Data Stewards and Data Governance Teams
5 minutes
5 Questions
In the PMI-CPMAI framework, the Identify Data Needs work sits within the Data Understanding phase. Here the AI project team determines what data is required, where it lives, who controls it, and whether it can legally and ethically be used. Data Stewards and Data Governance Teams are key stakeholders in this effort because AI projects are data-centric, and most AI failures trace back to data issues rather than algorithms. Data Stewards are subject-matter custodians responsible for specific data domains, such as customer, financial, or operational data. They understand the meaning, lineage, quality, and limitations of the data they oversee. For an AI project manager, stewards help answer critical questions. Does the needed data exist? What does each field actually represent? How accurate, complete, and current is it? Are there known biases or gaps? Stewards also maintain metadata and data dictionaries, and they can grant or facilitate access. Engaging them early prevents costly misunderstandings during data preparation and model training. Data Governance Teams operate at the organizational level. They define the policies, standards, roles, and controls for how data is collected, stored, shared, secured, and retained. Their concerns include privacy regulations such as GDPR, HIPAA, and CCPA, consent and permitted use, data classification, security, retention rules, and ethical or responsible AI considerations. They approve whether data can be repurposed for AI training and may require anonymization, masking, or data-sharing agreements. For CPMAI practitioners, best practices include identifying stewards and governance contacts during stakeholder analysis, documenting data sources and ownership, confirming usage rights before committing to a solution, and building governance approval time into the project schedule. Collaboration with these groups supports trustworthy AI, reduces compliance risk, and ensures that data quality and quantity are sufficient. Ultimately, they help the project answer a central CPMAI go or no-go question: do we have the right data, in the right condition, with the right permissions, to deliver the AI solution?
Data Stewards and Data Governance Teams (PMI-CPMAI: Identify Data Needs)
Overview
In the PMI Cognitive Project Management for AI (CPMAI) methodology, the Identify Data Needs phase (Phase II: Data Understanding) is where the project team works out what data is required, where it lives, who owns it, and whether it can legally and ethically be used. Two of the most important stakeholder groups in this phase are Data Stewards and Data Governance Teams. They are the people who know the data, control access to it, and make sure its use is compliant, high quality and well documented. CPMAI treats AI projects as data projects first. Knowing who these stakeholders are, and when and how to engage them, is a core exam competency.
Why It Is Important
1. AI projects fail mainly because of data. CPMAI stresses that most AI project failures come from data issues, not from algorithms. Common causes include data that is unavailable, of poor quality, poorly understood or legally unusable. Data stewards and governance teams are the first line of defense against these risks.
2. Access and permissions. You cannot build a model on data you are not allowed to use. Governance teams grant, restrict or condition access based on policy, contracts, consent and regulation (for example GDPR, HIPAA or CCPA).
3. Data quality and meaning. Data stewards understand business definitions, lineage, known defects and context. Without them, teams misread fields, introduce bias or train on stale or corrupted data.
4. Trustworthy and responsible AI. Governance supports privacy, security, transparency, fairness and auditability. All of these are key elements of trustworthy AI in CPMAI.
5. Go/No-Go decisions. CPMAI is iterative and includes checkpoints. If governance blocks data use, or stewards confirm the data is insufficient, the project may need to iterate back to Business Understanding or be stopped. Engaging these groups early prevents wasted effort.
6. Organizational alignment. Governance ensures AI work follows enterprise data strategy, master data management and retention policies. This avoids shadow data pipelines and duplicated datasets.
What It Is
Data Steward: A person, usually from the business or a subject matter area, who is accountable for the quality, definition, appropriate use and maintenance of a specific data domain (for example customer data, product data or claims data). Key traits:
- Knows what each data element means, where it comes from and its known limitations.
- Maintains metadata, data dictionaries and business glossaries.
- Monitors and remediates data quality issues.
- Acts as a bridge between business users and technical teams.
- Often approves or advises on access requests for their domain.
Common steward types:
- Business data stewards focus on definitions and business rules.
- Technical data stewards focus on systems, schemas and lineage.
- Domain or operational stewards handle day-to-day data quality in a domain.
