Identity & Relationships
Connect account, user, seat, tenant and subscription identities.
Technology and SaaS · Customer Data Governance
Customer Data Governance That Connects Customer, Tenant, Product and Revenue Data
DataConsultant helps technology and SaaS organisations establish accountable customer-data ownership across acquisition, signup, tenant provisioning, subscription, product telemetry, support, billing, analytics and AI. We connect definitions, identity, quality, privacy, lifecycle controls, lineage and decision rights so customer information can be used consistently across product, go-to-market and data teams.
Customer, account, user and tenant identity governed together
Product telemetry, subscription and support data connected to business definitions
Purpose, access, retention, quality and downstream-use controls designed into workflows
Implementation roadmap, operating model and stewardship routines made actionable
Scope, timeline and commercial terms are confirmed after reviewing products, customer journeys, data domains, systems, jurisdictions, controls, stakeholder groups and implementation needs.
Trusted Product Signals
Govern product events, usage definitions and derived customer signals.
Controlled Customer Use
Map purpose, access, sharing, retention and preference requirements.
Analytics & AI Readiness
Make approved customer data easier to trace, evaluate and govern.
01
Why Customer Data Governance Becomes a Scale Problem in Technology and SaaS
SaaS customer data is rarely a single record. It is a relationship between organisations, users, tenants, subscriptions, product events, support interactions, commercial status and permissions. Growth adds products, channels, integrations, acquisitions, geographies and AI use cases—making accountability and meaning harder to maintain unless governance follows the operating model.
AcquireProspect, campaign, contact and account signals.
Sign UpIdentity, account, user, consent and source context.
ProvisionTenant, workspace, seat, role and entitlement relationships.
Use ProductEvents, feature adoption, sessions and product telemetry.
Support & SuccessCases, engagement, customer health and interventions.
Renew & ExpandSubscription, revenue, usage and lifecycle decisions.
Learn & AutomateAnalytics, personalisation, scoring and approved AI use.
The problem is not only fragmented systems—it is fragmented decisions.
CRM may define an account one way, billing another, product telemetry a third and customer-success tooling a fourth. Without a decision model, teams can resolve the same identity, quality, retention or usage question differently.
- Account, tenant and user relationships are unclear across B2B and B2C models.
- Telemetry schemas change faster than business definitions and metric lineage.
- Consent, preferences and retention decisions do not reliably propagate downstream.
- Customer-health, churn and personalisation signals inherit undocumented data quality.
- New AI use cases create additional questions about approved data, access and evidence.
Typical current state
- Different definitions of customer, active user, account and tenant.
- Duplicate identities and unresolved organisation relationships.
- Event names and properties changed without governed impact analysis.
- Privacy and retention rules handled as separate policy activities.
- Quality issues corrected locally without root-cause ownership.
- AI or analytics teams discover customer-data constraints late.
Target governance capability
- Named customer-domain owners, stewards and technology custodians.
- Canonical definitions and relationship rules for key business entities.
- Critical data elements and event contracts tied to measurable quality.
- Purpose, access, sharing, retention and deletion controls embedded in flows.
- Lineage, issue management and control evidence available for review.
- Approved analytics and AI use connected to governance and change decisions.
Map the Customer Data Decisions That Are Causing Rework or Risk
Start with the identities, systems, product events, lifecycle decisions and control gaps that most affect customer insight, retention, product operations or privacy execution.
02
What DataConsultant Does: Build an Operating System for Customer Data Decisions
The service is designed around accountable decision-making, not a policy binder or a single tool. DataConsultant connects the customer-data domain, business rules, controls, architecture and operating routines so governance can work across product, data, engineering, go-to-market, privacy and security teams.
Service definition
Customer Data Governance for a digital product operating model
We help define who may create, change, combine, enrich, share, retain and use customer information; what standards apply; how quality is measured; how exceptions are handled; and how evidence is produced across the customer lifecycle.
01Business problemConflicting customer identities, definitions, controls or downstream use.
02Required capabilityAccountable domains, critical data, quality and lifecycle controls.
03Implementation mechanismRoles, policies, workflows, metadata, lineage, rules and change gates.
04Operating outcomeRepeatable governance that can support product, revenue, analytics and AI decisions.
