Multi-Agency Data Governance: Managing Overlapping Regulatory Requirements
Banks increasingly face a difficult data governance challenge: different regulators can require overlapping information, but each may define, structure, or report that information differently. Without a coordinated framework, compliance teams can end up collecting the same data multiple times, maintaining conflicting definitions, and manually reconciling reports.
This is where multi-agency data governance becomes critical.
Consider a regional bank responding to requirements from agencies such as the FDIC, NYDFS, and SEC. Each regulatory regime may have its own reporting scope, data fields, definitions, and evidence requirements. The result can be a fragmented governance environment in which the same underlying data must support several regulatory processes.
The hidden risk is not simply regulatory complexity. It is the lack of a common data foundation capable of supporting multiple regulatory obligations without sacrificing consistency, traceability, or auditability.
The Problem: Siloed Data Governance Meets Overlapping Regulations
Banks often organize compliance processes around individual regulations or regulatory agencies. That approach can work when requirements are isolated, but it becomes harder to manage when multiple rules depend on related data.
For example, SEC Rule 605 governs public reporting of order-execution information for covered orders in NMS stocks. The SEC's 2024 amendments expanded the scope of reporting entities and modified the information and statistics required in execution-quality reports. The compliance date for those amendments was subsequently extended to August 1, 2026.
Similar challenges can arise when banks must simultaneously manage consumer, lending, licensing, risk, and transaction data for other regulatory programs.
A siloed approach creates three major problems:
1. Duplicative Data Capture
Different compliance teams may collect the same underlying attributes for separate regulatory processes. This increases manual work and creates additional opportunities for data-entry errors.
2. Inconsistent Data Definitions
The same business concept may be represented differently across systems. Without a canonical data model, teams can produce reports that are technically complete but difficult to reconcile.
3. Regulatory Change Gaps
When a regulator changes a reporting requirement, compliance teams may need to determine which systems, data pipelines, controls, and reports are affected.
This makes regulatory change management a data-governance problem as much as a compliance problem.
Why Multi-Agency Data Governance Matters
A strong multi-agency data governance framework creates a common layer between enterprise data and individual regulatory reporting requirements.
Instead of maintaining independent data pipelines for every regulator, banks can establish a canonical representation of important data elements and map regulatory requirements back to that foundation.
A coordinated framework can help organizations establish:
- Consistent data definitions
- Centralized data ownership
- End-to-end data lineage
- Reusable regulatory data
- Automated validation
- Version-controlled regulatory mappings
- Audit-ready evidence
- Faster regulatory change impact analysis
This approach does not mean every regulator receives the same report. Instead, it means each report can be generated from a trusted, governed source of underlying data.
The CoComply Approach to Multi-Agency Data Governance
CoComply addresses this challenge with a continuous, AI-driven certification layer designed to connect regulatory requirements with a common data model.
The platform can ingest regulatory schemas and reporting requirements, map them to enterprise data elements, and identify relationships between overlapping requirements.
AI agents can then help:
- Map regulatory requirements to the relevant data elements.
- Identify overlapping attributes across regulatory programs.
- Trace data lineage from source systems to regulatory reports.
- Detect changes when requirements or schemas are updated.
- Surface impact analysis across affected data pipelines.
- Generate evidence supporting governance and compliance controls.
- Re-certify governed data as requirements and source data change.
For SEC Rule 605 specifically, the regulatory framework includes detailed execution-quality information and reporting requirements. The SEC's 2026 FAQs address issues including timestamping and execution-quality statistics, reinforcing the importance of precise, consistently governed data.
The goal is not simply to automate reporting. It is to establish a traceable connection between regulatory requirements, source data, governance controls, and the final report.
Enhancing Compliance Through Better Data Governance
A multi-agency approach can deliver value beyond regulatory reporting.
Automated Data Lineage
Data lineage can show where each reported data element originated, how it was transformed, and where it ultimately appears in a regulatory submission.
Continuous Validation
Automated controls can identify changes or inconsistencies before they become reporting problems.
Regulatory Change Management
When a rule changes, compliance teams can identify affected data elements, pipelines, controls, and reports instead of manually reviewing every system.
Centralized Evidence
A common evidence layer can make it easier for compliance teams and auditors to understand how regulatory reports were produced.
Better Executive Visibility
Governance dashboards can provide leadership with a clearer view of data quality, regulatory exposure, control effectiveness, and remediation priorities.
Together, these capabilities help shift data governance from a collection of disconnected compliance processes toward an enterprise-wide control framework.
Building a Cross-Regulatory Data Governance Framework
Banks looking to reduce regulatory complexity can start with five practical steps.
1. Inventory Regulatory Data Requirements
Create a centralized inventory of the data elements required by each relevant regulator and reporting obligation.
2. Establish a Canonical Data Model
Identify common business definitions and establish authoritative sources for critical data elements.
3. Map Regulations to Data Lineage
Connect every material regulatory data element to its source, transformations, controls, and downstream reports.
4. Automate Regulatory Change Impact Analysis
Use automated monitoring to identify which data assets and reporting processes could be affected when regulatory requirements change.
5. Continuously Certify Critical Data
Move beyond point-in-time validation by continuously testing whether governed data remains accurate, complete, consistent, and traceable.
Turning Regulatory Complexity Into a Strategic Advantage
Multi-agency data governance can help banks turn regulatory complexity into a more manageable and scalable operating model.
Instead of creating separate governance processes for every regulator, institutions can build a shared data foundation that supports multiple reporting obligations while preserving each regulator's specific requirements.
For banks, the objective is not simply to produce more compliance reports. It is to create trusted, traceable, and reusable data that can support regulatory reporting, risk management, operational decisions, and future products.
As regulatory reporting becomes more data-intensive, a unified data governance framework can give financial institutions greater control over data lineage, regulatory change, and audit readiness.
For organizations evaluating this approach, CoComply's AI-enabled certification layer provides a way to connect regulatory requirements, governed data, and continuous evidence generation within a single framework.
For additional background on the SEC's Rule 605 amendments, consult the SEC's official 2024-32 press release and its subsequent Rule 605 implementation guidance.