Overview
Data governance maturity is not proven by the policy you have. It is proven by the behaviours, controls and data quality you can sustain.
Across Australia and New Zealand, public sector organisations are moving beyond foundational data governance and are now under pressure to make governance operational: improving data quality, clarifying ownership, classifying sensitive data, safely opening access, strengthening lineage and preparing trusted data for AI, analytics, ISO 27001 readiness and cross-domain sharing.
The opportunity is significant. CSIRO’s Data61 has estimated that digital technologies, including AI, could contribute around AUD $315 billion to Australia’s economy by 2030, while New Zealand’s AI Strategy notes that adopting generative AI alone could add NZD $76 billion to the New Zealand economy by 2038 — more than 15% of GDP.
But these benefits depend on trusted, usable, and well-governed data. In government, poor data quality, unclear ownership, weak metadata, inconsistent classification and limited lineage can quickly create operational, privacy, cyber, audit and public trust risks. These issues do not just slow down AI and analytics projects.They affect service delivery, reporting, compliance, information sharing, and defensible decision-making.
Most organisations already have a data governance policy or framework. The harder task is turning that policy into day-to-day behaviour: clear ownership, practical stewardship, consistent classification, controlled access, measurable data quality, reliable reporting and governance routines that teams follow.
This course focuses on that “last mile” of data governance — the practical, human and change-heavy work of making governance sticks. Designed as the next action-based step after foundational data governance training, Advanced Data Governance II: Implementation and Uplift helps participants move from policy to practice and build the implementation artefacts needed for AI-ready, ISO 27001-aligned and shareable data governance.
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 DWH, lakehouse, governed platform and lineage solutions support trusted AI and cross-domain sharing
Map one AI use case against governance, data quality, privacy, security, ISO 27001, classification and lineage prerequisites
Leave with an internal implementation pack that can be shared with executives, data owners, and delivery teams
Online Training
Advanced Data Governance II: Implementation and Uplift
Session details
- Move from governance theory to practical implementation — the part where most policies stall
- Prepare your organisation for AI by strengthening the data foundations AI depends on — quality, ownership, classification, metadata and lineage
- Reduce the risk of opening data access too quickly by identifying sensitive data and applying clear classification and access controls
- Connect data governance uplift to ISO 27001 readiness, cyber security, privacy and audit expectations
- Build practical artefacts your organisation can reuse, including a roadmap, delegation pack, comms plan, scorecards and executive one-pager
- Improve confidence in cross-domain sharing by balancing access, risk, privacy, security, and operational needs
- Equip data, digital, risk, governance and business teams with a shared language for AI-ready data governance
Familiarity with the topic is required
Key Sessions
Module 1 — From Policy to Action Plan
- 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 organisational 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
- 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 behavioural 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
- 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 centralisation 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 organisation 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 socialise 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 ICT 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 ICT 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 Australian, New Zealand, USA and Canada 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.
Register Today
Join this training for professionals working within the Public Sector
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Ends 4 Sep
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per person + tax $200 savingRegular
Ends 16 Nov
$A 1195
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