Ownership & Decision Rights
- Customer-domain charter and boundaries
- Owner, steward and custodian roles
- RACI, forums and escalation
- Change and exception decisions
Definitions, Identity & Quality
- Business glossary and critical elements
- Account, user and tenant relationship rules
- Quality dimensions, rules and thresholds
- Issue, root-cause and remediation workflow
Privacy & Lifecycle Control
- Purpose and downstream-use mapping
- Consent and preference control requirements
- Access, sharing, retention and deletion
- Third-party and cross-system evidence
Technology & Operations
- Metadata, catalogue and lineage requirements
- Data contracts and change gates
- Platform control requirements
- Governance reporting, adoption and training
03
Govern the Relationships Between Customer, Tenant, Subscription and Product Data
A SaaS customer domain needs more than a list of entities. Governance must make the relationships explicit—who the organisation is, who the users are, where they operate, what they bought, what they are entitled to use, what they actually use, how they are supported, and what permissions apply to downstream processing.
Commercial & relationship context
Prospect / Contact / Lead Source
Organisation / Account / Hierarchy
Subscription / Plan / Contract
Billing / Invoice / Revenue Status
Customer Success / Support / Interaction
Customer Data Governance Domain
Govern identity and relationship rules across the digital product and commercial lifecycle.
User / SeatIdentityTenant / WorkspaceEntitlementUsage / EventConsent / Preference
Product & approved-use context
Product / Feature / Release
Session / Event / Product Telemetry
Customer Health / Adoption / Churn Signal
Marketing / Personalisation / Experiment
Analytics / Model Input / AI Grounding Data
04
Embed Governance Across the SaaS Customer Data Architecture
Customer Data Governance has to travel with data—from capture and operational systems through integration and storage to activation, analytics and AI. The architecture below is a category-level reference: the actual client stack is assessed rather than assumed.
1. Capture & OperationsWhere customer data originates
Web & AppSignup, identity, journeys
CRMAccount and contact context
Product ServicesTenant, feature and usage events
BillingPlans, invoices, entitlement
Support / SuccessCases, health and intervention
2. Integration & IdentityHow records and events connect
APIs & EventsExchange and event streams
Identity ResolutionAccount/user/tenant links
Data ContractsSchema and change expectations
OrchestrationMovement and transformation
Preference SyncConsent and suppression flow
3. Data & Governance PlaneWhere trust and evidence are maintained
Warehouse / LakehouseAnalytical customer data
Catalog & GlossaryDefinitions and ownership
LineageSource-to-use traceability
Quality MonitoringRules, thresholds, issues
Access & LifecyclePermissions, retention, deletion
4. Approved ConsumptionDecisions that customer data supports
Product AnalyticsAdoption and feature decisions
Customer HealthSuccess and intervention
Revenue AnalyticsRenewal, expansion, churn
ActivationSegmentation and personalisation
AI SystemsApproved models and grounding
Customer 360 & Account Resolution
Define identifiers, source precedence, relationships, merge/split rules, ownership and exception handling for account, user and tenant views.
Typical decision: which record and relationship is trusted for an approved downstream use?Telemetry & Metric Governance
Control event definitions, schemas, ownership, lineage, quality and change so product metrics remain interpretable as releases evolve.
Typical decision: can an event or derived signal be trusted for product, customer or experiment decisions?Customer Health, Churn & Expansion
Govern the features, statuses and business definitions that feed health scores, retention analysis and commercial interventions.
Typical decision: what data is approved and sufficiently reliable to trigger an intervention?Consent, Preference & Retention
Map capture points, purpose, preferences, sharing, retention, deletion and evidence across operational and analytical copies.
Typical decision: may this customer data continue to be processed for this purpose and in this system?CRM, CDP or Platform Migration
Embed governance requirements into entity mapping, data movement, quality remediation, lineage, cutover and stewardship.
Typical decision: which definitions and controls must survive system or platform change?Customer-Facing Analytics & AI
Define data suitability, approved sources, sensitive attributes, access, evidence and lifecycle controls before customer data feeds models or GenAI.
Typical decision: is this data approved, controlled and fit for this model or automated customer interaction?Turn a Customer 360, CDP or AI Programme Into a Governed Data Capability
Define the customer domains, identity rules, quality controls, lineage, permissions and stewardship decisions that the programme needs before implementation choices become hard to reverse.
