Unclear accountability
Study, data-domain, platform and control responsibilities overlap, leaving ownership of definitions, quality, access and reuse decisions uncertain.
Healthcare & Life Sciences · Research Data Governance
Govern Research Data for Trusted, Traceable and Reusable Science
DataConsultant helps healthcare and life-sciences organisations establish accountable governance across clinical, laboratory, observational, real-world and analytical research data. We connect ownership, metadata, lineage, quality, privacy, consent, standards, sharing, retention and platform controls so approved research use can be supported by evidence rather than fragmented practice.
Study, programme and data-domain accountability
Metadata, provenance and transformation lineage
Privacy-aware access, sharing and reuse controls
Implementation roadmap and operating handover
Scope is tailored to the organisation, research portfolio, jurisdictions, systems, data uses and applicable obligations. DataConsultant does not provide legal advice or guarantee regulatory compliance.
Research Data Governance Control RoomIllustrative operating model
Research sources
01Clinical trial / eCRF data
02Laboratory, assay & omics
03RWD, registries & imaging
04Documents & study metadata
Governed Research Data Estate
Controlled data products, metadata, provenance and approved use
Study & dataset identity
Quality & lineage evidence
Access & purpose controls
Standards & lifecycle rules
Governance gates
✓Owner & steward approval
✓Quality / metadata readiness
!Privacy / consent constraints
→Approved sharing / reuse route
Traceable provenance
Controlled access
Reproducible analysis
Evidence for oversight
Accountable ownershipSponsor, investigator, owner, steward and custodian responsibilities
Traceable data lifecycleSource-to-analysis lineage, provenance and change evidence
Risk-aware approved usePrivacy, consent, access, sharing, retention and oversight
Interoperable research dataStandards, metadata, identifiers and reusable data products
Why it matters
01Research Data Risk Usually Appears Between Teams, Systems and Uses
Research data moves across investigators, CROs, laboratories, clinical systems, analytics environments, repositories, regulators, publications and secondary-use programmes. Governance creates a connected decision system for that movement rather than relying on study-by-study workarounds.
Broken provenance
Transformations, derivations and hand-offs are difficult to reconstruct, weakening reproducibility, investigation and evidence for downstream use.
Inconsistent standards
Different programmes use competing identifiers, terms, structures and metadata, making integration and cross-study reuse expensive and error-prone.
Purpose and access drift
Data moves into new repositories or analytical uses without a clear link to approved purpose, permission, role, sharing condition or retention expectation.
Quality issues repeat
Teams correct symptoms in downstream datasets while root causes, critical elements, rule ownership and remediation evidence remain fragmented.
Metadata is incomplete
Datasets may exist without sufficient context about study, population, variables, methods, provenance, sensitivity, quality or intended reuse.
Supplier boundaries are weak
CRO, laboratory, cloud, platform and analytics providers can create unclear custody, transfer, change, evidence and exit responsibilities.
AI reuse outpaces governance
Research datasets become model inputs without consistent documentation of provenance, approved use, representativeness, leakage risk or evaluation boundaries.
Current state → target state
02Move from Study-by-Study Controls to a Reusable Research Governance Capability
The target is not a central team that owns every research decision. It is a clear operating model where accountability, standards and evidence can be applied consistently while programmes retain scientific responsibility.
Current State
Typical symptoms in fragmented research environments
↘Data-management plans vary without shared minimum controls
↘Dataset ownership differs from system or study ownership
↘Metadata lives in documents, spreadsheets and local tools
↘Lineage is reconstructed manually after issues occur
↘Quality thresholds are inconsistent across programmes
↘Secondary-use approval is slow or poorly evidenced
↘Supplier and repository hand-offs lack standard evidence
↘Archival and retention decisions are disconnected from use
Target State
Governed research data capability embedded in the lifecycle
✓Named owners, stewards and decision authorities
✓Minimum metadata, identifier and provenance standards
✓Critical-data and quality rules tied to intended use
✓Traceable source, transformation and analytical lineage
✓Risk-based access, sharing and secondary-use workflows
✓Controlled vendor, repository and transfer interfaces
✓Lifecycle, retention and archive responsibilities defined
✓Governance KPIs, issue workflow and improvement cadence
Better-supported research decisions begin with data that is understood, governed and fit for approved use.
