Product Velocity
New journeys, features and partner integrations can create data definitions and responsibilities faster than informal governance can absorb.
Fintech · Data Governance Office
Fintech Data Governance Office for Accountable, Scalable Data
DataConsultant helps fintech organisations design and mobilise a practical governance office across digital onboarding, payments, lending, fraud and risk, finance, partner APIs, analytics and AI. We connect domain ownership, stewardship, critical data, quality, metadata, lineage, privacy, controls and decision forums so governance can keep pace with product change without becoming a policy-only layer.
Domain owners and stewards for customer, payments, lending, risk and finance data
Critical-data, glossary, quality, metadata and lineage controls tied to real fintech decisions
Privacy, security, regulatory evidence and third-party data responsibilities built into workflows
Governance cadence, reporting, mobilisation and transition into sustainable operations
Scope, timeline and commercial terms are confirmed after reviewing the fintech business model, regulated-entity context, data domains, systems, partners, control requirements and implementation depth.
Fintech Governance Operating ViewGoverned flow
API Ecosystems
KYC providers, bureaus, payment partners, lenders, cloud services and data platforms create shared accountability across organisational boundaries.
Decision-Critical Data
Onboarding, payment, credit, fraud, reconciliation and customer decisions rely on data that must be understood, controlled and traceable.
AI & Analytics
Feature data, training or evaluation datasets and automated decisions add new provenance, quality, access and oversight requirements.
Why a governance office becomes necessary
When Fintech Growth Outruns Data Accountability
A fintech data governance office is most useful when ownership and control need to become part of the operating model rather than an occasional compliance exercise. The trigger is often not “more policy”; it is repeated friction at the points where product, data, technology and risk teams need the same data to mean the same thing.
Ownership Is Unclear
Customer, payment, lending and risk data can cross multiple squads and systems, leaving definition, quality and remediation decisions without a durable owner.
- Conflicting business definitions
- Stewardship depends on individuals
- Cross-domain issues escalate late
Control Evidence Is Fragmented
Quality checks, reconciliations, approvals, access decisions and issue evidence may sit across spreadsheets, tickets, platforms and partner processes.
- Manual evidence collection
- Unclear source-to-report lineage
- Controls not linked to critical data
Partner Data Creates Dependencies
Fintech operations often rely on external KYC, payment, bureau, banking, cloud or data providers, creating shared data quality and control responsibilities.
- Source precedence is unclear
- Data contracts change
- Third-party issues affect customer journeys
AI Expands the Governance Surface
Fraud, credit, personalisation and service automation can depend on model features, derived data, labels, prompts or evaluation evidence that need governed provenance and use.
- Model inputs lack accountable owners
- Training or feature lineage is incomplete
- Monitoring and change evidence is disconnected
Industry process context
Governance Across the Fintech Data Value Chain
The governance office should attach accountability to business stages and decisions—not to an abstract list of policies. The exact route varies by business model, but the following shows how data moves through common fintech operating contexts.
Acquire & Onboard
Identity, consent, device, KYC and profile data enter the ecosystem.
Decision: verify & activateAccount / Wallet
Customer, account, wallet, merchant and entitlement relationships are established.
Control: ownership & accessPay / Transfer
Transaction, payment, beneficiary, merchant and settlement events are processed.
Decision: route & authoriseLend / Service
Applications, bureau data, income, credit features, decisions and repayments are used.
Decision: assess & serviceDetect Risk
Fraud signals, AML/KYC events, rules, cases and model outputs support investigation.
Decision: investigate & interveneReconcile & Finance
Ledger, fees, settlements, exceptions and finance data are matched and explained.
Control: reconcile & evidenceAnalyse & AI
Operational data is transformed into metrics, features, models and product insights.
Control: provenance & qualityReport & Assure
Management, risk, audit and regulatory evidence depends on traceable source data.
Outcome: accountable evidenceData domains and architecture
A Governance Office Built Around Fintech Data Relationships
Governance becomes operational when business domains, system flows and control responsibilities are connected. DataConsultant maps the domains that matter to the client’s products and then designs a governance plane over the existing architecture rather than assuming a new technology stack.
Priority Domain Network
Representative domains are prioritised by the organisation’s actual product and regulatory context.
