Accountable Ownership
Make material reporting data somebody’s explicit responsibility, with decision rights and escalation.
Banking · Regulatory Data Governance
Regulatory Data Governance for Banking That Makes Critical Reporting Data Traceable, Owned and Controlled
DataConsultant helps banks establish accountable governance around the customer, account, transaction, credit, risk, finance and reference data used in regulatory and supervisory reporting. We connect critical-data ownership, business definitions, source-to-report lineage, data-quality and reconciliation controls, issue management, evidence and governance operating models so reporting data can be managed as a sustainable business capability.
✓Critical data ownership and stewardship
✓Source-to-report lineage and transformation traceability
✓Data-quality, reconciliation and exception controls
✓Governance evidence, issue workflow and operating cadence
Applicability of regulatory requirements depends on entity type, jurisdiction, business model and reporting obligations. DataConsultant supports data-governance design and implementation; it does not provide regulatory approval or replace qualified legal, compliance or statutory assurance.
Banking Regulatory Data Control MapGoverned flow
OwnershipOwner · steward · producer · consumer
QualityRules · thresholds · reconciliation
ControlEvidence · exception · sign-off
ChangeImpact · remediation · monitoring
Traceable Lineage
Connect report measures to transformations, data stores, interfaces and banking source systems.
Operable Controls
Define quality, reconciliation, exception and evidence controls around critical data flows.
Stronger Evidence
Create a structured record of definitions, owners, controls, issues, changes and governance decisions.
1
Why Regulatory Data Becomes Difficult to Govern Across a Banking Estate
Regulatory reporting rarely begins in one system or ends with one team. Data may pass through operational platforms, risk engines, finance calculations, integration layers, warehouses, manual adjustments and reporting processes before a return or management report is produced.
Fragmented source-to-report flows
Regulatory and risk reports can depend on core banking, lending, payments, treasury, finance, risk and reference-data systems, with transformations split across platforms and teams.
Definitions drift across functions
Risk, finance, compliance, operations and technology may use similar terms with different calculation logic, granularity, timing, aggregation or reference-data assumptions.
Manual adjustments hide control risk
Spreadsheets, overlays and late corrections can solve an immediate reporting need while weakening repeatability, traceability, evidence and accountable ownership.
Lineage stops at system boundaries
Technical lineage may not explain business calculations, while business documentation may not identify the fields, jobs, interfaces and transformations that produce a number.
Quality exceptions lack accountable closure
A failed data check is not a control outcome unless severity, impact, owner, root cause, remediation, acceptance and recurrence monitoring are defined.
Regulatory change creates data change
New or amended reporting expectations can alter definitions, sources, calculations, controls and evidence. Governance needs a controlled impact-assessment and change path.
Start With the Reporting Flows That Carry the Most Business and Regulatory Risk
Share the reports, recurring data issues, audit or supervisory findings, critical data domains and source-system complexity driving the need. DataConsultant can help define a risk-led regulatory data governance scope.
Direct Definition
What Banking Regulatory Data Governance Actually Covers
Regulatory data governance is the controlled operating model around data that supports supervisory, prudential, risk, finance and compliance reporting. It establishes who owns material data, how the data is defined, where it originates, how it changes, what quality and reconciliation controls apply, how exceptions are resolved, what evidence is retained and how governance decisions are made.
DataConsultant connects those governance requirements to the bank’s real processes and technology estate. The service can begin with a narrow reporting domain or scale to a cross-functional programme spanning multiple reports, legal entities, systems and data domains.
Govern the requirementReporting scope, measure, data requirement, business meaning and accountable report owner.
Govern the dataCritical elements, definitions, ownership, source, reference data, quality and lifecycle.
Govern the flowInterfaces, transformations, calculations, aggregations, reconciliations and lineage.
Govern the evidenceControl execution, exceptions, approvals, changes, remediation and governance records.
2
Move From Report-by-Report Fixes to a Reusable Regulatory Data Control Capability
The target state is a repeatable governance system that can be applied across priority reports and data domains while preserving report-specific accountability and control requirements.
Common current state
- Ownership exists at report level but not for underlying data elements.
