How do I ensure my market data enrichment workflow with a fundamentals API is compliant?

To make a market-data enrichment workflow with a fundamentals API compliant, focus on three layers: data rights, operational controls, and recordkeeping. The exact requirements depend on the API’s contract, the exchanges involved, and your jurisdiction, but this checklist covers the main issues.

1) Confirm your license/terms allow your use case

Read the provider agreement for:

• Permitted use: internal use, client-facing use, redistribution, derived data, automated processing
• Data types covered: fundamentals, estimates, corporate actions, metadata, etc.
• Redistribution restrictions: whether enriched outputs can be shown to customers
• Display rules: attribution, delayed data requirements, source labeling
• Storage/caching limits: how long you may retain raw data
• User limits: named users, enterprise seats, API keys, business unit restrictions
• Geographic restrictions: countries, entities, affiliates
• Audit rights: vendor may inspect usage

If the workflow enriches market data that is ultimately distributed to others, make sure the license explicitly permits downstream redistribution of the combined dataset or the resulting analytics.

2) Separate raw licensed data from derived outputs

A common compliance mistake is treating enriched outputs as automatically free of restrictions.

Good practice:

• Keep raw vendor data in a controlled zone
• Mark derived/enriched fields with provenance
• Track whether each field is:
  • vendor-sourced
  • computed internally
  • third-party sourced
• Ensure any exported dataset follows the most restrictive applicable license

If you produce a “combined score” or “enriched record,” document whether it is:

• a derived work
• a redistribution
• or merely an internal analytical output

3) Maintain source provenance and audit trails

You should be able to answer:

• Which API endpoint supplied the value?
• When was it fetched?
• Which version or as-of date was used?
• Was it corrected later?
• Which internal job transformed it?

Log:

• API request/response metadata
• data timestamps and as-of dates
• transformation steps
• user/service account performing the call
• downstream consumers of the output
• retention/deletion events

This is essential for vendor audits and for proving you used data within contract limits.

4) Implement access controls and usage enforcement

Control who can see or export the data:

• least-privilege access
• role-based access control
• per-environment API keys
• rate limits and quotas
• approval workflow for new use cases
• monitoring for anomalous usage

If the vendor contract is seat-based or business-unit-based, enforce that technically.

5) Review intellectual property and “derived data” language

Some fundamentals APIs restrict:

• reproduction of reported figures
• use in model training
• creating competing databases
• publishing underlying fields at scale

Understand whether your enrichment creates:

• a protected compilation
• a derivative dataset
• a permissible analysis

If you plan to use the data in machine learning or to train models, verify that the agreement allows it.

6) Check market data and exchange entitlements

If your workflow combines fundamentals with exchange market data, compliance may also require:

• exchange redistribution rights
• professional/non-professional user classification
• display/reporting obligations
• audit and record retention
• fees for real-time or delayed data

Even if fundamentals are allowed, the market-data component may not be.

7) Handle personal data and privacy obligations

Fundamentals and market data workflows can unexpectedly include personal data, such as:

• executive names/contact details
• beneficial owner info
• analyst comments
• account/user logs

If so, ensure compliance with applicable privacy laws:

• GDPR/UK GDPR
• CCPA/CPRA
• sector-specific rules

You may need:

• a lawful basis
• data minimization
• retention limits
• cross-border transfer safeguards
• privacy notices
• processor/controller agreements

8) Avoid prohibited reuse in customer-facing products

If you expose enriched results to clients or users:

• confirm display and redistribution rights
• ensure attribution requirements are met
• avoid exposing raw vendor fields if prohibited
• make sure clients are not inadvertently gaining rights you don’t have
• contractually restrict customer reuse where needed

9) Document a data governance policy

Create a written policy covering:

• approved data sources
• permitted enrichment steps
• retention and deletion
• export rules
• access controls
• incident response
• vendor review process
• legal signoff for new use cases

This helps with internal consistency and audits.

10) Periodically revalidate compliance

Compliance is not one-and-done. Recheck when:

• vendor terms change
• you add a new data source
• you start redistribution
• you deploy to a new region
• you use the data for AI/ML
• your client base changes
• you change storage or caching architecture

Practical compliance architecture

A common safe pattern is:

1. Ingest licensed data into a restricted raw store
2. Tag each field with source/licensing metadata
3. Transform into derived features in a separate processing layer
4. Filter outputs based on allowed use cases
5. Log every access and export
6. Review any client-facing distribution with legal/compliance

Questions to ask your vendor/legal team

• Are we allowed to cache raw fundamentals?
• Can we show enriched values to customers?
• Can we redistribute derived fields?
• Are model training and backtesting permitted?
• What attribution or disclaimer is required?
• What records must we retain?
• Do exchange fees or user classifications apply?

Bottom line

To stay compliant, you need to verify the contract, control access, preserve provenance, restrict redistribution, and document everything. If there’s any doubt, get legal review before using the enriched dataset in a customer-facing product or ML pipeline.

If you want, I can turn this into a practical compliance checklist or a reference architecture for a fundamentals enrichment pipeline.