Technical Experts on Data Systems and Structures
5 minutes
5 Questions
In the PMI-CPMAI framework, the Identify Data Needs domain focuses on determining what data an AI solution requires, where that data resides, and whether it is usable. A key part of this work is engaging Technical Experts on Data Systems and Structures. These are the people who understand how an organization's data is physically and logically stored, moved, and governed. These experts typically include data architects, data engineers, database administrators, IT system owners, enterprise integration specialists, and cloud or platform engineers. Business stakeholders and domain experts explain what the data means. Technical experts explain how the data exists: its schemas, formats, storage locations, access methods, volumes, refresh frequencies, and interdependencies across systems. In CPMAI's iterative, data-centric approach, AI project managers rely on these experts to answer several practical questions: - Which source systems hold the relevant data, such as ERP, CRM, data warehouses, data lakes, or streaming platforms? - Is the data structured, semi-structured, or unstructured? - How is the data labeled, and how complete is it? - What pipelines, APIs, or extraction methods are available? - What security, privacy, and compliance controls restrict access? - What lineage and metadata exist to establish trustworthiness? Their input helps the team assess data feasibility early, before significant investment in model development. It also helps them estimate the effort needed for data preparation, integration, and transformation. Finally, it surfaces technical risks such as legacy system limitations, siloed data, inconsistent formats, latency constraints, and scalability issues. Project managers should identify these experts during stakeholder analysis and involve them in data discovery workshops. Their findings should be documented in data inventories or data requirement artifacts. Collaboration with these experts also supports realistic go/no-go decisions in the CPMAI phases. Ultimately, technical data experts bridge the gap between what the AI solution needs and what the organization's infrastructure can actually deliver. This reduces the chance of project failure caused by unavailable, inaccessible, or poor-quality data.
Technical Experts on Data Systems and Structures (PMI-CPMAI: Identify Data Needs)
Overview
In the PMI Cognitive Project Management for AI (PMI-CPMAI) methodology, the Identify Data Needs work belongs to Phase II, Data Understanding. Here the team works out what data the AI solution requires, where that data lives, and whether it can be used. A key activity in this work is engaging technical experts on data systems and structures. These are the people who actually know how the organization's data is stored, structured, moved, secured and maintained.
CPMAI is data-centric. It assumes that most AI project failures come from data problems, not algorithm problems. For that reason, the exam expects you to know who to consult about data and when. Technical experts are the authoritative source on the technical reality of the data landscape.
What It Is
Technical experts on data systems and structures are stakeholders with deep, hands-on knowledge of the organization's data infrastructure. Typical roles include:
• Database administrators (DBAs): know schemas, tables, indexes, performance limits, backup and access controls.
• Data engineers: build and maintain pipelines, ETL/ELT processes, data lakes and warehouses.
• Data architects: design enterprise data models, integration patterns and data flows between systems.
• System and application owners / IT administrators: understand source systems such as ERP, CRM, IoT platforms and legacy applications.
• Cloud and infrastructure engineers: understand storage, compute, networking and scalability.
• Data stewards and data governance specialists (with a technical focus): know metadata, lineage, data dictionaries and access policies.
• Security and integration specialists: understand APIs, encryption, permissions and cross-system connectivity.
They differ from subject matter experts (SMEs) / domain experts, who know what the data means in a business context. Technical experts know where the data is, how it is structured, and how it can be accessed and moved. CPMAI expects both groups to be engaged, because each covers what the other cannot.
Why It Is Important
1. Prevents false assumptions about data availability. Business stakeholders often believe data exists and is easy to use. Technical experts can confirm whether it is truly captured, how far back history goes, and in what format.
2. Reveals data quality and structural issues early. Examples include inconsistent schemas, missing fields, duplicate records, siloed systems, unstructured formats and incompatible identifiers across systems.
3. Clarifies access and feasibility. Experts know about permissions, licensing limits, API rate limits, legacy-system constraints and the effort needed to extract data.
4. Supports the go/no-go decision. CPMAI stresses iterating back to earlier phases if data is not adequate. Technical input is essential to judging whether the project is feasible as scoped.