- Business definitions and technical logic are stored in separate documents.
- Lineage is incomplete across interfaces, manual steps or transformations.
- Quality checks are inconsistent, duplicated or not tied to materiality.
- Exceptions move through email and spreadsheets without a controlled workflow.
- Evidence is assembled late for review, audit or supervisory requests.
- Regulatory change is translated into data changes reactively.
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Target governed state
- Critical data has accountable business owners and operational stewards.
- Definitions, calculations and control requirements are governed and versioned.
- Source-to-report lineage links business meaning with technical movement.
- Quality and reconciliation controls have thresholds, owners and evidence.
- Issues are prioritised by business impact with traceable remediation and closure.
- Governance evidence is retained as part of normal operations.
- Change impact can identify affected data, controls, systems and reports.
3
Banking Processes and Data Domains That Feed Regulatory and Risk Reporting
The data-control boundary should follow the banking value chain rather than the organisation chart. Operational activity creates data that is transformed, aggregated and consumed by risk, finance and regulatory reporting.
Banking stage
Key data domains
Reporting dependency
Governance focus
Customer and onboarding
Identity, KYC, classification and relationship setup
Identity, KYC, classification and relationship setup
Customer, party, legal entity, address, segment, reference data
Customer and counterparty attribution, aggregation and reporting classifications
Identifiers, completeness, source precedence, reference-data validity and ownership
Accounts and products
Deposits, balances, limits and product structures
Deposits, balances, limits and product structures
Account, product, contract, currency, branch and organisational hierarchy
Balances, classifications, product and entity views used by finance and risk
Product definitions, hierarchy, effective dating, reference data and reconciliation
Lending and credit
Facilities, exposures, collateral and repayment
Facilities, exposures, collateral and repayment
Borrower, facility, exposure, collateral, rating, delinquency and credit risk
Credit exposure, concentration, provisioning and risk aggregation inputs
Critical calculations, rating lineage, collateral values, timeliness and adjustments
Payments and transactions
Movement of funds and transaction processing
Movement of funds and transaction processing
Transaction, payment, counterparty, channel, settlement and currency
Volume, exposure, liquidity, financial-crime and operational reporting inputs
Completeness, duplicates, cut-off, interface control and reconciliation
Treasury and risk
Positions, market data, liquidity and risk measures
Positions, market data, liquidity and risk measures
Instrument, position, market data, liquidity and market/credit/operational risk
Risk aggregation, capital, liquidity and management-risk reporting
Aggregation logic, model/input lineage, reference data, timeliness and scenario data
Finance and close
Accounting, general ledger and consolidation
Accounting, general ledger and consolidation
GL, chart of accounts, entity, cost centre, finance adjustments and consolidation
Financial and prudential measures, reconciliations and submitted returns
Source-to-GL reconciliation, mapping, sign-off, adjustments and evidence
Regulatory reporting
Aggregation, calculation, review and submission
Aggregation, calculation, review and submission
Report, return, metric, critical data element, rule, control and evidence
Supervisory returns, prudential reports and supporting management information
End-to-end lineage, quality, control execution, issue closure and change governance
Customer / PartyIdentity, classification, relationship
Account / ProductBalances, contract and product
Transaction / PaymentMovement, counterparty and settlement
Credit / CollateralFacility, exposure and rating
Risk / TreasuryPositions, liquidity and risk measures
FinanceGL, consolidation and adjustments
Regulatory ReportingReturns, measures, controls and evidence
4
A Banking Regulatory Data Governance Framework From Requirement to Evidence
A useful framework connects the business or regulatory requirement to the data, processing logic, control evidence and accountable decision. This creates a repeatable chain that can be implemented in policies, workflows, catalogues and quality tooling.