Govern the Research Data You Actually Need to Reuse, Share and Defend
Define the study types, data domains, systems, jurisdictions and decisions that should determine your governance scope.
Research value chain
03Governance Must Follow the Research Data Lifecycle
The relevant governance decisions change as data moves from protocol and collection through transformation, analysis, disclosure, sharing and archive. The service maps controls to those real process transitions.
01
Plan
Protocol, objectives, endpoints, DMP, standards, roles
02
Collect
Clinical, laboratory, imaging, registry and external data
03
Integrate
Transfer, ingest, reconcile, identify and standardise
04
Curate
Clean, classify, document, enrich and quality-control
05
Analyse
Derive, model, validate, interpret and reproduce
06
Share
Submit, publish, exchange, grant access and reuse
07
Retain
Archive, preserve context, control access and dispose
Research data domains
04Different Research Data Types Need Different Control and Metadata Depth
Governance should distinguish the meaning, sensitivity, provenance, quality and reuse expectations of each domain rather than forcing every dataset into one rule set.
Study & protocolProtocol versions, endpoints, arms, sites, study identifiers
Participant / subjectIdentity links, coded subject data, demographics, consent context
Clinical observationseCRF, outcomes, vitals, assessments and clinical events
Laboratory & assaySpecimens, methods, measurements, units and quality context
Omics & molecularGenomic, transcriptomic, proteomic and derived research data
ImagingImages, annotations, acquisition context and derived features
Real-world dataEHR, claims, registries, devices and external observational sources
SafetyEvents, cases, signals, coding, reconciliation and reporting context
Analysis datasetsDerived variables, populations, statistical code and outputs
Metadata & provenanceDefinitions, origin, transformations, quality, owner and permitted use
What the service covers
05Research Data Governance from Accountability to Operational Control
DataConsultant can scope the work as an assessment, target-state design, implementation project or ongoing governance capability. Workstreams are selected according to the decisions and evidence required.
Current-state assessment
Review research portfolio, systems, policies, roles, flows, metadata, quality, access, sharing, retention and evidence gaps.
Ownership & stewardship
Define accountable data owners, study roles, stewards, custodians, control owners, forums and escalation routes.
Policy & standards
Translate research-data principles into minimum standards for naming, definitions, metadata, quality, documentation and lifecycle.
Metadata & catalogue
Specify study, dataset, variable, provenance, owner, sensitivity, quality, usage and access metadata needed for discovery and control.
Lineage & provenance
Map source-to-target flows, transformations, derivations, versioning and downstream dependencies for critical research data.
Research data quality
Define critical elements, rules, thresholds, reconciliation, issue management, root-cause ownership and monitoring.
Access, privacy & consent
Connect approved use with classification, permission, role, purpose, de-identification, sharing and review requirements.
Sharing & secondary use
Design intake, review, decision, evidence, repository, transfer and access workflows for internal and external reuse.
Lifecycle & records
Clarify active-use, archive, preservation, retention, legal-hold inputs, deletion and responsibility boundaries.
AI / model data governance
Add dataset documentation, approved use, representativeness, leakage, validation separation, model-input lineage and oversight controls where relevant.
Research Data Governance framework
06Ten Connected Control Areas for Defensible Research Data Use
The framework links business and scientific accountability with metadata, data quality, technology and evidence. It can be tailored to one research programme, a therapeutic area, an R&D function or an enterprise data capability.