5. Informs data preparation effort and cost estimates. Data preparation usually takes most of an AI project's effort. Experts help estimate integration, cleansing and transformation work.
6. Identifies governance, privacy and security constraints. Experts know where sensitive data (PII, PHI, financial data) resides and what controls apply.
7. Enables operationalization planning. Even this early, knowing the production data flow helps confirm the model can be fed data reliably once deployed.
How It Works
Step 1: Identify the right experts. Using the business problem and AI pattern chosen in Phase I (Business Understanding), find which systems likely hold the relevant data. Then identify their technical owners.
Step 2: Conduct structured discovery sessions. Typical questions include:
• Where does this data reside (systems, databases, files, cloud stores, third parties)?
• What is the structure (structured, semi-structured, unstructured; schemas; data types)?
• What is the volume, velocity and history available?
• How frequently is the data updated, and what is its latency?
• What are the known quality issues, gaps or inconsistencies?
• How is the data linked across systems (keys, identifiers)?
• What access methods exist (APIs, exports, queries, streaming)?
• What security, privacy, retention and compliance restrictions apply?
• Is there documentation (data dictionaries, metadata, lineage diagrams)?
Step 3: Map data needs to data sources. Build an inventory or data map that links each required data element to its source, owner, format and accessibility. Note any gaps.
Step 4: Assess feasibility and gaps. Together with the experts, determine whether the data is sufficient in quantity, quality and relevance. If it is not, consider options: acquiring third-party data, collecting new data, labeling, synthetic data, or re-scoping the problem.
Step 5: Document and communicate findings. Feed findings into the data understanding deliverables, risk register and stakeholder communications. Raise any blockers to the project sponsor.
Step 6: Iterate. CPMAI is iterative. If technical findings show the data cannot support the intended solution, return to Business Understanding to adjust scope, rather than forcing the project forward.
Key Distinctions to Remember
• Technical experts = how and where data is stored, structured, accessed and moved.
• Domain/business SMEs = what data means, business rules and relevance to the problem.
• Data scientists = whether data is suitable for modeling and which features are useful.
• Data owners/stewards = who has authority to grant use and governance approval.
A strong answer often involves several of these groups working together, but the question's wording usually points to one primary group.
Common Pitfalls CPMAI Warns Against
• Assuming data exists or is usable because the business says so.
• Skipping technical discovery and jumping straight to model selection.
• Underestimating integration effort across siloed or legacy systems.
• Ignoring access, security or privacy restrictions until late in the project.
• Engaging technical experts only after problems appear, instead of proactively.
Exam Tips: Answering Questions on Technical Experts on Data Systems and Structures
1. Match the question to the right expert. If the scenario involves schemas, databases, pipelines, storage, system integration, data formats or technical access, the best answer is usually to consult technical data experts such as DBAs, data engineers or data architects.
2. Choose data validation before modeling. If an option says to start building or selecting a model before confirming data availability with technical experts, it is almost always wrong. CPMAI puts data first.
3. Look for proactive engagement. Prefer answers where experts are engaged early in Data Understanding, not after failures occur.
4. Do not confuse meaning with mechanics. If the issue is what a field means or a business rule, consult domain SMEs. If it is where a field lives or why it cannot be extracted, consult technical experts.
5. Favor verification over assumption. Answers like "confirm with system owners" or "validate with data engineers" beat answers that accept stakeholder claims at face value.
6. Watch for feasibility scenarios. If technical experts reveal the data is insufficient or inaccessible, the right response is to reassess. That means iterating back, re-scoping or finding alternative data, not proceeding anyway.
7. Remember governance and security. Technical experts help identify where sensitive data sits and the controls on it. Pick answers that respect compliance requirements.
8. Documentation matters. Answers that involve creating a data inventory, data map or data dictionary with technical input reflect best practice.
9. Eliminate extreme options. Options that ignore stakeholders, cancel the project immediately without analysis, or skip discovery are usually distractors.
10. Think iteratively. CPMAI is cyclical and agile. Correct answers often involve refining requirements based on technical findings.