1. RequirementReport, measure, risk or supervisory expectation
2. Data requirementMeaning, grain, timing, aggregation and source need
3. Critical dataMaterial elements, definitions and accountable owner
4. Source and lineageSystems, interfaces, transformations and calculations
5. QualityRules, thresholds, reconciliations and exceptions
6. ControlChecks, sign-off and segregation
7. EvidenceExecution record, exception, approval and proof
8. Issue and changeRoot cause, remediation, impact and versioning
9. GovernanceForums, escalation, reporting and improvement
5
Target Architecture: Make Governance Work Across the Existing Banking Technology Estate
DataConsultant does not assume a specific vendor stack. The target architecture defines the information, integration and control capabilities that must work across the bank’s existing applications, data platforms and reporting tools.
1. Source applications
Systems of record and operational applications that create banking data.
Core bankingLendingPaymentsTreasuryRisk enginesERP / GL
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2. Data movement and processing
Interfaces, ingestion, transformations, calculations, mappings, aggregation and reconciliation.
APIs / filesETL / ELTStreamingCalculation logicManual adjustments
→
3. Governed data layer
Curated data, regulatory marts or domain products with governed definitions, quality, metadata and lineage.
WarehouseLakehouseRegulatory martReference dataMetadata / catalog
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4. Reporting and evidence
Regulatory, risk and finance outputs with review, sign-off, control evidence and change history.
Supervisory returnsRisk reportingFinance reportingControl evidenceIssue reporting
Cross-cutting governance and control plane
Business glossary · critical-data inventory · owner/steward model · lineage · data-quality rules · reconciliation · access/classification · control catalogue · issues · evidence · governance forums · change impact · metrics. Where analytical or AI models consume governed regulatory or risk data, provenance and quality should connect to model or AI governance rather than create a separate source of truth.
Need to Trace a Regulatory Report Across Multiple Banking Systems?
DataConsultant can help define the critical-data boundary, lineage depth, reconciliation points, control ownership and tooling requirements before the bank invests in broad catalogue or governance technology.
6
Regulatory Data Governance Capabilities Built Around Banking Reporting Risk
Final scope is tailored to the reports, data domains and control questions in view. These workstreams can be combined into one programme or delivered in focused phases.
Governance operating model
Define report ownership, domain ownership, stewardship, control ownership, forums, decision rights and escalation.
- RACI and responsibility boundaries
- Governance forums and cadence
- Policy-to-workflow mapping
Critical data and glossary
Identify material reporting data and standardise definitions, calculation context, source meaning and accountability.
- Critical data element inventory
- Business glossary and taxonomy
- Data criticality criteria
Source-to-report lineage
Trace business and technical lineage through systems, interfaces, mappings, calculations, aggregation and manual intervention.
- Business and technical lineage
- Transformation evidence
- Impact analysis
Quality and reconciliation
Translate data expectations into measurable rules, thresholds, reconciliations and controlled exceptions.
- Rule and threshold catalogue
- Source-to-target reconciliation
- Exception severity and ownership
Data controls and evidence
Connect control objectives to execution logic, preventive or detective checks, evidence, sign-off and assurance needs.
- Control catalogue
- Evidence requirements
- Control monitoring design
Issue and remediation governance
Create a controlled path from data exception to impact assessment, root cause, corrective action, validation and closure.
- Issue taxonomy and severity
- Accountable remediation
- Recurring-problem analysis
Tooling and architecture requirements
Define requirements for catalogues, lineage, quality, workflow, reporting and integration without forcing a predetermined platform.
- Capability and integration requirements
- Tool rationalisation
- Implementation architecture
Regulatory change and sustainability
Design a repeatable process to assess changes, update data requirements, revalidate controls and maintain governance artefacts.
- Change-impact workflow
- Operating metrics
- Continuous-improvement backlog
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Regulatory Context: Map Applicable Expectations to Data, Controls and Evidence
Regulatory data governance should be grounded in obligations that apply to the specific bank or regulated entity. These are examples of authoritative context relevant to Indian banking and risk-data governance; applicability must be confirmed for the organisation.
IT Governance, Risk, Controls and Assurance Practices
RBI’s 2023 Master Direction applies to specified regulated entities and covers IT governance, risk, controls, assurance, data migration controls, audit trails and related oversight. Regulatory data governance should align ownership and data controls with the bank’s applicable IT governance framework.