Customer & PartyIdentity / KYCConsent & PreferenceAccount / WalletMerchantPayment / TransactionCredit / LendingFraud / Risk / CaseProduct / Fee / PricingFinance / Ledger / ReconciliationPartner / Provider / APIRegulatory / EvidenceTelemetry / LogsAI Feature / Training / Evaluation
Example relationship: Customer ↔ Identity/KYC ↔ Account/Wallet ↔ Payment/Transaction ↔ Risk/Fraud ↔ Finance/Reconciliation ↔ Reporting, with partner and AI data intersecting multiple stages. The governance design records authoritative sources, owners, critical elements, definitions, quality rules, access expectations and lineage.
Governance Over the Existing Data Flow
Tooling is requirements-led and vendor-neutral unless platform selection is explicitly in scope.
ExperienceMobile app · web · merchant channels · operations · customer service
OperationalKYC/identity · payments · ledger · lending · fraud/case · CRM · finance
IntegrationAPIs · event streams · files · partner interfaces · orchestration · data contracts
Data platformPipelines · lake/lakehouse/warehouse · semantic layers · governed data products
ConsumptionOperations · reconciliation · BI · risk · regulatory evidence · ML/AI
Governance plane: business glossary · ownership · stewardship · critical data · quality rules · metadata · lineage · access/use · retention · issue management · control evidence · change governance
Transformation goal
From Reactive Data Escalation to a Fintech Governance Operating Discipline
The target is not bureaucracy. It is a repeatable mechanism for deciding who owns critical data, what good data means, which evidence is required, how changes are approved and how issues move from discovery to accountable resolution.
Typical Current State
- ×Product squads use different customer, transaction or risk definitions.
- ×Critical data elements are not consistently identified or tied to business use.
- ×Quality checks exist but thresholds, ownership and remediation are inconsistent.
- ×Partner and API data responsibilities are implicit rather than documented.
- ×Lineage for reconciliation, reporting or model features is incomplete.
- ×Governance issues are escalated through ad-hoc meetings and spreadsheets.
Governed Target State
- ✓Accountable owners and stewards are assigned by fintech data domain.
- ✓Critical data is linked to decisions, processes, systems, controls and evidence.
- ✓Quality rules, exceptions and remediation have agreed thresholds and owners.
- ✓Third-party data obligations are mapped into contracts, controls and workflows.
- ✓Business and technical lineage supports traceability and change impact.
- ✓Forums, issue workflow, MI and escalation create a sustainable operating cadence.
What DataConsultant does
Design the Governance Office Around Decisions, Domains and Controls
DataConsultant combines operating-model design with data governance, quality, metadata, lineage, privacy, security and implementation planning. The work is shaped around the client’s fintech business model rather than a generic governance template.
Onboarding, payments, lending, risk and partner data cross teams.
Owners, stewards, forums, policies and escalation are connected.
Rules and controls are tied to business use and risk.
Governance moves into product, data and control processes.
Governance becomes repeatable rather than project-dependent.
Governance Office Charter
Purpose, scope, authority, service catalogue, interfaces, decision boundaries, escalation and measures for a fintech-specific central governance capability.
Output: charter + service modelDomain Ownership & Stewardship
Accountability for customer, KYC, payments, lending, fraud/risk, finance and other relevant data domains, including RACI and role expectations.
Output: domain model + RACICritical Data & Business Glossary
Prioritisation of high-value or high-risk elements, business definitions, authoritative sources, use cases, owners and approval workflows.
Output: CDE approach + glossaryData Quality & Issue Control
Quality dimensions, business rules, thresholds, exceptions, root-cause workflow, remediation ownership, acceptance evidence and reporting.
Output: rule/control library + workflowMetadata, Lineage & Change
Business and technical metadata requirements, lineage priorities, source-to-decision tracing, impact analysis and change-governance integration.
Output: metadata + lineage designPrivacy, Security & Regulatory Interface
Data classification, purpose/use, access, retention, residency, third-party responsibilities and evidence interfaces with specialist control functions.
Output: control responsibility mapPartner & API Data Governance
Responsibilities for data received from or shared with KYC, payment, credit, banking, cloud and other external providers, including change and issue handling.
Output: partner data governance modelAI & Model Data Governance
Ownership, provenance, quality, access, feature/training/evaluation data, change evidence and interfaces with model or AI governance.
Output: AI-data governance overlayGovernance MI & Adoption
Forum cadence, issue and control reporting, adoption measures, domain scorecards, training, communications and continuous-improvement backlog.