Data Owner (related role): Usually a senior business leader who is ultimately accountable for a data asset and makes the final decisions on access and use. Stewards often act on the owner's behalf. On exams, the owner is accountable and the steward manages the data day to day.
Data Custodian (related role): Usually IT or data engineering staff who store, secure, back up and technically manage the data according to the rules that governance and owners define.
Data Governance Team / Council: A cross-functional body that sets and enforces the policies, standards, processes and decision rights for how data is collected, stored, accessed, shared, protected and retired. Members often include the Chief Data Officer (CDO), legal and compliance, privacy officers, security (CISO), data owners, stewards and sometimes ethics or AI governance representatives. Their responsibilities include:
- Data policies covering classification, retention, privacy and sharing.
- Regulatory and contractual compliance.
- Approving data use for new purposes, such as training an AI model.
- Data quality standards and metrics.
- Metadata and catalog standards.
- Resolving data disputes and setting priorities.
- Increasingly, AI governance: model risk, responsible AI and bias oversight.
How It Works in a CPMAI Project
Step 1: Identify the data required (from Phase I, Business Understanding). The AI pattern and success criteria defined earlier tell you what data is needed. Examples include labeled images for recognition, transaction history for predictive analytics, or text for conversational AI.
Step 2: Locate data sources and their stewards and owners. Use the data catalog, governance office or enterprise architecture to find who is responsible for each candidate dataset. Add them to the stakeholder register.
Step 3: Engage stewards for data understanding. Work with stewards to confirm:
- Field definitions and business meaning.
- Lineage and how the data is created.
- Known quality issues, gaps and biases.
- Volume, velocity, variety and veracity (the V's of big data).
- Whether labels exist and how reliable they are.
Step 4: Engage governance for approval and compliance. Submit data use requests describing the purpose, the data elements, retention and security controls. Governance checks:
- Consent and the legal basis for the new purpose.
- Data classification, such as PII, PHI or confidential.
- Requirements for anonymization or pseudonymization.
- Cross-border transfer restrictions.
- Third-party and vendor data licensing terms.
- Ethical and responsible AI considerations.
Step 5: Document decisions. Record approvals, constraints and conditions, for example "data must be masked" or "only aggregated data." Record data quality findings and any remediation plans. This supports auditability and later phases (Data Preparation, Model Evaluation, Operationalization).
Step 6: Feed into Go/No-Go. If data is unavailable, unapproved or insufficient, the team decides whether to source alternative data, generate synthetic data, collect new data, narrow the scope, or iterate back to Business Understanding.
Step 7: Ongoing collaboration. In operationalization, stewards and governance stay involved. They monitor data drift, data quality in production pipelines, retention, access reviews and model governance.
Key Distinctions to Remember
- Data Governance is the framework of policies, standards and decision rights.
- Data Management is the execution of those policies: storage, integration, quality operations.
- Data Steward is accountable for the quality and meaning of a data domain.
- Data Owner is ultimately accountable for, and authorizes, use of the data.
- Data Custodian provides the technical safekeeping.
- Data Scientist / AI Team consumes the data and must comply with governance. They do not decide on their own what data use is allowed.
Common Scenarios
- The team finds a promising dataset containing customer PII. Correct action: consult the data owner, steward and governance or privacy office before using it.
- Model results are strange because a field means something different than assumed. Correct action: work with the data steward to clarify definitions and lineage.
- A vendor dataset is available. Correct action: have governance and legal review the license and terms of use for AI training.
- Data quality is poor. Correct action: engage stewards to assess root causes and remediation. Do not simply drop the data or proceed silently.
Exam Tips: Answering Questions on Data Stewards and Data Governance Teams
1. Engage early. The best answer usually involves stewards and governance in the Data Understanding phase, before data preparation or modeling. Answers that delay governance until deployment are usually wrong.
2. Do not bypass governance. Reject options where the data scientist or project manager uses data first and asks permission later. Also reject options that use data simply because it is technically accessible. Being able to access data is not the same as being authorized to use it.
3. Match the question to the role.
- Questions about meaning, definitions, quality or lineage point to the data steward.
