Data Governance & AI Readiness for Business Professionals
AI initiatives often expose data problems that were previously tolerated: unclear ownership, competing definitions, weak quality evidence, missing lineage, uncontrolled access, uncertain rights and no accountable route for resolving exceptions. Buying another tool does not solve these operating problems. Organizations need a practical system that connects business decisions, data responsibilities, evidence and change.
This applied online course teaches business professionals how to build that system. Each lesson follows Alder & Row Group, a fictional organization, through a different data-governance decision and produces one reusable professional artifact. A separate capstone integrates the twenty lesson artifacts into a Data Governance & AI Readiness Operating System that can be adapted to a sanitized workplace context.
Who this course is for
The course is designed for professionals who own, coordinate, consume or support governed data, including:
- aspiring and current data-governance managers, analysts and coordinators;
- business data owners and data stewards;
- analytics, reporting and business-intelligence professionals;
- operations, product, finance, risk and compliance teams that depend on trusted data;
- data-quality, metadata, master-data and information-management specialists;
- technology and transformation professionals preparing data for AI-supported work; and
- managers who need to turn fragmented data initiatives into an accountable operating model.
No programming background is required. Learners should be comfortable working with business processes, evidence, roles, decisions and professional documentation. The course does not provide legal advice, technical certification or approval for a real AI system.
What you will be able to do
By the end of the course, you will be able to:
- frame a data-governance and AI-readiness mandate with explicit scope and boundaries;
- map data domains, products, decisions and accountable owners;
- assign business-data decision rights and stewardship services;
- govern definitions, metadata, quality rules and issue evidence;
- map lineage, provenance and transformation dependencies;
- prepare evidence for access, retention, sharing and third-party data decisions;
- assess data readiness for a defined AI-supported use without claiming to approve the AI system;
- specify reproducible data supply for development, evaluation and business acceptance;
- govern master, reference and unstructured knowledge assets; and
- deliver a controlled data-readiness dossier to AI governance and delivery teams.
Applied learning: build a data-governance operating portfolio
Every core lesson combines a realistic Alder & Row Group case scene with six distinct theory topics, a blank artifact template, the same template completed for the case, three copyable AI prompts and a self-assessment rubric. Alder & Row Group is fictional; no real customer, employee, supplier or commercially restricted data is used.
Across four modules, you will create twenty distinct professional artifacts:
- Data Governance & AI Readiness Charter;
- Data Domain & Product Landscape Map;
- Data Decision Rights & Stewardship Map;
- Critical Data & AI Dependency Register;
- Data Policy, Standard & Exception Architecture;
- Business Glossary & Definition Decision Log;
- Metadata Intake & Curation Workflow;
- Data Quality Rule Catalogue;
- Data Quality Baseline & Issue Register;
- Data Lineage & Provenance Map;
- Data Handling Constraint Register;
- Purpose-Based Access Evidence Pack;
- Retention Decision Evidence Pack;
- Data-Sharing Evidence Pack;
- Third-Party Data Evidence Register;
- AI-Use Data Readiness Assessment & Handoff;
- AI Data Supply & Evaluation Brief;
- Golden Record Reconciliation Design;
- Knowledge Corpus Curation Plan; and
- Data-to-AI Governance Handoff Dossier.
The separate applied capstone reconciles these artifacts into one coherent Data Governance & AI Readiness Operating System. The result shows how a business can connect scope, ownership, meaning, quality, provenance, constraints, data supply, handoffs and improvement without confusing data governance with AI-system approval.
Curriculum
Module 1 — Governance Foundations, Scope and Accountability
- Frame the data governance and AI-readiness mandate — create a Data Governance & AI Readiness Charter.
- Map data domains, products and decision dependencies — create a Data Domain & Product Landscape Map.
- Assign business-data decision rights and stewardship — create a Data Decision Rights & Stewardship Map.
- Identify critical data and AI dependencies — create a Critical Data & AI Dependency Register.