05
Translate Customer Data Risk Into Quality Rules, Controls and Evidence
Governance becomes operational when a data element has a business rule, a measurable quality expectation, an accountable owner, a control, an exception path and evidence. The exact thresholds are agreed with the client; they are not imposed generically.
| Customer-data area | Example governance question | Control design | Evidence / monitoring | Business impact |
|---|---|---|---|---|
| Account & tenant identity | Which organisation, tenant and user relationship is authoritative? | Identifier standard, source precedence, merge/split workflow, steward approval. | Duplicate rate, unresolved relationship queue, approved exception history. | Customer 360, support routing, revenue attribution and access decisions. |
| Subscription & entitlement | Are plan, contract and product-access states consistent across systems? | Critical-element rules, reconciliation, ownership and change controls. | Reconciliation exceptions, stale entitlements, issue age and root cause. | Provisioning, billing, customer experience and revenue reporting. |
| Product telemetry | Does an event still mean the same thing after a release or schema change? | Event contract, definition owner, schema review, lineage and quality checks. | Schema-change log, failed events, lineage coverage, metric impact assessment. | Product adoption, experimentation, health scoring and roadmap decisions. |
| Consent & preference | Can the captured state be traced to permitted downstream activation? | Purpose mapping, synchronisation rules, suppression logic, audit evidence. | Preference propagation, stale states, failed suppression, control reviews. | Customer trust, marketing operations and privacy execution. |
| Derived customer signals | Is a churn, health or segment attribute based on governed inputs? | Definition, input lineage, quality expectations, owner and approved use. | Model or metric version, input quality, drift/exception review where relevant. | Retention, expansion, prioritisation and customer interventions. |
06
Design Privacy, Security and Regulatory Requirements Into Customer Data Operations
Depending on jurisdiction, business model, data handled and applicable obligations, a technology or SaaS provider may need to operate different privacy and customer-rights controls. DataConsultant translates applicable requirements supplied or validated with the client into ownership, data-flow, lifecycle and evidence requirements. Legal interpretation remains with appropriately qualified legal counsel.
Control the data lifecycle, not just the privacy notice.
Customer Data Governance can connect collection purpose and notice context to downstream systems, access decisions, third-party sharing, retention, deletion, quality and evidence. This gives product, data and engineering teams a control model they can implement.
Important: The examples alongside are jurisdictional reference points, not a statement that every law applies to every organisation. Applicability and legal obligations must be assessed for the client’s actual business.
India — Digital Personal Data Protection Act, 2023 and Rules, 2025
The DPDP Act uses phased commencement. As of September 2026, some provisions have commenced, while additional provisions are scheduled later. Governance design should therefore use the current commencement status, the organisation’s role and actual processing context rather than assuming every obligation is already in force.
Review the DPDP Act on India Code →Review the 2025 Rules and enforcement timeline on MeitY →
European Union / EEA — GDPR where applicable
Where the GDPR applies, customer-data governance may need to support lawful processing, transparency, data minimisation, rights handling, processor governance, security, accountability and transfer controls. Governance should link those requirements to real data flows and operating ownership.
Review the European Commission GDPR framework →California — CCPA as amended, where applicable
For businesses in scope, governance may need to support consumer rights, notices, sale/sharing controls, sensitive personal information handling, vendor roles and operational response workflows. Applicability depends on the statutory scope and the organisation’s activities.
Review the California Attorney General CCPA guidance →07
Make Customer Data Governance an Input to AI Governance—not an Afterthought
Technology and SaaS organisations increasingly use customer information in support copilots, customer-health models, recommendation, personalisation, lead scoring, product analytics and generative AI. Customer Data Governance should determine whether the data is approved, traceable, sufficiently reliable and appropriately controlled before it enters the AI lifecycle.
01Business purposeDefine the customer decision or experience the AI supports.
02Approved dataIdentify sources, purposes, sensitive attributes and restrictions.
03Quality & lineageEvaluate definitions, completeness, provenance and transformations.
04Access & vendor useControl retrieval, sharing, model-provider and grounding pathways.
05EvaluationConnect output testing to known data constraints and intended use.
06Human oversightDefine review, escalation and customer-impact decisions where needed.
07Monitor & changeTrack data, schema, model and approved-use changes through governance.
08
Create a Customer Data Operating Model That Matches SaaS Product Delivery
The governance model has to work with product squads, platform teams and go-to-market operations—not around them. DataConsultant can define federated decision rights so domain accountability remains clear while local teams can deliver at product speed.