Sample Question
During Data Understanding, the business sponsor insists that five years of customer transaction history is readily available for a churn prediction model. What should the project manager do FIRST?
A) Begin feature engineering using the assumed dataset
B) Validate availability, structure and accessibility of the data with the database administrators and data engineers
C) Select a churn prediction algorithm
D) Ask the sponsor for written confirmation
Answer: B. Technical experts must confirm the data's real availability and structure before any modeling work begins.
Summary
Technical experts on data systems and structures are essential partners in identifying data needs. They turn business assumptions into verified facts about data location, structure, quality, access and constraints. On the exam, recognize when a scenario calls for their input, prioritize early validation over assumptions, and remember that CPMAI's data-first, iterative approach depends on accurate technical knowledge of the data landscape.
In the PMI Cognitive Project Management for AI (PMI-CPMAI) methodology, the Identify Data Needs work belongs to Phase II, Data Understanding. Here the team works out what data the AI solution requires, where that data lives, and whether it can be used. A key activity in this work is engaging technical experts on data systems and structures. These are the people who actually know how the organization's data is stored, structured, moved, secured and maintained.
CPMAI is data-centric. It assumes that most AI project failures come from data problems, not algorithm problems. For that reason, the exam expects you to know who to consult about data and when. Technical experts are the authoritative source on the technical reality of the data landscape.
What It Is
Technical experts on data systems and structures are stakeholders with deep, hands-on knowledge of the organization's data infrastructure. Typical roles include:
• Database administrators (DBAs): know schemas, tables, indexes, performance limits, backup and access controls.
• Data engineers: build and maintain pipelines, ETL/ELT processes, data lakes and warehouses.
• Data architects: design enterprise data models, integration patterns and data flows between systems.
• System and application owners / IT administrators: understand source systems such as ERP, CRM, IoT platforms and legacy applications.
• Cloud and infrastructure engineers: understand storage, compute, networking and scalability.
• Data stewards and data governance specialists (with a technical focus): know metadata, lineage, data dictionaries and access policies.
• Security and integration specialists: understand APIs, encryption, permissions and cross-system connectivity.
They differ from subject matter experts (SMEs) / domain experts, who know what the data means in a business context. Technical experts know where the data is, how it is structured, and how it can be accessed and moved. CPMAI expects both groups to be engaged, because each covers what the other cannot.
Why It Is Important
1. Prevents false assumptions about data availability. Business stakeholders often believe data exists and is easy to use. Technical experts can confirm whether it is truly captured, how far back history goes, and in what format.
2. Reveals data quality and structural issues early. Examples include inconsistent schemas, missing fields, duplicate records, siloed systems, unstructured formats and incompatible identifiers across systems.
3. Clarifies access and feasibility. Experts know about permissions, licensing limits, API rate limits, legacy-system constraints and the effort needed to extract data.
4. Supports the go/no-go decision. CPMAI stresses iterating back to earlier phases if data is not adequate. Technical input is essential to judging whether the project is feasible as scoped.
5. Informs data preparation effort and cost estimates. Data preparation usually takes most of an AI project's effort. Experts help estimate integration, cleansing and transformation work.
6. Identifies governance, privacy and security constraints. Experts know where sensitive data (PII, PHI, financial data) resides and what controls apply.
7. Enables operationalization planning. Even this early, knowing the production data flow helps confirm the model can be fed data reliably once deployed.
How It Works
Step 1: Identify the right experts. Using the business problem and AI pattern chosen in Phase I (Business Understanding), find which systems likely hold the relevant data. Then identify their technical owners.
Step 2: Conduct structured discovery sessions. Typical questions include:
• Where does this data reside (systems, databases, files, cloud stores, third parties)?
• What is the structure (structured, semi-structured, unstructured; schemas; data types)?
• What is the volume, velocity and history available?
• How frequently is the data updated, and what is its latency?
• What are the known quality issues, gaps or inconsistencies?
• How is the data linked across systems (keys, identifiers)?
• What access methods exist (APIs, exports, queries, streaming)?