Scope & purpose
Research objectives, use boundaries, stakeholders and decisions
Accountability
Owner, steward, investigator, custodian and approver roles
Data inventory
Studies, datasets, critical elements, systems and suppliers
Standards
Definitions, identifiers, terminology, structures and documentation
Metadata & lineage
Context, origin, transformation, version, quality and dependency
Quality controls
Rules, thresholds, reconciliation, issue ownership and evidence
Access & sharing
Purpose, permissions, role, privacy, security and transfer
Lifecycle
Active use, retention, preservation, archive and deletion
Change & exceptions
New sources, uses, releases, deviations and risk acceptance
Evidence & monitoring
KPIs, logs, reviews, issue trends, approvals and improvement
Architecture and data flow
07Governance Must Be Designed into the Research Data Architecture
DataConsultant remains vendor-neutral. The architecture work focuses on how research data is identified, integrated, controlled, described, transformed and made available for approved consumption across the organisation’s actual technology estate.
Typical source and operating systems
Actual platforms are confirmed during discovery; no client stack is assumed.
EDC / eCRF and clinical data management
CTMS, eTMF and research operations
LIMS, laboratory and assay platforms
EHR, registries and real-world sources
Imaging, devices and digital endpoints
Biostatistics and analytical environments
Document, repository and archival systems
Data platforms, lakehouses and warehouses
Governed research data flow
Controls should remain visible across ingestion, transformation and downstream use.
Acquire & integrate
- Controlled transfer and ingestion
- Identity and study context
- Validation and reconciliation
- Source and supplier evidence
Governed data layer
- Catalogue and metadata
- Lineage and provenance
- Quality and issue status
- Classification and approved-use context
- Version and change history
Consume & reuse
- Biostatistics and reporting
- Submission / disclosure support
- Research repositories
- Secondary analysis and RWE
- Approved AI / ML use
Data quality and controls
08Quality Controls Should Reflect Research Risk and Intended Use
Not every field deserves the same control intensity. The service helps identify critical data, define measurable rules and assign ownership for exceptions, remediation and evidence.
Scientific meaningDefinitions, endpoints, units, terminology and analytical interpretation
CompletenessRequired events, observations, metadata and expected records
ValidityPermitted values, ranges, formats, relationships and protocol logic
ConsistencyCross-source, cross-visit, cross-study and reference-data alignment
TimelinessFreshness, latency, reconciliation windows and downstream availability
TraceabilityOrigin, transformation, derivation, version and decision evidence
| Control area | Example | Owner | Priority |
|---|---|---|---|
| Study identity | Study and site identifiers remain consistent across transfers | Data management | High |
| Participant linkage | Coded identifiers reconcile without exposing direct identity | Study / privacy | High |
| Critical endpoints | Required endpoint values, units and visit context are complete | Clinical / statistics | High |
| Laboratory data | Method, unit, reference range and specimen context are preserved | Lab / data steward | Medium |
| Derived datasets | Derivations reference source variables, code/version and specification | Biostatistics | High |
| Secondary use | Dataset access is linked to approved purpose and required conditions | Governance forum | Controlled |
Governance, risk and regulatory context
09Research Governance Needs Evidence, Boundaries and Specialist Review
Depending on jurisdiction, research type, sponsor responsibilities, data handled and intended use, different legal, regulatory, ethical, security and contractual requirements may apply. DataConsultant can map these considerations into governance design but does not replace legal, regulatory, ethics or statutory assurance specialists.
Decision rights
Clarify who proposes, reviews, approves, implements and accepts risk for data collection, reuse, sharing, retention and exceptions.
- Research sponsor and investigator roles
- Data owner and steward authority
- Privacy, security, quality and regulatory escalation
Privacy and participant protection
Connect governance with purpose, minimisation, consent/permission context, de-identification, access, transfer and rights-management inputs.
- Sensitive-data classification
- Role and purpose-based access
- Sharing and secondary-use review
Data integrity and records
Define the evidence needed to demonstrate trustworthy electronic records, controlled change, provenance and lifecycle responsibilities.