- Questions about policy, compliance, approval or cross-domain decisions point to the governance team.
- Questions about final accountability or authorization point to the data owner.
- Questions about storage, security or infrastructure point to the custodian / IT.
4. Watch for privacy triggers. Words like PII, PHI, consent, regulation, GDPR, cross-border or sensitive mean the answer should involve governance, privacy or legal review.
5. Repurposing data needs review. Data collected for one purpose (for example billing) and reused for AI training often needs new approval. Choose answers that verify permitted use.
6. Data issues come before algorithms. If a question describes poor model performance with unclear data, the CPMAI-aligned answer usually goes back to data understanding with stewards, not to tuning the model.
7. Iterate, do not force. If governance denies access, the correct response is to explore alternatives (synthetic data, anonymization, a different data source, a smaller scope) or revisit Business Understanding. Do not escalate to override policy.
8. Documentation matters. Prefer answers that record data approvals, constraints, metadata and quality findings. This supports trustworthy, auditable AI.
9. Collaboration is better than unilateral action. PMI exams favor communication, stakeholder engagement and cross-functional alignment. Choose the option that brings the right stakeholders together.
10. Keep the lifecycle in mind. Stewards and governance stay relevant after deployment, for drift monitoring, access reviews and retention. Answers implying their job ends at data handoff are incomplete.
11. Eliminate extreme options. Avoid answers like "cancel the project immediately" or "proceed without approval." Look for the measured, process-driven option.
Quick Memory Aid
Stewards KNOW the data, Owners DECIDE on the data, Custodians PROTECT the data, Governance RULES the data, and the AI team USES the data responsibly.
Summary
Data Stewards and Data Governance Teams are essential stakeholders in the CPMAI Identify Data Needs step. Stewards provide deep knowledge of data meaning and quality. Governance teams provide the policies, approvals and compliance oversight that make data use legitimate and trustworthy. On the exam, favor answers that:
- engage these groups early,
- respect authorization and privacy,
- clarify data with stewards before modeling,
- document decisions, and
- iterate responsibly when data constraints arise.
In the PMI Cognitive Project Management for AI (CPMAI) methodology, the Identify Data Needs phase (Phase II: Data Understanding) is where the project team works out what data is required, where it lives, who owns it, and whether it can legally and ethically be used. Two of the most important stakeholder groups in this phase are Data Stewards and Data Governance Teams. They are the people who know the data, control access to it, and make sure its use is compliant, high quality and well documented. CPMAI treats AI projects as data projects first. Knowing who these stakeholders are, and when and how to engage them, is a core exam competency.
Why It Is Important
1. AI projects fail mainly because of data. CPMAI stresses that most AI project failures come from data issues, not from algorithms. Common causes include data that is unavailable, of poor quality, poorly understood or legally unusable. Data stewards and governance teams are the first line of defense against these risks.
2. Access and permissions. You cannot build a model on data you are not allowed to use. Governance teams grant, restrict or condition access based on policy, contracts, consent and regulation (for example GDPR, HIPAA or CCPA).
3. Data quality and meaning. Data stewards understand business definitions, lineage, known defects and context. Without them, teams misread fields, introduce bias or train on stale or corrupted data.
4. Trustworthy and responsible AI. Governance supports privacy, security, transparency, fairness and auditability. All of these are key elements of trustworthy AI in CPMAI.
5. Go/No-Go decisions. CPMAI is iterative and includes checkpoints. If governance blocks data use, or stewards confirm the data is insufficient, the project may need to iterate back to Business Understanding or be stopped. Engaging these groups early prevents wasted effort.
6. Organizational alignment. Governance ensures AI work follows enterprise data strategy, master data management and retention policies. This avoids shadow data pipelines and duplicated datasets.
What It Is
Data Steward: A person, usually from the business or a subject matter area, who is accountable for the quality, definition, appropriate use and maintenance of a specific data domain (for example customer data, product data or claims data). Key traits:
- Knows what each data element means, where it comes from and its known limitations.
- Maintains metadata, data dictionaries and business glossaries.
- Monitors and remediates data quality issues.
- Acts as a bridge between business users and technical teams.