One customer domain, distributed execution
An accountable domain owner sets business outcomes, definitions and priority controls. Stewards coordinate day-to-day quality and issue decisions. Technology custodians implement controls. Product, RevOps, marketing, customer success, privacy, security and AI teams participate where their decisions affect customer data.
Domain leadership
Customer-domain charter, owner, decision rights, critical data, policy exceptions, investment priorities and executive escalation.
Stewardship network
Business definitions, quality rules, issue triage, metadata ownership, approval workflow and coordination across products and functions.
Technology custody
Implementation of access, data contracts, lineage, quality checks, retention, deletion, monitoring and platform change requirements.
Control partners
Privacy, security, legal, risk and AI governance participate where applicable without replacing business ownership of the customer domain.
09
How DataConsultant Delivers Customer Data Governance
The engagement follows the customer data chain from business decisions to systems and controls. It can begin as a diagnostic, continue into target-state design, and then move into implementation and operating support when those activities are included in scope.
01
Align
Confirm customer journeys, product model, business outcomes, sponsor, risks and scope.
02
Discover
Inventory domains, systems, identifiers, events, flows, controls, obligations and evidence.
03
Diagnose
Assess ownership, definitions, quality, lifecycle controls, lineage and recurring issues.
04
Design
Define domain model, roles, standards, controls, governance forums and target architecture.
05
Mobilise
Prioritise CDEs, workflows, tool requirements, remediation backlog, pilots and adoption.
06
Operationalise
Embed routines, measurement, change gates, issue handling, training and continuous improvement.
10
Tangible Outputs: What You Receive and What DataConsultant Needs From You
Deliverables are selected to support the client’s decisions and implementation plan. The service does not assume every artefact is required; the final pack is confirmed during scope definition.
01Current-state assessmentCustomer-data landscape, maturity, gaps, risks and evidence limitations.
02Customer-domain & identifier modelAccount, user, tenant, subscription and key relationship definitions.
03Critical-data & glossary packPriority terms, elements, owners and business definitions.
04Ownership & RACI modelOwners, stewards, custodians, control partners and decision rights.
05Quality-rule catalogueRules, dimensions, thresholds, monitoring, exceptions and remediation paths.
06Lifecycle control matrixPurpose, access, sharing, consent/preference, retention, deletion and evidence.
07Lineage & architecture requirementsSource-to-use flows, metadata needs, integration and platform control requirements.
08Governance operating cadenceForums, issue workflow, change gates, escalation and decision records.
09Implementation roadmapPrioritised workstreams, dependencies, owners, mobilisation backlog and decisions.
10KPI & control monitoring specificationOwnership, quality, issue, lifecycle and adoption measures with evidence sources.
11
Move From Governance Design to Implementation Without Losing Accountability
Implementation can be scoped separately or as a continuation of the design engagement. DataConsultant can support governance mobilisation while working with the client’s product teams, internal data organisation, platform owners, implementation partners and specialist legal or security advisers.
Confirm roles and decision forums
Appoint owners and stewards, finalise scope, define cadence, approve decision rights and establish issue intake.
Start with critical customer flows
Select high-impact identities, CDEs, events and lifecycle controls rather than attempting enterprise-wide rollout at once.
Embed metadata and controls
Support glossary/catalogue workflows, quality rules, lineage requirements, access/lifecycle controls and platform implementation guidance.
Integrate with product delivery
Add governance checks to schema change, data product, campaign, customer activation, analytics and AI delivery processes where relevant.
Measure and improve
Establish reporting, control reviews, issue ageing, adoption metrics, lessons learned and a continuous-improvement backlog.
Need Governance That Product and Data Teams Can Actually Operate?
DataConsultant can help translate the framework into roles, workflows, quality rules, metadata, lineage, change gates, control evidence and implementation backlog aligned to your delivery environment.
12
Sustain Customer Data Governance After the Initial Programme
Governance must keep pace with new products, pricing models, acquisition channels, event schemas, data platforms, partners and AI use cases. Ongoing support can be designed around the client’s desired level of ownership and internal capability.
Senior Advisory
Decision support for domain strategy, governance design, complex exceptions, roadmap choices and executive reviews.
Best when the client owns daily operations but needs periodic specialist guidance.Governance Operations
Support forums, stewardship coordination, issue tracking, decision logs, reporting, policy updates and continuous-improvement backlog.