- Version and change history
- Traceable transformation evidence
- Retention and archive controls
Third-party and transfer control
Make CRO, laboratory, platform, repository and data-sharing responsibilities explicit across hand-offs and service boundaries.
- Custody and transfer expectations
- Quality and evidence handover
- Exit and data-return responsibilities
Standards and interoperability
Define where common terminology, data models, identifiers, metadata and exchange standards are required for controlled interoperability.
- CDISC-aligned study data where applicable
- Controlled terminology and identifiers
- Repository and exchange metadata
Assurance and monitoring
Establish review cadence, control evidence, issue management, exceptions, KPIs and audit-ready decision records proportionate to the risk.
- Governance health indicators
- Issue and exception backlog
- Periodic control review
Regulatory interpretation boundary: references below are authoritative sources that may be relevant to some healthcare and life-sciences research environments. Applicability must be confirmed for the organisation, country, entity, study, product and data use. DataConsultant can help translate confirmed obligations into ownership, controls, data requirements and evidence.
India: New Drugs and Clinical Trials Rules, 2019CDSCO source for India clinical-trial regulatory requirements and amendments.
India: ICMR ethical guidelinesICMR source for ethical guidance relevant to biomedical and health research involving human participants.
India: Digital Personal Data Protection Rules, 2025MeitY source for the DPDP Rules and related implementation material.
ICH E6(R3) Good Clinical PracticeCurrent GCP guidance incorporating risk-based approaches and modern data and technology practices.
FDA electronic systems and records guidanceFinal guidance on trustworthy electronic systems, records and signatures in clinical investigations.
NIH Data Management and Sharing PolicyNIH expectations for planning, managing and sharing scientific data where the policy applies.
CDISC SDTMFoundational clinical study data model and terminology context for standardised study data.
AI and model considerations
10Research Data Reused for AI Needs Additional Governance
When clinical or research data becomes training, validation, grounding or evaluation data, governance should make the dataset’s origin, permitted use, limitations and transformation history visible to model and risk owners.
Dataset documentation
Record purpose, provenance, population, collection context, transformations, known limitations and approved uses.
Representativeness
Review whether the data reflects the intended population, setting, timeframe and model task, with limitations made explicit.
Leakage and access
Control sensitive fields, unauthorised reuse, training/validation contamination, prompt exposure and downstream access paths.
Model-input lineage
Connect model versions to datasets, features, transformations, evaluation evidence, release decisions and later changes.
Delivery methodology
11From Research Priorities to an Operable Governance Model
Delivery is evidence-led and adapted to the maturity of the organisation. A focused assessment can stop after recommendations, while a broader programme can continue through design, implementation and managed operation.
Align
Confirm research outcomes, sponsors, portfolio scope, constraints and decision criteria.
Output: agreed scopeDiscover
Map stakeholders, systems, policies, data domains, vendors, flows and existing standards.
Output: evidence mapAssess
Review ownership, metadata, quality, lineage, access, sharing, lifecycle and control gaps.
Output: findings registerDesign
Define target governance, data standards, workflows, controls, architecture and operating roles.
Output: target modelPrioritise
Sequence domains, controls, technology changes and remediation by value, risk and dependency.
Output: roadmapImplement
Support workflow, catalogue, quality, lineage, policy, reporting and role enablement.
Output: operating controlsTransition
Validate adoption, transfer knowledge, establish KPIs, review cadence and improvement backlog.
Output: handoverTurn Governance Design into Working Research Data Controls
DataConsultant can continue into implementation, platform enablement, operating-model activation, training and managed governance support.
Tangible deliverables
12Outputs Designed for Research, Data, Technology and Control Teams
Final deliverables depend on the agreed scope. The following are representative outputs for a substantial Research Data Governance engagement.
Current-state assessment
Evidence-backed findings across research data, systems, roles, quality and controls.
Research data-domain map
Studies, datasets, systems, producers, consumers, owners and critical dependencies.