- Often approves or advises on access requests for their domain.
Common steward types:
- Business data stewards focus on definitions and business rules.
- Technical data stewards focus on systems, schemas and lineage.
- Domain or operational stewards handle day-to-day data quality in a domain.
Data Owner (related role): Usually a senior business leader who is ultimately accountable for a data asset and makes the final decisions on access and use. Stewards often act on the owner's behalf. On exams, the owner is accountable and the steward manages the data day to day.
Data Custodian (related role): Usually IT or data engineering staff who store, secure, back up and technically manage the data according to the rules that governance and owners define.
Data Governance Team / Council: A cross-functional body that sets and enforces the policies, standards, processes and decision rights for how data is collected, stored, accessed, shared, protected and retired. Members often include the Chief Data Officer (CDO), legal and compliance, privacy officers, security (CISO), data owners, stewards and sometimes ethics or AI governance representatives. Their responsibilities include:
- Data policies covering classification, retention, privacy and sharing.
- Regulatory and contractual compliance.
- Approving data use for new purposes, such as training an AI model.
- Data quality standards and metrics.
- Metadata and catalog standards.
- Resolving data disputes and setting priorities.
- Increasingly, AI governance: model risk, responsible AI and bias oversight.
How It Works in a CPMAI Project
Step 1: Identify the data required (from Phase I, Business Understanding). The AI pattern and success criteria defined earlier tell you what data is needed. Examples include labeled images for recognition, transaction history for predictive analytics, or text for conversational AI.
Step 2: Locate data sources and their stewards and owners. Use the data catalog, governance office or enterprise architecture to find who is responsible for each candidate dataset. Add them to the stakeholder register.
Step 3: Engage stewards for data understanding. Work with stewards to confirm:
- Field definitions and business meaning.
- Lineage and how the data is created.
- Known quality issues, gaps and biases.
- Volume, velocity, variety and veracity (the V's of big data).
- Whether labels exist and how reliable they are.
Step 4: Engage governance for approval and compliance. Submit data use requests describing the purpose, the data elements, retention and security controls. Governance checks:
- Consent and the legal basis for the new purpose.
- Data classification, such as PII, PHI or confidential.
- Requirements for anonymization or pseudonymization.
- Cross-border transfer restrictions.
- Third-party and vendor data licensing terms.
- Ethical and responsible AI considerations.
Step 5: Document decisions. Record approvals, constraints and conditions, for example "data must be masked" or "only aggregated data." Record data quality findings and any remediation plans. This supports auditability and later phases (Data Preparation, Model Evaluation, Operationalization).
Step 6: Feed into Go/No-Go. If data is unavailable, unapproved or insufficient, the team decides whether to source alternative data, generate synthetic data, collect new data, narrow the scope, or iterate back to Business Understanding.
Step 7: Ongoing collaboration. In operationalization, stewards and governance stay involved. They monitor data drift, data quality in production pipelines, retention, access reviews and model governance.
Key Distinctions to Remember
- Data Governance is the framework of policies, standards and decision rights.
- Data Management is the execution of those policies: storage, integration, quality operations.
- Data Steward is accountable for the quality and meaning of a data domain.
- Data Owner is ultimately accountable for, and authorizes, use of the data.
- Data Custodian provides the technical safekeeping.
- Data Scientist / AI Team consumes the data and must comply with governance. They do not decide on their own what data use is allowed.
Common Scenarios
- The team finds a promising dataset containing customer PII. Correct action: consult the data owner, steward and governance or privacy office before using it.
- Model results are strange because a field means something different than assumed. Correct action: work with the data steward to clarify definitions and lineage.
- A vendor dataset is available. Correct action: have governance and legal review the license and terms of use for AI training.
- Data quality is poor. Correct action: engage stewards to assess root causes and remediation. Do not simply drop the data or proceed silently.
Exam Tips: Answering Questions on Data Stewards and Data Governance Teams
1. Engage early. The best answer usually involves stewards and governance in the Data Understanding phase, before data preparation or modeling. Answers that delay governance until deployment are usually wrong.