Best when operating routines need additional capacity or structure.Quality & Metadata Operations
Monitor rule coverage and exceptions, support remediation, maintain glossary/catalogue ownership and coordinate lineage changes.
Best when governance tooling and data controls need sustained operating discipline.Enablement & Transfer
Role-based training, steward playbooks, product/engineering guidance, governance office mentoring and handover to internal teams.
Best when the target state is a self-sustaining internal capability.13
Commercial Treatment: Scope Customer Data Governance Around the Decisions and Controls Required
A diagnostic, target-state design, implementation programme and ongoing governance service involve different evidence, stakeholders and delivery responsibilities. This page therefore uses scope-led Request a Quote pricing rather than a fixed numeric fee, with commercial scope confirmed after discovery.
Custom Scope & Pricing
Request a scope-based quote
Commercial basis Request a QuoteTimeline is also confirmed after scoping. Third-party software, cloud consumption, platform licences and specialist legal, audit or security services are separate unless explicitly included in a written scope.
Request a Customer Data Governance QuoteBusiness model & product estateB2B/B2C mix, products, plans, tenants, customer journeys and acquisition channels.
Data domains & identifiersNumber of customer entities, critical elements, relationship complexity and identity resolution.
Systems & integrationsCRM, product services, billing, support, data platform, APIs, events and third parties.
Geographies & obligationsLegal entities, jurisdictions, privacy requirements, data residency and contractual constraints.
Governance depthAssessment only, operating-model design, policy/control design, metadata, quality, lineage or tooling advisory.
AI & analytics useCustomer models, personalisation, GenAI, grounding data, vendor use and governance integration.
Stakeholders & changeProduct squads, data teams, go-to-market functions, workshops, role mobilisation and training.
Implementation & run supportConfiguration guidance, rollout, assurance, operating support, managed governance and transfer.
14
Buyer Guidance: When Customer Data Governance Is the Right Starting Point
Use this service when the underlying problem is durable cross-functional accountability for customer information. Choose a narrower technical, legal or data-remediation service when governance is not the real constraint.
Good fit
- Customer, account, user or tenant data is inconsistent across CRM, product, billing or support systems.
- A customer 360, CDP, CRM, data-platform or AI programme needs clear ownership and control requirements.
- Consent, preference, access, retention or deletion obligations are difficult to operationalise across systems.
- Product telemetry or derived customer signals lack stable definitions, lineage or issue ownership.
- Recurring data-quality defects affect customer experience, revenue analytics or intervention decisions.
- Leadership needs an operating model and phased roadmap, not another isolated policy.
May require a different service
- A one-off cleansing task is required without a continuing ownership or control problem.
- The organisation needs a formal legal opinion, regulatory representation or certification audit.
- The only requirement is a penetration test or forensic security investigation.
- A specific software vendor must perform configuration with no independent governance design.
- No accountable sponsor or stakeholder access is available to resolve cross-functional decisions.
- A broader enterprise strategy or architecture decision must be made before customer governance can be scoped.
Build Customer Data Governance Around Your Actual Products, Tenants and Customer Journeys
Share the customer-data decisions you need to improve, the systems involved and the operating constraints. DataConsultant can help define the right diagnostic, design, implementation or operating-support scope.
16
Customer Data Governance for Technology and SaaS — FAQs
Direct answers to common questions about customer domains, product telemetry, privacy, AI, deliverables, implementation, ongoing support, timeline and commercial scope.
What is Customer Data Governance for a technology or SaaS business?
Customer Data Governance is the operating framework that defines who owns customer, account, user, tenant, subscription, preference and related product-usage data; how that data is defined and measured; which purposes and downstream uses are approved; and how quality, access, retention, sharing, issues and change are controlled across the customer-data lifecycle.
Which SaaS business processes can the service cover?
Scope can follow the customer lifecycle from acquisition and signup through identity and account creation, tenant or workspace provisioning, subscription and entitlement management, product usage, customer success, support, billing, renewal, expansion, churn analysis, marketing activation and approved analytics or AI use. Final process scope is agreed during discovery.
Which customer data domains are normally in scope?
Relevant domains can include prospect and contact, organisation or account, user or seat, identity, tenant or workspace, subscription, plan, entitlement, product usage and telemetry, support, customer success, billing, revenue, consent, preference, marketing engagement and derived analytics attributes. The engagement identifies which domains are critical for the client rather than assuming every domain is required.