Review the RBI Master Direction ↗Filing of Supervisory Returns Directions, 2024
RBI consolidated instructions for supervisory returns for specified supervised entities. A governance programme can connect reporting requirements to critical data, source-to-report lineage, quality, reconciliation, sign-off and retained evidence.
Review the RBI Directions ↗BCBS 239 Risk Data Aggregation and Reporting
BCBS 239 establishes principles for governance, data architecture, accuracy, completeness, timeliness, adaptability and risk reporting, particularly for systemically important banks and as applied by relevant supervisors.
Review the Basel Committee principles ↗Digital Personal Data Protection Framework
Where regulatory data contains personal data, India’s DPDP Act and the 2025 Rules may create additional governance considerations, subject to applicable provisions and the implementation timeline.
Review MeitY’s DPDP Rules page ↗Boundary of service: DataConsultant helps translate confirmed obligations and supervisory expectations into data-governance capabilities, controls and evidence. The bank remains responsible for determining applicability, regulatory interpretation, formal compliance positions, statutory assurance and regulatory submissions.
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A Target Operating Model That Puts Regulatory Data Decisions With Accountable Roles
The capability becomes sustainable when responsibility for data, controls, issues and changes is embedded into normal banking governance rather than held by a temporary project team.
Executive / regulatory reporting sponsor
Sets the governance mandate, resolves cross-functional decisions, approves priorities and ensures required business and technology participation.
Report and data owners
Own reporting purpose, critical definitions, materiality, data acceptance, control expectations, issues and business sign-off.
Data stewards and producers
Maintain definitions and metadata, operate quality processes, investigate exceptions and coordinate remediation with source teams.
Risk, finance, compliance and control owners
Define control objectives, challenge evidence, assess exceptions and ensure decisions follow the bank’s risk and compliance model.
Architecture and engineering
Own technical lineage, interfaces, transformations, data-platform implementation, automation and technical remediation.
Data governance office
Maintains standards, critical-data methods, forums, metrics, escalation, policy alignment and cross-domain consistency.
Independent assurance / audit
Provides independent challenge or assurance according to the bank’s governance model; it should not own the first-line controls it reviews.
Programme and change leadership
Coordinates roadmap delivery, dependencies, adoption, training, tool changes and transition into business-as-usual operations.
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How DataConsultant Delivers Regulatory Data Governance From Discovery to Operationalisation
The engagement follows reporting data from business requirement to source, transformation, control, evidence and operating ownership. Depth is adjusted to the scope and maturity of the bank.
Scope
Confirm reports, entities, domains, risk drivers, stakeholders, evidence, exclusions and required decisions.
Discover
Inventory reports, critical measures, source systems, calculations, owners, controls, issues and current artefacts.
Trace
Map critical data, definitions, business and technical lineage, transformations, manual steps and dependencies.
Assess Controls
Review quality rules, reconciliations, control evidence, issue handling, access and change governance.
Design
Define target ownership, stewardship, governance forums, standards, control model, architecture and metrics.
Mobilise
Prioritise gaps, create workstreams, owners, dependencies, implementation backlog and transition plan.
Implement and Operate
Support rollout, tooling, controls, adoption, governance operations, reporting and continuous improvement as scoped.
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Tangible Deliverables for Banking Data Owners, Reporting Teams and Control Functions
Deliverables are selected according to the agreed reporting and implementation boundary. The aim is to create artefacts that can be used to make decisions, implement controls and operate governance after the engagement.
DELIVERABLE 01
Regulatory data landscape
Priority reports, processes, data domains, systems, producers, consumers and governance boundaries.
DELIVERABLE 02
Critical-data inventory and glossary
Material data elements, definitions, criticality rationale, owners and reporting context.
DELIVERABLE 03
Ownership and RACI model
Report owners, data owners, stewards, producers, control owners and escalation responsibilities.
DELIVERABLE 04
Source-to-report lineage
Business and technical flow across source, interfaces, transformations, calculations and reporting outputs.
DELIVERABLE 05
Quality and reconciliation catalogue
Rules, dimensions, thresholds, reconciliations, severity, ownership and monitoring requirements.
DELIVERABLE 06
Control and evidence model
Control objectives, preventive/detective checks, execution evidence, sign-off and exception treatment.
