Overview
Canadian public sector organizations are under growing pressure to improve data quality, clarify ownership and prepare trusted data for AI, analytics and secure information sharing. But having a governance policy is not enough — it must translate into consistent practices, effective controls and measurable outcomes.
Advanced Data Governance for AI & Automation II: Implementation and Uplift helps public sector teams across Canada move from governance frameworks to everyday practice.
Explore practical approaches to strengthen data ownership, stewardship, classification, access, quality and lineage. Learn how to build sustainable governance routines that support better service delivery, responsible AI adoption and secure data sharing across departments and jurisdictions.
Connect implementation to applicable Canadian privacy legislation, information management policies, cybersecurity standards and audit expectations — supporting accountable, defensible decision-making.
Register now and gain practical tools and implementation resources to turn your data governance policy into measurable action.
Course Inclusions
Get the tools to take your data governance to the next level - implement and level up your data governance, ready for AI use.
This hands-on course will help you build fit-for-purpose frameworks and boost data confidence across your team.
Who Should Attend?
This course is designed for public sector professionals involved in data governance, data quality, information management, privacy, cyber, risk, digital transformation, and AI readiness. It is relevant for data leaders, governance and quality teams, data owners, custodians and stewards, architects, analytics and reporting leads, AI/digital transformation teams, program managers and senior leaders accountable for data-enabled services. It is also suitable for policy, strategy, technology and business teams involved in data sharing, DWH/ lakehouse initiatives, metadata, catalogue, lineage, reporting or AI-enabled decision support.
Learning Outcomes
By the end of this course, you will be able to:
Translate an existing data governance policy into a practical 90 / 180 / 365-day implementation roadmap.
Identify where governance is stalling and distinguish policy maturity from operational maturity
Clarify how data owners, custodians and stewards make decisions in practice, not just in role descriptions
Build a practical delegation pack for one data domain, including classification, asset to register entries and evidence requirements
Identify sensitive data and use classification as a structured step towards opening data access safely.
Prioritise critical data elements and select fit-for-purpose data quality measures
Define 3–5 practical KPIs covering governance adoption and data quality improvement
Understand how data warehouses, lakehouses, governed data platforms and data lineage tools support trusted AI and secure data sharing across departments and program areas.
Map an AI use case against data governance, quality, privacy, security, classification and lineage requirements to identify implementation gaps and priorities.
Leave with an internal implementation pack that can be shared with executives, data owners, and delivery teams
Online Training
Advanced Data Governance for AI & Automation II: Implementation and Uplift
Session details
- Move from governance theory to practical implementation — turning policies into clear accountabilities, repeatable processes and measurable results across your department or agency.
- Prepare your organization for AI by strengthening the data foundations it depends on — quality, ownership, classification, metadata and lineage.
- Enable responsible access to government data by identifying sensitive information and applying appropriate security classifications, access controls and safeguards.
- Connect data governance improvements to Canadian public-sector requirements — including applicable federal, provincial and territorial privacy legislation, information management policies, cybersecurity standards and audit expectations.
- Build practical tools your organization can reuse, including an implementation roadmap, roles and delegated authorities package, communications plan, performance scorecards and executive briefing.
- Strengthen interdepartmental and cross-jurisdictional data sharing by balancing service delivery needs with privacy, security, accountability and appropriate access.
- Equip data, digital, privacy, security, risk and program teams with a shared language for implementing AI-ready data governance in the Canadian public sector.
Familiarity with the topic is required
Registration for this training opens soon. To register your interest or learn more, email [email protected]
Key Sessions
Each module combines practical instruction with interactive activities that turn new knowledge into action. Learners will apply key concepts to realistic public sector scenarios, test approaches with peers and consider how each lesson relates to their organization.
The activities are designed to build confidence and reinforce learning throughout the course. Participants will leave with practical tools, clear next steps and approaches they can use immediately to strengthen data governance.