Can Customer Data Governance support a customer 360, CRM, CDP or identity-resolution programme?
Yes. The service can define customer and account identifiers, source precedence, ownership, critical data elements, match and exception requirements, permitted uses, quality controls, metadata, lineage and stewardship workflows that a customer 360, CRM, CDP or identity-resolution programme needs. Product-specific configuration is scoped separately where required.
How do you handle product telemetry and event data?
The engagement can govern event naming, ownership, business definitions, identifiers, schema change, quality expectations, source-to-metric lineage, retention, access and approved analytical use. This is particularly important when product events feed activation, customer-health metrics, experimentation, personalisation, churn models or AI systems.
How are privacy, consent and customer rights addressed?
DataConsultant can map governance requirements for collection purpose, notices, consent or preference evidence where applicable, access, sharing, retention, deletion, customer-rights workflows and third-party processing into data ownership and technology controls. Applicability depends on jurisdiction, business model and data handled. The service supports operational readiness and does not replace legal advice or guarantee compliance.
How is customer data quality managed?
DataConsultant can identify critical customer data elements, define business rules and quality dimensions, assign thresholds and owners, establish exception and root-cause workflows, connect issues to business impact and specify monitoring. Examples can include duplicate identities, invalid account relationships, inconsistent subscription status, missing consent state or broken event definitions.
How does Customer Data Governance support AI and advanced analytics?
The service can establish which customer datasets are approved for model training, grounding, segmentation, scoring, recommendation or generative-AI use; define ownership and data-quality expectations; identify sensitive attributes and access restrictions; require lineage and evidence; and connect customer-data use to AI governance, evaluation, human oversight and change controls where relevant.
What deliverables can we expect?
Depending on scope, deliverables can include a current-state assessment, customer-domain and identifier model, business glossary and critical-data inventory, ownership and RACI model, stewardship workflows, data-quality rule catalogue, purpose and lifecycle control matrix, lineage and system map, governance forum design, issue workflow, target architecture requirements, implementation backlog, KPI and control-monitoring specification, and role-based enablement material.
What information should we prepare before the engagement?
Useful inputs include customer journey maps, organisation and responsibility information, system and integration inventories, data dictionaries, event schemas, sample or masked data where appropriate, quality reports, issue logs, privacy and retention policies, consent or preference flows, architecture and lineage artefacts, AI use-case inventories, and access to accountable business, product, data, engineering, privacy and security stakeholders. Missing evidence is recorded as a limitation rather than assumed.
Can DataConsultant help implement the governance model?
Yes. Implementation support can be scoped for role mobilisation, governance forums, glossary and catalogue rollout, data-quality rules, issue workflows, lineage, control requirements, change gates, reporting, training, platform-advisory coordination and implementation assurance. Implementation is not automatically included in an assessment or design engagement unless agreed in scope.
Can DataConsultant provide ongoing Customer Data Governance support?
Yes. Ongoing support can be scoped as senior advisory, governance operations, stewardship coordination, quality and issue monitoring, metadata maintenance, control reporting, change review, AI-data governance support, managed operations or capability transfer. Service boundaries and responsibilities are agreed during mobilisation.
How long does a Customer Data Governance engagement take?
Timeline is confirmed after scoping. It depends on the number of products, customer journeys, business units, jurisdictions, systems, data domains, critical elements, stakeholder groups, evidence quality, control requirements, workshops, implementation depth and review cycles.
How is Customer Data Governance pricing determined?
Pricing is scope-led and provided through a Request a Quote process for this page. The quote is confirmed after the number of products, tenants, customer domains, systems and integrations, geographies, critical data elements, privacy and control requirements, workshops, deliverables, implementation support, tooling work, training and ongoing operating needs are understood.
When might this not be the right service?
A narrower service may be more appropriate when the requirement is only a one-off data cleansing exercise, a product-specific configuration task, a penetration test, a formal certification audit or a legal opinion. Customer Data Governance is most useful when the organisation needs durable cross-functional ownership, rules, controls and operating routines around customer information.
Customer Data Governance Enquiry
Request a Customer Data Governance Scope Review
Share your contact details and requirement. DataConsultant can review the likely scope, evidence needed, stakeholder involvement and appropriate next step.