Governance charter
Mandate, scope, authority, principles, forums, decisions and escalation model.
Ownership & RACI
Research, data, platform and control responsibilities for material decisions.
Metadata specification
Required study, dataset, variable, provenance, quality, owner and use metadata.
Lineage model
Critical source-to-analysis transformations, derivations and downstream dependencies.
Quality-control catalogue
Critical elements, rule logic, thresholds, evidence, ownership and remediation workflow.
Access & sharing workflow
Purpose, review, approval, transfer, repository, exception and evidence requirements.
Target architecture
Governance-enabled data flow, catalogue, quality, lineage, security and consumption design.
Roadmap & KPI model
Priorities, dependencies, owners, milestones, adoption measures and operating cadence.
Engagement models and commercial treatment
13Select the Engagement Depth That Matches the Research Decision
No fixed price or duration is published for this service. Scope depends on the research portfolio, systems, jurisdictions, data domains, evidence depth, stakeholder participation, deliverables and implementation responsibilities.
Research Governance Assessment
For leaders who need an independent view of current gaps, material risks and priority actions.
- Discovery and evidence review
- Maturity / gap assessment
- Prioritised recommendations
- Executive decision summary
Target Governance Blueprint
For organisations that need a complete operating model, controls, standards and architecture direction.
- Ownership and stewardship design
- Metadata, quality and lineage model
- Access / sharing workflows
- Implementation roadmap
Governance Mobilisation
For teams moving from approved design into working processes, tools, reporting and role activation.
- Workflow and policy operationalisation
- Catalogue / quality / lineage enablement
- Role onboarding and training
- Delivery assurance and handover
Managed Governance Support
For organisations that need recurring coordination, monitoring, issue management and continuous improvement.
- Governance calendar and forums
- Issue / exception tracking
- KPI and control reporting
- Continuous improvement backlog
PricingRequest a QuoteScope-led commercial proposal
Key scoping factors Number of research programmes and domains · systems and suppliers · countries / entities · privacy and regulatory review needs · metadata and lineage depth · quality-control complexity · workshops and stakeholder groups · implementation and operating support.
Request a Scope-Based Estimate →What we need from you
14Useful Evidence for a Faster, More Defensible Assessment
Missing evidence is recorded as a limitation rather than assumed. A complete environment is not required to start, but access to accountable stakeholders and a representative sample of evidence materially improves the quality of the assessment.
Research portfolio context
Programmes, study types, therapeutic areas, geographies and intended research uses.
Policies and plans
Data-management plans, governance policies, privacy/security procedures and retention schedules.
Systems and vendors
EDC, LIMS, CTMS, repositories, analytics platforms, CROs, laboratories and data providers.
Data flows and metadata
Architecture diagrams, transfer specifications, dictionaries, standards and lineage evidence.
Quality and issue evidence
Validation reports, reconciliation results, issue logs, audit findings and recurring defects.
Stakeholders and obligations
Research, clinical, statistical, quality, privacy, security, regulatory, legal and platform owners.
Frequently asked questions
16Research Data Governance Questions
Practical answers for research, data, technology, quality, privacy and governance leaders evaluating service scope, implementation and commercial fit.
What is Research Data Governance?
Research Data Governance is the operating framework for deciding how research data is defined, owned, collected, accessed, transformed, shared, retained, archived and reused. In healthcare and life sciences it connects scientific objectives with participant protection, data quality, metadata, lineage, security, privacy, records, standards, reproducibility and accountable decision-making.
Which research data can be included in scope?
Scope can include clinical-trial data, observational and real-world data, laboratory and assay data, omics data, imaging data, registry data, biospecimen metadata, protocol and operational data, safety data, statistical datasets, research documents, metadata and approved secondary-use datasets. The exact boundary should be agreed by study type, jurisdiction, system landscape and intended use.
Who should sponsor a Research Data Governance programme?