- Why most policies stall: the implementation gap
- Framing the last mile: data quality, ISO 27001 readiness and governance uplift as the foundation for AI
- Mapping your current state: policy maturity vs. operational maturity
- Building your implementation roadmap from an existing policy
- ISO 27001 uplift as a forcing function — what auditors and the business will look for
Practical Application — Policy Audit and Implementation Roadmap
- Audit your own policy — what is actionable, what is aspirational, what is missing — and begin the implementation of roadmap.
- From nomination to action: data owners, custodians and stewards in practice
- Information ownership, classification and asset registers through an ISO 27001 lens
- Identification of sensitive data and classification as a step towards opening data access
- Building delegation packs custodians can hand to stewards
- RACI for governance decisions: classification, access, quality, retention, sharing and escalation
- Embedding ownership into BAU — not as a side-of-desk task
Practical Application — Delegation Pack and Access Readiness
- Draft a delegation pack for one data domain, including sensitive data, classification and asset to register entries.
- Working with, not around, change managers
- Aligning data governance to organizational strategy, ISO 27001 milestones, and the AI agenda
- Building a communications plan: audiences, messages, channels, cadence
- Creating culture: making governance roles desirable, not punitive
- Benefits articulation — what is in it for owners, teams, executives and delivery groups
Practical Application
- Exercise: Build a one-page communications plan for your governance rollout
- What good looks like: leading vs. lagging indicators
- Data quality dimensions and measurement: accuracy, completeness, timeliness, consistency, validity, uniqueness
- Identifying critical data elements (CDEs) — where to focus DQ effort
- Building data quality scorecards
- Root cause analysis for DQ issues — fixing causes, not symptoms
- Data quality improvement KPIs
- Governance adoption KPIs and behavioral change indicators
- Comms plan metrics — how to measure whether the message landed
- Reporting cadence and dashboards for SLT visibility
- Closing the loop: feeding metrics back into the roadmap
Practical Application
- Exercise: Define 3–5 KPIs, including at least one DQ measure, and a measurement plan for your program
- AI readiness as the outcome of strong governance, DQ and ISO 27001 foundations
- Why AI breaks first where governance is weakest
- Metadata management and quality at scale
- Data centralization through DWH, lakehouse or governed platform approaches — what needs to be decided before scaling AI
- Lineage-defining solutions and why lineage matters for trust, auditability, access decisions and AI use
- Cross-domain sharing without chaos: risk, privacy and security in balance
- Single source of truth debates: Fabric vs ERP/SAP, DWH, lakehouse or domain platforms
- AI literacy and shadow IT — what data owners need to know
- AI Governance – A short brief
- Data Governance and AI Governance – A united governance framework
Practical Application — AI Use Case Readiness Check
- Identify one AI use case in your organization and map the governance, DQ, sensitive data, classification, lineage and ISO prerequisites.
Participants leave with a tailored, ready-to-share pack containing:
- Implementation roadmap: 90 / 180 / 365-day view
- Delegation pack template: custodian → steward, aligned to ISO 27001 classification, sensitive data and asset register requirements
- Communications plan with success criteria
- Data quality scorecard template
- KPI scorecard and reporting template: governance adoption + DQ improvement
- Data access opening checklist: sensitive data, classification, access conditions, lineage and approvals
- Executive one-pager to socialize internally
- Capstone: Present your pack to peers for feedback
Meet Your Facilitator
Nigel Schmalkuche
Managing Director and Principal Consultant | Strategic Architects
Nigel is a leader in strategy, AI, data, cloud, enterprise and business architecture and has guided government across Australia through digital business transformation. He is a results-orientated business and IT professional with 30 years’ experience in the fields of energy, education, elections, finance, health care, housing, legal, mining, manufacturing, news and media, pharmaceutical, policing, racing, transport and utilities.
Nigel has held various professional and leadership roles in IT and Enterprise Architecture in Queensland State and Local Government. He is one of our leading trainers and facilitates training courses on Enterprise, Business and Data Architecture, AI and RPA for Canadian, Australian, New Zealand and American Government Professionals
Nigel has written two books, AI and Data Strategy and Data to Insight that cover a wide variety of Data Architecture topics including Data Governance.
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