Public Sector Data Strategy and Information Management Training Course

Course Introduction

The Public Sector Data Strategy and Information Management Training Course equips government professionals with advanced capabilities for developing, implementing, and governing data strategies that align information assets with institutional priorities. The programme examines how public institutions can transform data from a fragmented administrative resource into a strategic asset supporting policy development, planning, service delivery, performance management, accountability, innovation, and evidence-based decision-making.

Public institutions increasingly manage large and diverse information resources generated through administrative systems, financial operations, human resources, procurement, programmes, regulatory activities, digital services, surveys, and citizen interactions. Without a coherent data strategy, these resources can become fragmented across departments, poorly governed, difficult to access, inconsistent in quality, and expensive to maintain. This course provides a structured approach to developing data strategies that connect institutional objectives, information requirements, technology, governance, people, processes, and measurable outcomes.

Participants will examine the foundations of strategic data management, including data maturity assessment, institutional data inventories, information architecture, governance models, data ownership, stewardship, quality management, metadata, interoperability, master data, and information lifecycle management. They will learn how to establish priorities, define strategic data objectives, identify capability gaps, develop implementation roadmaps, and create governance arrangements that sustain data value over time.

The programme also addresses practical information-management requirements across the public sector. Participants will explore data classification, information security, privacy, access management, records, retention, sharing, integration, reporting, analytics, and institutional knowledge management. Particular attention is given to developing coherent policies and operating models that enable departments and agencies to share and reuse information while maintaining appropriate controls over sensitive and confidential data.

Emerging technologies are integrated throughout the programme, including cloud data platforms, data lakes, data fabrics, APIs, artificial intelligence, machine learning, automation, knowledge graphs, real-time analytics, digital public infrastructure, and intelligent information systems. Participants will evaluate how these technologies influence public-sector data strategies while considering emerging issues such as data sovereignty, cybersecurity, privacy, vendor dependency, interoperability, algorithmic bias, responsible AI, and the changing skills required for modern information management.

By the end of the course, participants will be able to assess data maturity, formulate strategic information priorities, strengthen data governance, improve information quality, coordinate data assets, modernize information environments, and develop practical implementation roadmaps. The programme supports public institutions in building sustainable data capabilities that improve organizational performance, reduce information fragmentation, strengthen accountability, and enable more responsive and intelligent government.

Duration

10 days

Who Should Attend

• Permanent secretaries, directors, departmental heads, and senior public administrators responsible for institutional strategy, information, data, or digital transformation.

• Chief information officers, ICT directors, enterprise architects, IT managers, and government digital-transformation leaders.

• Chief data officers, data managers, data stewards, information-governance officers, and data-quality specialists.

• Policy analysts, planning officers, economists, statisticians, researchers, and management-information professionals using data for strategic planning.

• Monitoring and evaluation officers responsible for performance information, results frameworks, indicators, programme data, and institutional reporting.

• Records managers, information-management professionals, knowledge managers, archivists, and information-policy specialists.

• Finance, procurement, human-resource, programme, operations, and service-delivery managers responsible for significant institutional data assets.

• Data analysts, business-intelligence specialists, database administrators, systems analysts, and data-engineering professionals.

• Risk, compliance, internal audit, privacy, cybersecurity, and governance professionals involved in information controls and institutional assurance.

• Consultants, development partners, advisers, and technical specialists supporting public-sector data strategy, governance, information management, and digital modernization.

Course Objectives

• Develop advanced capabilities for formulating public-sector data strategies aligned with institutional mandates, policy priorities, operational requirements, and measurable outcomes.

• Assess organizational data maturity across governance, people, processes, technology, quality, culture, architecture, security, analytics, and information-management capabilities.

• Establish data-governance frameworks that clearly define ownership, stewardship, accountability, access rights, quality responsibilities, and institutional decision-making authority.

• Develop strategic approaches for managing government information throughout its lifecycle, including creation, acquisition, storage, use, sharing, retention, archiving, and disposal.

• Strengthen information architecture and interoperability by aligning data standards, metadata, systems, integration mechanisms, business processes, and institutional information requirements.

• Improve data quality through systematic controls covering accuracy, completeness, consistency, validity, timeliness, uniqueness, relevance, integrity, and fitness for purpose.

• Develop practical data-management policies covering classification, metadata, master data, reference data, information sharing, retention, privacy, security, and responsible data use.

• Strengthen the strategic use of analytics, business intelligence, dashboards, forecasting, and decision-support capabilities within public-sector information environments.

• Apply emerging technologies including cloud platforms, data fabrics, artificial intelligence, machine learning, automation, and knowledge graphs within appropriate strategic and governance frameworks.

• Establish investment priorities and implementation roadmaps that balance strategic value, institutional capacity, technology requirements, risk, cost, sustainability, and expected public-sector benefits.