Output: governance calendar + MI packTarget operating model
A Federated Governance Office That Keeps Accountability With the Business
A central office should enable consistency and escalation without becoming the owner of every dataset. The operating model is usually federated: enterprise standards and governance coordination at the centre, with decision rights retained by accountable fintech domain owners and executed through stewards, product and technology teams.
Regulatory, privacy and control context
Connect Governance Evidence to Applicable Fintech Obligations
Fintech is not a single regulatory category. Applicability depends on jurisdiction, legal entity, licence, product, role in the ecosystem, processing activity and data handled. DataConsultant therefore maps obligations to data processes and controls during discovery instead of assuming that every rule applies to every fintech organisation.
Digital Personal Data Protection Act, 2023
For digital personal data within scope, the governance office can help maintain data inventories, ownership, processing purpose, notice/consent interfaces where applicable, processor responsibilities, access, retention, erasure and breach-related evidence.
Official MeitY Act text ↗Digital Personal Data Protection Rules, 2025
The Rules were notified in November 2025 with an enforcement timeline. Governance design should track which requirements are in force for the organisation and connect privacy responsibilities to data owners, processors, systems and operational evidence.
Official MeitY Rules ↗RBI IT Governance, Risk and Controls Directions
The 2023 Directions apply to specified RBI-regulated entities, including scheduled commercial banks, small finance banks, payments banks, NBFCs, credit information companies and AIFIs. Where a fintech entity is in scope, data governance should interface with IT governance, third-party, assurance and control responsibilities.
RBI Master Direction ↗Payment System Data Storage
RBI’s 2018 directive applies to payment-system providers authorised or approved by RBI. For relevant payment businesses, the data governance office should make residency, data-flow, third-party and evidence responsibilities explicit across the payment chain.
RBI payment-data directive ↗CERT-In Cyber Directions
CERT-In’s 2022 directions include requirements for specified organisations around cyber-incident reporting and ICT-system logging, including secure retention of logs for a rolling period. The data governance office should coordinate data classification, retention and evidence interfaces with security rather than duplicating incident response.
Official CERT-In directions ↗Product-Specific RBI Requirements
Digital lending, KYC, payment aggregation, account aggregation and other financial activities may have additional requirements. During scoping, DataConsultant identifies which obligations are relevant to the client’s role and maps data ownership, quality, lineage, access, third-party and evidence needs accordingly.
Reserve Bank of India ↗Scope boundary: DataConsultant supports governance design, readiness, control mapping and implementation. The service does not replace legal advice, statutory audit, regulatory interpretation by qualified counsel, certification or a regulator’s determination of compliance.
AI and model-data considerations
Extend the Governance Office to the Data Behind Fintech AI
AI governance and data governance intersect wherever models depend on customer, transaction, credit, fraud, behavioural or third-party data. The governance office should make data accountability visible across the AI lifecycle while specialist model-risk or AI-governance teams retain their own responsibilities.
Fintech AI Data Questions the Office Should Answer
For each material use case, governance should connect intended purpose with data provenance, quality, permitted use, access, transformation, evaluation and change evidence.
- Credit: Which source data and derived features influence eligibility or pricing decisions, and who owns their quality?
- Fraud: How are labels, alerts, device signals and partner data sourced, changed and monitored?
- Personalisation: Is customer data being used for a compatible and approved purpose with appropriate access and retention?
- Generative AI: Which enterprise data can be used for retrieval or grounding, and how are confidential data, output quality and human review controlled?
01Use CasePurpose, owner, decision and materiality.
02DataSources, rights, provenance, quality and sensitivity.
03BuildFeatures, training/grounding data and transformations.
04EvaluateData coverage, performance evidence and limitations.
05ApproveRisk, privacy, security and business-owner sign-off.
06DeployAccess, lineage, versions and operating controls.
07MonitorData drift, quality, exceptions and output concerns.
08ChangeSource, feature, prompt, model or vendor changes.
09EvidenceDecisions, approvals, tests and control records.
10RetireDependencies, data retention and decommissioning.
How DataConsultant delivers the work
From Governance Diagnosis to Mobilised Fintech Operations
The engagement is designed as a consulting and transformation sequence, not a software-development lifecycle. Each stage produces decisions and evidence that can be reviewed with business, product, data, technology and control stakeholders.
Align
Confirm business model, products, legal entities, sponsor, governance triggers, priority decisions, regulatory context and acceptance criteria.