2. Do not bypass governance. Reject options where the data scientist or project manager uses data first and asks permission later. Also reject options that use data simply because it is technically accessible. Being able to access data is not the same as being authorized to use it.
3. Match the question to the role.
- Questions about meaning, definitions, quality or lineage point to the data steward.
- Questions about policy, compliance, approval or cross-domain decisions point to the governance team.
- Questions about final accountability or authorization point to the data owner.
- Questions about storage, security or infrastructure point to the custodian / IT.
4. Watch for privacy triggers. Words like PII, PHI, consent, regulation, GDPR, cross-border or sensitive mean the answer should involve governance, privacy or legal review.
5. Repurposing data needs review. Data collected for one purpose (for example billing) and reused for AI training often needs new approval. Choose answers that verify permitted use.
6. Data issues come before algorithms. If a question describes poor model performance with unclear data, the CPMAI-aligned answer usually goes back to data understanding with stewards, not to tuning the model.
7. Iterate, do not force. If governance denies access, the correct response is to explore alternatives (synthetic data, anonymization, a different data source, a smaller scope) or revisit Business Understanding. Do not escalate to override policy.
8. Documentation matters. Prefer answers that record data approvals, constraints, metadata and quality findings. This supports trustworthy, auditable AI.
9. Collaboration is better than unilateral action. PMI exams favor communication, stakeholder engagement and cross-functional alignment. Choose the option that brings the right stakeholders together.
10. Keep the lifecycle in mind. Stewards and governance stay relevant after deployment, for drift monitoring, access reviews and retention. Answers implying their job ends at data handoff are incomplete.
11. Eliminate extreme options. Avoid answers like "cancel the project immediately" or "proceed without approval." Look for the measured, process-driven option.
Quick Memory Aid
Stewards KNOW the data, Owners DECIDE on the data, Custodians PROTECT the data, Governance RULES the data, and the AI team USES the data responsibly.
Summary
Data Stewards and Data Governance Teams are essential stakeholders in the CPMAI Identify Data Needs step. Stewards provide deep knowledge of data meaning and quality. Governance teams provide the policies, approvals and compliance oversight that make data use legitimate and trustworthy. On the exam, favor answers that:
- engage these groups early,
- respect authorization and privacy,
- clarify data with stewards before modeling,
- document decisions, and
- iterate responsibly when data constraints arise.
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More Data Stewards and Data Governance Teams questions
19 questions (total)
Other Identify Data Needs concepts
Access Controls and User Permissions for DataCloud Storage and Distributed Data RepositoriesCollaboration Tools and Version Control for AI WorkspacesComparing Available Data Against RequirementsCPMAI Phase II: Data UnderstandingData Collection Processes for Ongoing FeedsData Distributions, Bias and GapsData Extraction from Identified SourcesData Freshness and Relevance for TrainingData Gaps and Remediation StrategiesData Governance Fundamentals for AIData Lineage and Usage Documentation for AuditData Ownership and Access PermissionsData Protection Compliance for Project DataData Quality Dimensions: Accuracy, Completeness, ConsistencyData Quality Standards and Acceptance CriteriaData Refresh and Update ProceduresData Representativeness for Target Use CasesData Schema and Structure for Modeling CompatibilityData Storage and Backup for AI ProjectsData Sufficiency for Robust ModelsData Transfers and Migrations to AI EnvironmentsData Types and Formats for AI Model TrainingData Usage Rights and Licensing AgreementsData Volume Requirements and Sampling StrategiesEngaging Business Users on Data Context and MeaningExecutive Summaries of Data Assessment FindingsExploratory Data AnalysisExternal and Third-Party Data SourcesGo/No-Go Decisions on Data ReadinessLegacy Systems and Historical Data ArchivesLocating Domain Experts for Data SourcesMapping Data Requirements to Business ObjectivesMapping Internal Databases and Data WarehousesPresenting Data Readiness to StakeholdersProvisioning Compute for Data Processing and TrainingSecure Development Environments for AI TeamsTechnical Experts on Data Systems and StructuresTemporal and Granularity Requirements for DataTranslating Technical Data Concepts for Business AudiencesValidating Completeness and Accuracy During CollectionVisualizations and Reports for Data Insights