DELIVERABLE 07
Issue and remediation workflow
Triage, materiality, root cause, action ownership, acceptance, closure and recurrence monitoring.
DELIVERABLE 08
Target operating model
Governance forums, roles, decision rights, service boundaries, cadence, metrics and escalation.
DELIVERABLE 09
Architecture and tooling requirements
Catalogue, lineage, quality, workflow, reporting, integration and automation requirements.
DELIVERABLE 10
Roadmap and executive decision pack
Priorities, dependencies, owners, implementation backlog, decisions, limitations and mobilisation actions.
Turn Regulatory Data Governance Design Into Operable Controls and Ownership
DataConsultant can support mobilisation, lineage and metadata enablement, data-quality controls, issue workflows, governance forums, implementation assurance and handover so the target model does not stop at a framework document.
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Implementation and Ongoing Operations: Design, Mobilise, Operate and Improve
Implementation support is scoped separately from assessment or design. DataConsultant can work with bank teams, platform vendors and systems integrators while keeping ownership, acceptance criteria and responsibility boundaries explicit.
Design
Confirm the target operating model, standards, critical-data method, lineage and quality requirements, controls, workflow and architecture.
Mobilise
Create workstreams, governance forums, owners, implementation backlog, dependencies, tool decisions and acceptance criteria.
Implement
Support ownership rollout, metadata, lineage, quality rules, reconciliations, control evidence, issue workflow and reporting.
Operate
Run or support governance cadence, stewardship, quality monitoring, issue triage, evidence coordination and change assessment.
Improve
Analyse recurring defects, control performance, adoption, lineage gaps and process friction to maintain an improvement backlog.
Scale / Transfer
Extend proven patterns to additional reports or domains and transfer methods, runbooks and capability to accountable internal teams.
Client Readiness
What DataConsultant May Need From the Bank
Regulatory data governance is evidence-led. The engagement can begin even when documentation is incomplete, but missing evidence should be recorded as a limitation and converted into a discovery or remediation action rather than silently assumed.
Scope boundary: legal interpretation, regulatory sign-off, statutory audit, model validation, penetration testing and vendor product implementation are not automatically included unless explicitly commissioned through appropriately qualified parties.
Reporting inventory and prioritiesPriority supervisory, prudential, finance or risk reports, material measures and known pain points.
Accountable stakeholdersReport owners, risk, finance, compliance, data owners, stewards, architecture, engineering and controls.
Architecture and system evidenceSource-system inventory, data flows, interface diagrams, calculation logic, warehouses and reporting platforms.
Definitions and metadataBusiness glossaries, data dictionaries, critical-data registers, existing lineage and reference-data rules.
Quality and reconciliation evidenceRules, scorecards, exceptions, reconciliations, manual adjustments and known recurring defects.
Controls and assurance findingsPolicies, control inventories, audit findings, issue logs, supervisory observations and remediation plans where shareable.
Regulatory contextApplicable obligations and internal compliance interpretations confirmed by the bank’s accountable functions.
Tooling and change landscapeCatalogues, quality tools, workflow platforms, active transformations, migrations and vendor dependencies.
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Commercial Scope Is Based on the Reporting Boundary, Data Complexity and Implementation Depth
DataConsultant does not publish a fixed price or standard duration for this service. Each engagement is quoted after the reporting scope, data domains, systems, control requirements, stakeholder model and implementation responsibilities are understood.
Regulatory Data Diagnostic
For a bank that needs an evidence-based view of critical gaps, priority reports and the right governance response before a larger programme.
Request a Quote
- Current-state discovery
- Priority report/data scope
- Ownership, lineage and control gap view
- Prioritised recommendations
- Executive findings
Governance Framework and Roadmap
For banks that need a complete target model linking critical data, lineage, quality, controls, evidence, governance forums and implementation priorities.
Request a Quote
- Critical-data and ownership model
- Lineage and metadata requirements
- Quality/control framework
- Target operating model
- Implementation roadmap
Implementation and Mobilisation
For organisations moving from an approved design into governance rollout, tool enablement, controls, workflows, reporting and adoption.