Map
Map fintech processes, domains, systems, APIs, partners, critical data, reporting, analytics/AI uses and control dependencies.
Assess
Review ownership, stewardship, policies, definitions, quality, lineage, issue history, tooling, forums, risk findings and evidence gaps.
Prioritise
Rank governance gaps by business impact, customer effect, regulatory relevance, data criticality, feasibility and dependency.
Design
Define charter, domain model, decision rights, roles, standards, control interfaces, issue workflow, reporting and technology requirements.
Validate
Walk target processes through real fintech scenarios such as onboarding, payment exceptions, credit features, reconciliation or data-sharing changes.
Mobilise
Onboard owners and stewards, establish forums, populate critical-data and glossary assets, launch issue workflows and baseline MI.
Operate & Improve
Run governance cadence, measure adoption and control health, address recurring issues, transfer capability and refine the model as products evolve.
MobilisationMandate & sponsorshipCharter, sponsor, scope, forums, dependencies and mobilisation backlog.
FoundationDomains & ownershipOwners, stewards, critical data, glossary, policy and decision rights.
ControlQuality & issuesRules, thresholds, exceptions, root cause, remediation and evidence.
TraceabilityMetadata & lineageBusiness/technical metadata, source-to-decision lineage and change impact.
IntegrationProduct, partner & AI workflowsEmbed governance in product change, API data, access, risk and AI-data decisions.
OperateMI, adoption & transferGovernance operations, KPI/MI, training, backlog, managed support or transition.
Tangible outputs and client inputs
What You Receive—and What We Need to Build It Properly
Deliverables are tailored to the decisions required and evidence available. Missing source information is recorded as a limitation rather than silently assumed.
Typical Fintech Governance Office Deliverables
- Governance-office charter and service catalogue
- Fintech data-domain model and ownership map
- RACI and decision-rights framework
- Data owner and steward role profiles
- Governance council / forum design and cadence
- Policy and data-standard framework
- Critical-data-element prioritisation method
- Business glossary and definition workflow
- Data-quality rule and control framework
- Data issue taxonomy, severity and escalation workflow
- Metadata, catalogue and lineage requirements
- Partner / third-party data responsibility model
- Privacy, security and regulatory control-interface map
- AI-data governance overlay where relevant
- Governance KPI / MI and reporting pack
- Implementation roadmap and mobilisation backlog
- Training, adoption and communications plan
- Operating playbook and transition plan
Useful Client Inputs
- Business context: products, licences/roles, legal entities, geographies, growth and transformation priorities.
- Organisation: sponsor, product teams, business owners, risk/compliance, privacy, security, data, engineering and AI stakeholders.
- Architecture: system, API, partner, data-flow and platform inventories or diagrams.
- Governance evidence: policies, existing ownership, stewardship, councils, glossary, catalogue, quality rules and issue workflows.
- Risk evidence: audits, regulatory findings, incidents, material data issues, reconciliation breaks and control assessments.
- Data samples / metadata: representative schemas, dictionaries, lineage extracts or profiling information where appropriate and approved.
- AI context: use-case inventory, model/feature dependencies, data sources and evaluation evidence where relevant.
- Delivery constraints: deadlines, programmes, platform changes, vendor dependencies, access and review requirements.
Implementation and ongoing operations
Design → Mobilise → Implement → Operate → Improve → Transfer
The governance office can be implemented with internal teams, existing vendors and platform owners. Ongoing support is scoped separately so responsibilities, acceptance criteria and transition expectations remain explicit.
Senior Governance Support
Decision support for governance leaders, domain conflicts, policy changes, regulatory data priorities and roadmap adjustments.
Governance Office Operations
Forum coordination, stewardship, issue intake, standards, MI, backlog management, evidence and continuous-improvement routines.
Quality & Issue Operations
Rule monitoring, exception triage, root-cause coordination, remediation tracking, scorecards and recurring-issue analysis.
Catalogue & Lineage Operations
Metadata maintenance, ownership workflows, glossary governance, lineage updates, change impact and adoption support.
AI Data Governance Operations
Use-case data checks, provenance, quality, access, change evidence and coordination with model or responsible-AI governance.
Delivery Assurance
Implementation governance, workstream dependencies, acceptance criteria, risk tracking and evidence that agreed controls are operational.
Owner & Steward Training
Role-based workshops, practical scenarios, playbooks and guided adoption tied to the client’s data domains and workflows.