• Strengthen information security, privacy, data sovereignty, cybersecurity resilience, ethical information use, and responsible AI governance within public-sector data strategies.

• Develop sustainable data cultures, workforce capabilities, performance measures, and continuous-improvement mechanisms that enable institutions to realize long-term value from information assets.

Comprehensive Course Outline

Module 1: Foundations of Public-Sector Data Strategy

• Examine the strategic role of data in public administration, policymaking, planning, service delivery, institutional performance, transparency, and accountability.

• Distinguish data strategy, data governance, information management, data management, digital transformation, analytics, and knowledge management.

• Identify common strategic challenges involving fragmented data assets, duplicated systems, inconsistent standards, weak governance, poor quality, and limited institutional capability.

• Explore emerging developments involving data-driven government, intelligent administration, digital public infrastructure, real-time information, and AI-enabled public-sector strategy.

Module 2: Data Maturity and Institutional Capability Assessment

• Assess institutional maturity across data governance, information architecture, data quality, technology, workforce skills, culture, security, analytics, and strategic leadership.

• Develop maturity models and assessment criteria that identify current capabilities, critical gaps, dependencies, risks, and priority areas for improvement.

• Use assessment findings to establish realistic strategic priorities based on institutional mandates, operational requirements, resource constraints, and expected public value.

• Explore emerging approaches involving automated maturity assessment, data observability, capability benchmarking, AI-assisted gap analysis, and continuous data-capability monitoring.

Module 3: Data Strategy Development and Strategic Alignment

• Translate government mandates, strategic plans, policy priorities, programme objectives, and operational requirements into clear data-strategy priorities and outcomes.

• Develop data-strategy components covering vision, principles, objectives, priority datasets, governance, architecture, capabilities, investments, implementation, and performance measures.

• Establish alignment between data strategy, digital transformation, enterprise architecture, information systems, service delivery, performance management, and institutional reform.

• Explore emerging strategy approaches incorporating AI readiness, real-time data, predictive analytics, digital public infrastructure, and data-driven policy environments.

Module 4: Data Governance and Institutional Operating Models

• Establish governance structures that define data ownership, stewardship, custodianship, accountability, access, quality, security, and decision rights.

• Design operating models that coordinate central data functions with ministries, departments, agencies, programmes, business units, and ICT teams.

• Develop governance policies covering data standards, sharing, privacy, retention, classification, quality, metadata, master data, and responsible use.

• Explore emerging federated governance, data-product operating models, data domains, data mesh concepts, AI governance, and cross-government data councils.

Module 5: Information Architecture and Data Ecosystems

• Examine enterprise information architecture and its relationship with business processes, applications, databases, analytical platforms, reporting systems, and digital services.

• Map critical information flows to identify sources, transformations, integrations, consumers, reporting requirements, dependencies, and opportunities for consolidation.

• Align information architecture with strategic priorities, interoperability requirements, data governance, security controls, analytical needs, and institutional capabilities.

• Explore emerging architectures involving data lakes, lakehouses, data fabrics, semantic layers, knowledge graphs, event streaming, and cloud-native information environments.

Module 6: Data Lifecycle and Information Asset Management

• Manage data across creation, collection, acquisition, validation, storage, processing, use, sharing, archiving, retention, and responsible disposal stages.

• Develop lifecycle policies that define responsibilities, controls, retention periods, access requirements, quality expectations, and information-management procedures.

• Establish data inventories and information-asset registers that identify strategic datasets, owners, users, systems, classifications, quality status, and business value.

• Explore automated lifecycle management, intelligent retention, information discovery, machine-readable policies, and AI-assisted information-asset classification.

Module 7: Data Quality, Standards and Metadata

• Establish institutional data-quality frameworks covering accuracy, completeness, consistency, validity, timeliness, uniqueness, relevance, integrity, and coherence.

• Develop common data definitions, metadata standards, data dictionaries, business glossaries, classifications, reference values, and naming conventions.

• Establish data-quality indicators, validation procedures, exception management, reconciliation processes, and continuous-improvement mechanisms.

• Explore automated quality monitoring, anomaly detection, semantic metadata, knowledge graphs, AI-assisted data classification, and intelligent data-quality management.

Module 8: Master Data, Reference Data and Interoperability

• Identify critical government master-data domains such as citizens, employees, suppliers, organizations, locations, programmes, assets, and financial entities.

• Establish authoritative sources, common identifiers, validation procedures, ownership responsibilities, and controlled distribution of shared reference information.

• Strengthen interoperability through APIs, common standards, integration platforms, data exchange mechanisms, semantic models, and coordinated information architectures.

• Explore emerging entity resolution, linked data, knowledge graphs, data spaces, federated information environments, and intelligent interoperability technologies.

Module 9: Data Security, Privacy and Sovereignty

• Establish strategic controls for authentication, authorization, encryption, privileged access, monitoring, auditing, secure sharing, backup, and information protection.