- Build the policy, standard and exception architecture — create a Data Policy, Standard & Exception Architecture.
Module 2 — Meaning, Metadata, Quality and Traceability
- Create a governed business glossary — create a Business Glossary & Definition Decision Log.
- Operate metadata intake, curation and discovery — create a Metadata Intake & Curation Workflow.
- Define data-quality requirements and executable rules — create a Data Quality Rule Catalogue.
- Profile data, baseline quality and triage issues — create a Data Quality Baseline & Issue Register.
- Map lineage, provenance and transformation evidence — create a Data Lineage & Provenance Map.
Module 3 — Constraints, Sharing and External Data Evidence
- Record classification and handling requirements — create a Data Handling Constraint Register.
- Prepare purpose-based access review evidence — create a Purpose-Based Access Evidence Pack.
- Coordinate retention, archival and deletion decisions — create a Retention Decision Evidence Pack.
- Package an accountable data-sharing handoff — create a Data-Sharing Evidence Pack.
- Qualify third-party data evidence — create a Third-Party Data Evidence Register.
Module 4 — AI-Ready Data Supply and Accountable Handoff
- Assess dataset readiness for a defined AI-supported use — create an AI-Use Data Readiness Assessment & Handoff.
- Specify data supply for AI development and evaluation — create an AI Data Supply & Evaluation Brief.
- Reconcile master and reference data across domains — create a Golden Record Reconciliation Design.
- Prepare unstructured knowledge for retrieval-based AI — create a Knowledge Corpus Curation Plan.
- Hand data-readiness evidence to AI governance and delivery — create a Data-to-AI Governance Handoff Dossier.
Separate Applied Capstone
Build the Data Governance & AI Readiness Operating System — reconcile the twenty lesson artifacts into an integrated governance service model, evidence system, AI-readiness handoff and improvement rhythm.
How the course works
The course is online and self-paced. It can be completed within one month, depending on your pace and the depth with which you complete the practical assignments. A useful rhythm is one module per week followed by the capstone, although learners can adapt the schedule to their availability.
The lessons follow Alder & Row Group as it replaces disconnected cleanup projects and undocumented knowledge with owned decisions and governed evidence. Every case scene covers a different problem: mandate, domain boundaries, stewardship, critical data, policy, definitions, metadata, quality, lineage, handling constraints, access evidence, retention, sharing, suppliers, AI-use readiness, data supply, master data, knowledge curation or downstream handoff.
You may use the supplied case or a sanitized version of your organization. Never upload credentials, personal data, privileged communications, restricted system information, confidential contracts or other material you are not authorized to share. Legal, privacy, security, records, risk and technical decisions remain with appropriately authorized specialist owners.
AI-supported practice and self-assessment
AI is used as a structured thinking and feedback tool, not as source evidence, legal authority, technical validation or organizational approval. Each lesson provides three complete prompts grounded in the Alder & Row Group case. Highlighted context blocks show what a learner can replace with sanitized information from an approved work setting.
Each lesson also provides a rubric and a self-assessment prompt. The learner can submit a sanitized artifact and the rubric to an approved AI system to identify missing fields, contradictions, weak ownership, evidence gaps and unclear handoffs. The learner remains responsible for confidentiality, source verification, specialist review, decisions and approvals.
Certificate
After completing the learning activities, you can access the course certificate and your MTF Student ID from the final course section. The certificate uses the course title Data Governance & AI Readiness for Business Professionals.
Evidence behind the course
The course design is connected to current labor-market evidence and operating-practice trends:
- The Operating Shape of Data Governance and AI Readiness: Evidence from 105 Vacancies in 2026 examines demand across ownership, stewardship, quality, metadata, lineage, controls, remediation and AI-readiness work.
- From Data Cleanup to AI Readiness: Eight Business Operating Practices explains why governed meaning, ownership, quality evidence, provenance, constraints and accountable handoffs matter for sustainable AI-enabled operations.
Start the course
Build a data-governance operating system that connects business decisions, ownership, evidence and responsible AI readiness.