Scale or Transfer
Documented runbooks, capability handover, operating metrics, backlog and support model so client teams can sustain the office.
Clearer accountabilityCritical fintech data has named owners, stewards and decision paths.
More dependable dataQuality expectations connect to business rules, exceptions and remediation.
Stronger traceabilityMetadata and lineage support impact analysis, reporting and assurance.
Controlled data usePrivacy, access, third-party and AI-data decisions use repeatable governance.
Sustainable operationsForums, MI, workflows and training reduce dependence on informal escalation.
Commercial treatment
Custom Scope & Pricing for Fintech Data Governance Office Engagements
A fixed price is not published for this service. The commercial model is confirmed after discovery because the effort changes materially with fintech business model, regulatory context, number of domains, architecture complexity and the depth of mobilisation or ongoing operations required.
Commercial basis
Request a Scoped Quote
DataConsultant will define the engagement boundary, assumptions, deliverables, responsibilities and commercial terms after reviewing the decisions the client needs to make and the evidence available.
Request a Fintech Governance QuoteWhat Changes the Commercial Scope?
Fintech segment: payments, lending, wealth, embedded finance or other model
Legal entities, licences, regulated-entity relationships and geographies
Number of business processes and data domains in scope
Systems, APIs, event streams, partners and third-party data sources
Number and criticality of data elements, rules and controls
Current governance maturity, policies, ownership and issue backlog
Metadata, catalogue, lineage and quality-tool maturity
Privacy, security, audit and regulatory evidence requirements
AI/model use cases and governance interfaces
Workshop, stakeholder and executive-validation requirements
Implementation depth, platform configuration and change support
Ongoing operations, training, transition and support model
Buyer decision guidance
When This Service Is the Right Starting Point
A governance-office engagement is appropriate when the core problem is accountability and sustained operating control across domains. A narrower or different service may be better when the issue is isolated to one technical, legal or incident-response problem.
Strong Fit
- Fintech products are scaling faster than ownership and stewardship practices.
- Customer, payment, lending or risk definitions conflict across teams.
- Audit, risk or reconciliation issues repeatedly expose weak data accountability.
- Partner/API dependencies require clearer data responsibilities and change control.
- Data quality, metadata and lineage initiatives lack a common operating model.
- AI adoption requires clearer governance of features, training, grounding or evaluation data.
Consider Another Starting Point
- An active cyber incident requires specialist incident response or forensics.
- The primary need is legal interpretation or formal regulatory assurance.
- A single dataset needs profiling or remediation without an operating-model problem.
- The requirement is purely a data-platform migration or engineering build.
- A model requires deep independent validation rather than data-governance design.
- The organisation already has a mature governance office and only one specialised capability needs improvement.
Why DataConsultant for this problem
Governance Designed for Execution, Not Shelfware
Fintech process contextGovernance ties to onboarding, payments, lending, fraud, reconciliation, partners and AI.
Business + technology viewOwnership and policies connect to systems, APIs, metadata, lineage and quality controls.
Risk-aware boundariesPrivacy, security, compliance and assurance interfaces are explicit rather than duplicated.
Implementation pathDesign includes mobilisation, workflows, adoption, tooling requirements and operating playbooks.
Transparent limitationsAssumptions, evidence gaps, dependencies and out-of-scope activities are recorded before decisions.
Related capabilities
Adjacent DataConsultant Services That May Support the Governance Office
Use these only where the governance-office design identifies a deeper specialist need.
Frequently asked questions
Fintech Data Governance Office FAQs
These answers explain the service boundary, operating model, data domains, implementation and commercial approach. Project-specific obligations and responsibilities are confirmed during scoping.
What is a Fintech Data Governance Office?
A Fintech Data Governance Office is the coordinating operating capability that turns data policy into accountable day-to-day decisions across fintech products and data domains. It defines governance scope, ownership, stewardship, decision rights, forums, standards, critical data, quality controls, metadata and lineage expectations, issue workflows, reporting and adoption practices across business, product, data, technology, risk, privacy and security teams.
Which fintech data domains should the governance office cover first?
Priorities depend on the business model and risk profile. Common starting domains include customer and identity, KYC and verification, accounts or wallets, merchants, payments and transactions, lending and repayment, fraud and risk, product and pricing, finance and reconciliation, partner and API data, regulatory evidence, operational telemetry and the data used by analytics or AI. DataConsultant prioritises domains by business criticality, regulatory relevance, customer impact, decision dependency and known data issues.