Sponsorship commonly comes from research, clinical development, data, digital, medical, R&D operations or enterprise technology leadership. Effective governance also needs participation from investigators, data management, biostatistics, clinical operations, laboratory teams, privacy, security, records, quality, regulatory, legal and platform owners where relevant.
How is Research Data Governance different from clinical data management?
Clinical data management focuses on operational collection, cleaning and preparation of study data. Research Data Governance is broader: it establishes enterprise and programme-level accountability, policies, standards, metadata, lineage, access, retention, sharing, quality controls, secondary-use decision rights and evidence across multiple research data types and systems.
Can the service support FAIR data practices?
Yes. The service can translate findability, accessibility, interoperability and reusability goals into practical metadata, identifiers, catalogue, standards, access, provenance, quality and stewardship requirements. FAIR does not mean unrestricted access; privacy, consent, contractual, ethical, security and regulatory constraints still apply.
Does the service include privacy and consent controls?
It can include privacy-aware data classification, purpose and approved-use mapping, consent and permission dependencies, access principles, de-identification or pseudonymisation considerations, sharing controls, retention inputs and evidence requirements. Formal legal interpretation should remain with appropriately qualified legal and privacy professionals.
Can DataConsultant help with clinical-trial data standards such as CDISC?
The engagement can assess where standards such as CDISC structures, controlled terminology and metadata should influence data collection, transformation, exchange and downstream reuse. Detailed regulatory submission implementation should be scoped around the relevant sponsor processes, authorities, standards versions and specialist responsibilities.
How are metadata and lineage handled?
The service can define required metadata for studies, datasets, variables, transformations, owners, sensitivity, provenance, quality and permitted use. Lineage can be mapped from source collection through ingestion, cleaning, derivation, analysis, reporting, sharing and archive so material transformations and downstream dependencies remain traceable.
Can governance cover AI and machine-learning use of research data?
Yes. Where research data is used for machine learning or generative AI, governance can add approved-use checks, dataset documentation, provenance, representativeness, sensitive-data controls, training and validation separation, model-input lineage, human oversight, evaluation evidence and change controls. Governance does not guarantee model accuracy, safety or regulatory acceptance.
What deliverables can we expect?
Typical outputs can include a current-state assessment, research-data domain map, critical-data inventory, governance charter, ownership and stewardship model, metadata and lineage requirements, quality-control catalogue, access and sharing workflow, retention and lifecycle model, target architecture, operating model, implementation backlog, KPI framework and executive decision pack.
Can DataConsultant support implementation after the governance design?
Yes. Follow-on support can include policy operationalisation, catalogue and metadata configuration, quality-rule implementation, lineage enablement, workflow design, data-platform integration, reporting, role onboarding, training, delivery assurance and managed governance support. Responsibilities and acceptance criteria should be agreed before implementation begins.
How is Research Data Governance pricing determined?
DataConsultant does not publish a fixed price for this service. Pricing is scope-led and depends on research programmes, data domains, systems, countries or entities, stakeholder groups, assessment depth, metadata and lineage requirements, control complexity, workshops, deliverables, implementation support and ongoing operating needs. A written estimate is prepared after discovery.
What information should we prepare before starting?
Useful inputs include research portfolio context, study or programme types, data-management plans, policies, system and vendor inventories, architecture diagrams, data flows, metadata standards, quality findings, audit observations, access and sharing procedures, retention schedules, current role definitions, applicable obligations and access to accountable research and control stakeholders.
Research Data Governance Enquiry
Request a Research Data Governance Scope Review
Share your requirement. DataConsultant can review likely scope, evidence needs, stakeholder involvement, delivery approach and commercial next step.
Build Research Data Governance Around Evidence, Not Assumptions
Connect scientific objectives, accountable ownership, data quality, metadata, lineage, approved use and operational controls into one implementable governance capability.
Research-specific operating modelTraceable data lifecyclePrivacy-aware controlsImplementation-ready roadmap