Request a Quote
- Programme mobilisation
- Ownership/stewardship rollout
- Metadata, lineage and quality enablement
- Control and issue workflow setup
- Implementation assurance
Managed Governance Operations
For banks that need continuing operational support around governance cadence, stewardship, quality exceptions, evidence and change.
Request a Quote
- Governance forum support
- Metadata and critical-data maintenance
- Quality monitoring and issue triage
- Evidence and change coordination
- Continuous-improvement backlog
What affects price and timeline?
Number of legal entities and business units; reports in scope; banking processes and data domains; source systems and interfaces; critical data elements; lineage depth; calculation and transformation complexity; quality and reconciliation controls; manual adjustments; evidence requirements; stakeholder groups and workshops; tooling environment; remediation depth; implementation work; change and training needs; onsite requirements; managed-service boundary; and required delivery schedule. Timeline is confirmed after scoping.
Good fit for this service
- Regulatory or risk reporting depends on data from multiple systems and functions.
- Critical data ownership or stewardship is unclear or inconsistent.
- Lineage, quality, reconciliation or evidence gaps are recurring.
- Audit, supervisory or internal control findings point to systemic data-governance weaknesses.
- A reporting transformation or platform change needs governance and control by design.
- The bank wants a repeatable operating capability, not only a one-off report fix.
May require a different or additional service
- The need is solely legal interpretation of a regulation or formal compliance sign-off.
- A single isolated data defect has a known cause and only needs technical correction.
- The requirement is statutory audit, certification, model validation or penetration testing.
- The organisation wants a specific software product installed without governance or business-design work.
- No accountable sponsor or report/data owners can make cross-functional decisions.
- The primary need is broad enterprise data strategy rather than regulatory reporting data governance.
Get a Regulatory Data Governance Scope Built Around Your Actual Reports and Systems
Share the reports or data domains in view, key systems, legal entities, known control gaps and the implementation depth required. DataConsultant can prepare a scope-led proposal without inventing a one-size-fits-all package.
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Why Consider DataConsultant for Banking Regulatory Data Governance
Credibility for this work should come from the quality of the governance design, architecture logic, traceability, control model and implementation path—not unsupported claims or generic transformation language.
Reporting-to-data thinking
Start from the report, measure, business decision or control need and follow the data backwards through calculation, transformation and source.
Business ownership by design
Separate accountable business decisions from technical implementation while making hand-offs and escalation explicit.
Lineage connected to control
Treat lineage as an operating control and impact-analysis capability, not merely a diagram or catalogue feature.
Quality tied to materiality
Design rules, thresholds, reconciliation and exception management around business impact and reporting purpose.
Architecture plus operating model
Connect governance methods to the bank’s actual applications, data platform, workflow and tool environment.
Implementation continuity
Carry the design into mobilisation, control implementation, adoption, operational handover and ongoing improvement when scoped.
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Banking Regulatory Data Governance FAQs
Answers to common buyer questions about banking data domains, critical data, lineage, controls, regulatory context, implementation, operations, timeline and pricing.
What is regulatory data governance in banking?
Regulatory data governance in banking is the operating discipline used to assign accountability for data that supports supervisory, risk, finance and compliance reporting; define critical data and business meaning; trace source-to-report lineage; establish quality and reconciliation controls; manage exceptions and changes; and retain evidence showing how material data was produced and governed. The exact model should reflect the bank’s entity type, jurisdiction, reporting obligations, risk profile and technology estate.
What does DataConsultant’s Regulatory Data Governance service include?
A scoped engagement can include regulatory-report and data-flow discovery, critical data element identification, ownership and stewardship design, glossary and metadata requirements, source-to-report lineage, data-quality and reconciliation controls, issue and remediation workflows, evidence requirements, governance forums, target operating model, tooling requirements and an implementation roadmap. Final scope is agreed after discovery.
Which banking data domains are usually relevant?
Priority domains depend on the reporting and risk context. Common examples include customer and party, account, product, transaction and payment, lending and credit, collateral, treasury and market data, liquidity, risk, finance and general ledger, legal-entity and reference data, and regulatory reporting measures derived from those domains.