• Develop privacy and confidentiality frameworks covering sensitive citizen, employee, financial, procurement, programme, regulatory, and operational information.

• Assess data-sovereignty considerations involving cloud services, international data transfers, technology suppliers, hosting arrangements, and cross-border information flows.

• Address emerging threats involving ransomware, insider risks, supply-chain compromise, AI-enabled cyberattacks, re-identification, data leakage, and unauthorized information linkage.

Module 10: Analytics, Business Intelligence and Information Value

• Develop strategic approaches for using analytics, dashboards, performance information, business intelligence, forecasting, and decision-support systems.

• Identify priority analytical use cases where better information can improve resource allocation, programme implementation, service delivery, risk management, and institutional performance.

• Establish governance mechanisms for analytical models, indicators, dashboards, reporting products, data interpretation, and evidence-based decision-making.

• Explore predictive analytics, augmented intelligence, natural-language querying, automated insights, AI-assisted analysis, and intelligent executive information systems.

Module 11: Data Sharing, Collaboration and Information Partnerships

• Develop frameworks for sharing information across ministries, departments, agencies, public institutions, and authorized external stakeholders.

• Define information-sharing purposes, responsibilities, access conditions, security requirements, privacy safeguards, service levels, and accountability mechanisms.

• Establish institutional collaboration mechanisms that enable data reuse while preventing uncontrolled duplication, unauthorized access, inconsistent interpretations, and unmanaged information risks.

• Explore secure data spaces, privacy-enhancing technologies, federated analytics, controlled data exchange, interoperable platforms, and cross-government information ecosystems.

Module 12: Emerging Technologies and Strategic Data Innovation

• Assess the strategic implications of cloud computing, data platforms, APIs, automation, Internet of Things, edge computing, and real-time information environments.

• Examine artificial intelligence, machine learning, generative AI, natural-language processing, intelligent automation, and AI agents as emerging data-management capabilities.

• Evaluate emerging technology investments according to strategic value, interoperability, security, cost, sustainability, workforce capability, data sovereignty, and institutional readiness.

• Explore future developments involving autonomous data management, intelligent information ecosystems, digital twins, synthetic data, advanced knowledge graphs, and AI-native government platforms.

Module 13: Artificial Intelligence, Data Strategy and Responsible Innovation

• Develop strategic approaches for incorporating artificial intelligence into government data environments while maintaining governance, transparency, accountability, and human oversight.

• Assess data requirements for AI adoption, including quality, provenance, representativeness, security, accessibility, documentation, and lawful or authorized use.

• Establish controls for AI model validation, monitoring, explainability, bias assessment, human review, incident management, and responsible deployment.

• Address emerging issues involving generative AI, hallucinations, model drift, algorithmic discrimination, automated decision-making, synthetic data, AI sovereignty, and public trust.

Module 14: Data Strategy Implementation, Investment and Change

• Translate strategic data priorities into implementation programmes with defined activities, responsibilities, timelines, resources, dependencies, milestones, and measurable outcomes.

• Develop investment frameworks that prioritize data platforms, governance, integration, quality, security, analytics, workforce development, and high-value information initiatives.

• Apply change-management approaches that build executive sponsorship, staff participation, data literacy, cross-functional collaboration, and sustained institutional adoption.

• Explore emerging delivery models involving agile government, product management, DevSecOps, continuous modernization, platform strategies, and adaptive data transformation.

Module 15: Data Strategy Performance and Continuous Improvement

• Establish strategic performance indicators for data quality, governance effectiveness, information accessibility, interoperability, analytics adoption, reporting efficiency, security, and business value.

• Develop data-strategy review processes that assess implementation progress, benefits realization, capability development, risks, emerging needs, and changing technology conditions.

• Conduct audits and maturity reassessments to identify persistent gaps, emerging priorities, governance weaknesses, and opportunities for continuous improvement.

• Explore automated strategy monitoring, data-capability observability, predictive governance, AI-assisted performance assessment, and continuously adaptive data strategies.

Module 16: Strategic Public-Sector Data Transformation

• Integrate governance, architecture, lifecycle management, quality, interoperability, security, analytics, AI, workforce capability, investment, and organizational change into a unified data strategy.

• Develop institution-wide transformation roadmaps that prioritize high-value datasets, critical information systems, strategic use cases, governance reforms, and measurable public outcomes.

• Establish long-term capability-building approaches covering data leadership, professional skills, data literacy, analytical culture, institutional collaboration, and continuous innovation.

• Prepare public institutions for emerging data-driven environments featuring real-time information, predictive government, intelligent automation, interoperable platforms, AI-enabled administration, and strategic data ecosystems.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808

Training Venue 

The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.

Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Upskill certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808

Terms of Payment:

Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.