Who should sponsor a fintech data governance office?
Executive sponsorship may sit with a chief data officer, CIO, CTO, COO, chief risk officer, compliance leader, product executive or another accountable leader, depending on the organisation. The office should have enough authority to convene domain owners and resolve cross-functional decisions while keeping business accountability with the relevant owners rather than centralising every decision in the governance team.
What is included in DataConsultant’s Fintech Data Governance Office service?
Scope can include current-state assessment, governance-office charter, domain and ownership model, decision-rights and RACI design, governance forums, stewardship model, critical-data approach, glossary and metadata requirements, data-quality and issue controls, lineage priorities, privacy and security interfaces, regulatory evidence mapping, AI-data governance overlays, governance reporting, implementation roadmap, mobilisation support, training and operating playbooks. Final scope is confirmed during discovery.
Does a data governance office replace risk, compliance, privacy or information security?
No. The data governance office should coordinate responsibilities and evidence without duplicating specialist control functions. Risk, compliance, privacy, information security, internal audit, legal and product teams retain their own mandates. The operating model should define where data decisions intersect with those functions, which approvals are required and how exceptions are escalated.
How does the service support payments and digital lending businesses?
For payment and lending contexts, governance can focus on customer and KYC data, transaction and payment data, merchant or partner data, credit and repayment information, fraud and risk signals, reconciliation, reporting, API exchanges and model features. Applicable RBI or other obligations depend on the organisation’s licence, role and activities, so regulatory mappings are validated during scoping rather than assumed.
How are data quality and lineage handled?
DataConsultant can identify critical data elements, intended business uses, owners, quality dimensions, testable rules, thresholds, exceptions and remediation paths. Lineage work can map data from source applications and third parties through APIs, pipelines and transformations into operational decisions, reports, analytics and AI. The depth of technical lineage and automation depends on the systems, metadata access and tooling available.
Can the governance office cover AI and machine-learning data?
Yes, where relevant. The operating model can extend data ownership, provenance, quality, access, purpose, feature or training-data controls, evaluation evidence and change management to AI and machine-learning use cases. Model governance itself may require a separate or integrated AI governance workstream depending on the use cases and risk profile.
Which regulations and standards are considered?
The applicable set depends on jurisdiction, legal entity, product, licence, processing role and data handled. In India, relevant considerations may include the Digital Personal Data Protection Act and notified Rules, RBI directions applicable to regulated entities or payment-system providers, digital-lending requirements where relevant, and CERT-In cyber directions. DataConsultant supports governance and readiness; it does not provide a blanket guarantee of regulatory compliance or replace legal advice or statutory assurance.
How long does a Fintech Data Governance Office engagement take?
Timeline is confirmed after scoping. It depends on the number of data domains and legal entities, product and platform complexity, stakeholder availability, evidence quality, regulatory context, existing governance maturity, tooling, required deliverables and whether mobilisation, implementation or managed operations are included.
How is pricing determined?
DataConsultant does not publish a fixed price for this page. Commercial scope is determined after discovery based on the fintech business model, number of domains, systems and partners, critical data, stakeholder groups, governance maturity, regulatory and control requirements, workshops, deliverables, implementation depth, tooling support, training and any ongoing governance operations. Third-party software, cloud and licence costs are treated separately unless explicitly included in a proposal.
Can DataConsultant help implement and operate the governance office?
Yes. Implementation support can be scoped for governance mobilisation, data-owner and steward onboarding, forum setup, critical-data definition, quality rules, metadata and lineage rollout, issue workflows, dashboards, control evidence, AI-data governance, change management and training. Ongoing support can also be structured around governance operations, data-quality operations, metadata and catalogue operations, senior advisory or a transition-to-client model.
What information should we prepare before starting?
Useful inputs include the fintech product and legal-entity landscape, organisation and decision structures, policies, regulatory obligations, data-domain inventories, architecture and data-flow diagrams, system and API inventories, third-party dependencies, glossary or metadata assets, quality reports, issue backlogs, audit or risk findings, model or AI inventories where relevant, current governance forums and access to accountable business, product, technology, risk, privacy and security stakeholders.
Fintech Data Governance Enquiry
Request a Fintech Governance Scope Review
Share your contact details and requirement. DataConsultant can review the likely scope, stakeholder involvement, evidence required and appropriate next step.