• What security, privacy, retention and compliance restrictions apply?
• Is there documentation (data dictionaries, metadata, lineage diagrams)?
Step 3: Map data needs to data sources. Build an inventory or data map that links each required data element to its source, owner, format and accessibility. Note any gaps.
Step 4: Assess feasibility and gaps. Together with the experts, determine whether the data is sufficient in quantity, quality and relevance. If it is not, consider options: acquiring third-party data, collecting new data, labeling, synthetic data, or re-scoping the problem.
Step 5: Document and communicate findings. Feed findings into the data understanding deliverables, risk register and stakeholder communications. Raise any blockers to the project sponsor.
Step 6: Iterate. CPMAI is iterative. If technical findings show the data cannot support the intended solution, return to Business Understanding to adjust scope, rather than forcing the project forward.
Key Distinctions to Remember
• Technical experts = how and where data is stored, structured, accessed and moved.
• Domain/business SMEs = what data means, business rules and relevance to the problem.
• Data scientists = whether data is suitable for modeling and which features are useful.
• Data owners/stewards = who has authority to grant use and governance approval.
A strong answer often involves several of these groups working together, but the question's wording usually points to one primary group.
Common Pitfalls CPMAI Warns Against
• Assuming data exists or is usable because the business says so.
• Skipping technical discovery and jumping straight to model selection.
• Underestimating integration effort across siloed or legacy systems.
• Ignoring access, security or privacy restrictions until late in the project.
• Engaging technical experts only after problems appear, instead of proactively.
Exam Tips: Answering Questions on Technical Experts on Data Systems and Structures
1. Match the question to the right expert. If the scenario involves schemas, databases, pipelines, storage, system integration, data formats or technical access, the best answer is usually to consult technical data experts such as DBAs, data engineers or data architects.
2. Choose data validation before modeling. If an option says to start building or selecting a model before confirming data availability with technical experts, it is almost always wrong. CPMAI puts data first.
3. Look for proactive engagement. Prefer answers where experts are engaged early in Data Understanding, not after failures occur.
4. Do not confuse meaning with mechanics. If the issue is what a field means or a business rule, consult domain SMEs. If it is where a field lives or why it cannot be extracted, consult technical experts.
5. Favor verification over assumption. Answers like "confirm with system owners" or "validate with data engineers" beat answers that accept stakeholder claims at face value.
6. Watch for feasibility scenarios. If technical experts reveal the data is insufficient or inaccessible, the right response is to reassess. That means iterating back, re-scoping or finding alternative data, not proceeding anyway.
7. Remember governance and security. Technical experts help identify where sensitive data sits and the controls on it. Pick answers that respect compliance requirements.
8. Documentation matters. Answers that involve creating a data inventory, data map or data dictionary with technical input reflect best practice.
9. Eliminate extreme options. Options that ignore stakeholders, cancel the project immediately without analysis, or skip discovery are usually distractors.
10. Think iteratively. CPMAI is cyclical and agile. Correct answers often involve refining requirements based on technical findings.
Sample Question
During Data Understanding, the business sponsor insists that five years of customer transaction history is readily available for a churn prediction model. What should the project manager do FIRST?
A) Begin feature engineering using the assumed dataset
B) Validate availability, structure and accessibility of the data with the database administrators and data engineers
C) Select a churn prediction algorithm
D) Ask the sponsor for written confirmation
Answer: B. Technical experts must confirm the data's real availability and structure before any modeling work begins.
Summary
Technical experts on data systems and structures are essential partners in identifying data needs. They turn business assumptions into verified facts about data location, structure, quality, access and constraints. On the exam, recognize when a scenario calls for their input, prioritize early validation over assumptions, and remember that CPMAI's data-first, iterative approach depends on accurate technical knowledge of the data landscape.
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More Technical Experts on Data Systems and Structures 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 Stewards and Data Governance TeamsData 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 TeamsTemporal and Granularity Requirements for DataTranslating Technical Data Concepts for Business AudiencesValidating Completeness and Accuracy During CollectionVisualizations and Reports for Data Insights