How do you identify critical data elements for regulatory reporting?
The work starts from material reporting requirements, measures, calculations and control objectives, then traces the data required to produce them. Candidate critical data elements are assessed using regulatory or risk significance, downstream dependency, business impact, transformation complexity, data-quality exposure and the consequence of error or delay. Owners and subject-matter experts validate the inventory.
Can DataConsultant create source-to-report data lineage?
Yes. Lineage can be scoped from regulatory or risk reports back through calculations, transformations, data stores, interfaces and source applications. The required depth may range from business lineage to technical field-level lineage. Existing catalogues and lineage tools can be used where available; otherwise a phased capture approach can be defined.
How are data quality and reconciliation handled?
DataConsultant can help define quality dimensions, business rules, thresholds, preventive and detective checks, source-to-target reconciliations, exception severity, accountable owners, remediation workflows and monitoring. Controls should be tied to the purpose and materiality of the data rather than applying one threshold to every dataset.
Does this service guarantee regulatory compliance?
No. The service supports regulatory-data governance and readiness by helping organisations map applicable requirements to data ownership, flows, controls and evidence. It does not replace legal advice, regulatory interpretation by accountable compliance teams, statutory audit or supervisory approval. Applicability and sign-off remain with the bank and its appropriately qualified advisers.
How do RBI requirements and BCBS 239 relate to the engagement?
They can provide important context where applicable. RBI directions address matters such as IT governance, controls, assurance and supervisory returns for specified regulated or supervised entities. BCBS 239 sets principles for effective risk data aggregation and risk reporting, particularly for systemically important banks and as applied by relevant supervisors. The engagement maps only requirements confirmed as applicable to the client.
How are privacy and security requirements incorporated?
The governance design can incorporate data classification, purpose and minimisation considerations, access ownership, retention, sharing, residency, encryption and security-control dependencies. Personal-data obligations such as India’s DPDP framework may be relevant depending on the data handled and applicable implementation requirements.
Can you work with our existing banking systems and data platforms?
Yes. The approach is requirements-led and can work across core banking, lending, payments, treasury, risk, finance, data warehouse or lakehouse, integration, BI, metadata, data-quality and workflow environments. DataConsultant does not assume a particular vendor stack.
What deliverables can we expect?
Typical deliverables can include a regulatory data landscape, critical-data inventory and glossary, ownership and RACI model, source-to-report lineage, quality and reconciliation control catalogue, issue and remediation workflow, governance operating model, architecture and tooling requirements, implementation backlog and roadmap, and an executive decision pack.
Can DataConsultant implement the governance design?
Implementation support can be scoped separately. It may include governance mobilisation, ownership and stewardship rollout, metadata and lineage enablement, quality-control implementation, workflow configuration advisory, issue-management setup, reporting and scorecards, architecture support, implementation assurance, knowledge transfer and change adoption.
Can DataConsultant provide ongoing regulatory data governance support?
Yes, where required. Ongoing support can cover governance forums, stewardship operations, critical-data inventory maintenance, lineage and metadata upkeep, quality monitoring, issue triage, evidence coordination, control reporting, change impact assessment and continuous-improvement backlogs.
How long does a Regulatory Data Governance engagement take?
Timeline is confirmed after scoping. It depends on the number of regulatory or risk reports, business units and legal entities, data domains, source systems, lineage depth, critical data elements, control inventory, evidence quality, stakeholder availability, tooling, remediation depth and whether implementation or managed operations are included.
How is Regulatory Data Governance pricing determined?
DataConsultant does not publish a fixed price for this service. Commercial scope is based on the reporting and data boundary, legal entities, domains, systems, critical data elements, lineage depth, controls, workshops, regulatory context, deliverables, implementation responsibilities, onsite needs, tooling dependencies and ongoing support required. A written quote is prepared after the scope is understood.
Regulatory Data Governance Enquiry
Request a Banking Regulatory Data Governance Scope Review
Share your contact details and high-level requirement. DataConsultant can review the likely scope, evidence needed, stakeholder involvement and appropriate next step.