields to internal schema
• Set data precedence and rules for conflict resolution

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9. Match and Reconcile Securities Across Multiple Sources

Ensure consistent identification across vendors and systems:

• Use fuzzy matching and rules-based alignment (e.g., ISIN + Ticker)
• Reconcile discrepancies across feeds
• Merge duplicate entries
• Flag unmatched or low-confidence records for manual review

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10. Define Data Source Hierarchy and Prioritization Rules

Establish rules for which source is considered authoritative for each field or instrument type:

• E.g., Bloomberg for equity names, IDC for bond pricing
• Configure overrides and fallbacks
• Create a metadata structure for traceability (e.g., “source of truth” tags)

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11. Build a Master Security Data Model and Repository

Create a centralized, scalable, and flexible data architecture:

• Normalize data schemas
• Support relational links (issuer → security, security → corporate action)
• Design for extensibility (new fields, instruments)
• Enable cross-referencing and lineage tracking

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12. Implement Governance, Validation, and Quality Controls

Define the governance processes to ensure accuracy, consistency, and compliance:

• Set ownership of data domains
• Run regular data quality checks and exception reports
• Track changes and approvals
• Conduct periodic audits

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13. Enable Integration with Downstream Systems and Reporting

Design APIs, ETL pipelines, or direct database access to support data delivery:

• Real-time and batch interfaces
• Custom formats (e.g., XML, JSON, CSV) for different consumers
• Secure access and permissioning
• Support for audit and regulatory traceability