Training course
Overview
Strategic
Cloud Data Analytics is a professional executive and leadership training course
designed to help organizations develop a coherent strategy for using
cloud-based data and analytics to improve business performance, accelerate
digital transformation, strengthen decision-making, and create sustainable
competitive advantage. The course provides a strategic understanding of cloud
data platforms, analytical architectures, data governance, business
intelligence, advanced analytics, artificial intelligence, financial
management, risk, and organizational capability. Participants will learn how to
connect cloud analytics investments to enterprise objectives while establishing
the governance, operating models, capabilities, and performance measures
required to achieve measurable business outcomes.
The
course examines strategic approaches to cloud data analytics across
environments such as AWS, Microsoft Azure, and Google Cloud, including cloud
data warehouses, data lakes, lakehouses, analytical platforms, data
integration, real-time analytics, semantic layers, data products, and
self-service business intelligence. Rather than focusing primarily on technical
implementation, the training emphasizes enterprise architecture, portfolio
management, strategic prioritization, platform evaluation, transformation
planning, and long-term value realization. Practical frameworks, strategic
exercises, case studies, maturity assessments, and real-world scenarios enable
participants to evaluate current capabilities and define appropriate
target-state analytics strategies.
Strong
emphasis is placed on enterprise data governance, data quality, security,
privacy, compliance, resilience, financial management, and responsible
analytics. Participants explore established practices and frameworks including
DAMA-DMBOK principles, COBIT governance concepts, ISO/IEC 27001 information
security principles, ISO/IEC 38500 governance of IT, NIST Cybersecurity
Framework concepts, FinOps practices, DataOps principles, and responsible AI
governance approaches. The course also addresses cloud economics, total cost of
ownership, vendor and sourcing strategy, service-level objectives, risk
management, business continuity, data lineage, metadata, operating models, and
executive performance management.
By
the end of this 5-day Strategic Cloud Data Analytics training course,
participants will be able to assess enterprise analytics maturity, define a
strategic cloud analytics vision, evaluate architecture and platform options,
establish governance and operating models, manage investment and cloud
economics, strengthen security and resilience, and develop an actionable
transformation roadmap. Participants will apply these capabilities through
strategic workshops, investment exercises, governance simulations, architecture
assessments, maturity analysis, and an integrated capstone in which they
develop a comprehensive cloud data analytics strategy aligned with
organizational priorities, capabilities, risks, and long-term transformation
objectives.
Course
Duration
5
Days (40 Hours)
Target
Participants
This
course is suitable for:
•
Chief Executive Officers, Managing Directors, and General Managers
•
Chief Information Officers, Chief Technology Officers, and Chief Data Officers
•
Chief Digital Officers and enterprise transformation leaders
•
Chief Financial Officers and senior finance leaders involved in technology
investment
•
Directors and senior managers responsible for data, analytics, business
intelligence, or digital transformation
•
Enterprise architects and senior technology strategists
•
Data and analytics leaders responsible for enterprise platforms and operating
models
•
Risk, cybersecurity, privacy, governance, and compliance leaders
•
Program and portfolio leaders responsible for cloud modernization initiatives
•
Senior professionals responsible for developing or executing enterprise cloud
data and analytics strategies
Course
Objectives
By
the end of the training, participants will be able to:
•
Develop a strategic vision for cloud data analytics aligned with enterprise
objectives
•
Assess organizational data and analytics maturity across people, processes,
technology, governance, and data
•
Evaluate cloud data warehouse, data lake, lakehouse, and modern analytics
architectures
•
Align analytics investments with strategic priorities, measurable business
outcomes, and value realization
•
Establish enterprise data governance, ownership, stewardship, quality,
metadata, and lineage practices
•
Strengthen cloud analytics security, privacy, compliance, resilience, and risk
management
•
Evaluate cloud financial models, total cost of ownership, FinOps practices, and
investment options
•
Develop cloud platform, vendor, sourcing, and technology evaluation strategies
•
Design effective data and analytics operating models, accountability
structures, and organizational capabilities
•
Establish performance indicators, service-level objectives, and executive
analytics governance mechanisms
•
Develop strategies for real-time analytics, advanced analytics, machine
learning, and generative AI
•
Lead cloud analytics modernization, DataOps, automation, and enterprise
transformation initiatives
•
Develop a practical enterprise cloud data analytics transformation roadmap
Course
Content
Day
1: Strategic Foundations, Enterprise Alignment, and Cloud Analytics Maturity
Module:
Strategic Cloud Data Analytics Foundations and Enterprise Strategy
Topics
- Strategic
Introduction to Cloud Data Analytics
- Evolution
from traditional reporting to cloud-native analytics
- Strategic
role of data in enterprise decision-making
- Data,
analytics, intelligence, and business value
- Cloud
analytics as a strategic organizational capability
- Executive
case study: transforming decision-making through enterprise analytics
- Enterprise
Data and Analytics Strategy
- Vision,
mission, strategic objectives, and desired outcomes
- Business
capability alignment
- Analytics
priorities and enterprise value drivers
- Strategic
principles for cloud data adoption
- Workshop:
defining an enterprise analytics strategic vision
- Cloud
Analytics Ecosystems and Strategic Platform Choices
- AWS,
Microsoft Azure, and Google Cloud ecosystems
- Cloud-native
analytics services
- Platform
capabilities and strategic considerations
- Public,
private, hybrid, and multi-cloud strategies
- Exercise:
developing a cloud platform evaluation framework
- Modern Cloud
Data Architecture
- Data
warehouses, data lakes, and lakehouses
- Cloud
storage, analytical engines, and integration platforms
- Data
pipelines and analytical processing
- Distributed
and decoupled architectures
- Architecture
assessment case study
- Enterprise
Analytics Capability Mapping
- Business,
data, technology, people, and process capabilities
- Capability
dependencies and critical enablers
- Current-state
capability mapping
- Target-state
capability requirements
- Practical
capability-mapping exercise
- Cloud Data
Analytics Maturity Assessment
- Maturity
dimensions and assessment criteria
- Governance
and data-quality maturity
- Technology
and platform maturity
- Organizational
and operating-model maturity
- Workshop:
conducting an enterprise analytics maturity assessment
- Strategic
Analytics Portfolio Management
- Analytics
initiative identification
- Strategic
prioritization
- Value, risk,
complexity, and dependency assessment
- Portfolio
balancing
- Case study:
prioritizing competing analytics investments
- Business
Value and Analytics Outcome Management
- Revenue,
efficiency, customer, risk, and innovation outcomes
- Benefit
identification and measurement
- Leading and
lagging indicators
- Value
realization governance
- Exercise:
creating an analytics value framework
- Strategic
Data Architecture Principles
- Scalability
and flexibility
- Interoperability
and portability
- Security and
privacy by design
- Governance
and quality by design
- Architecture
principles workshop
- Strategic
Current-State and Target-State Assessment
- Current-state
architecture and capability review
- Strategic gap
analysis
- Business and
technology constraints
- Target-state
priorities
- Executive
workshop: defining strategic cloud analytics priorities
Day
2: Enterprise Architecture, Governance, Quality, Security, and Control
Module:
Strategic Data Governance, Architecture, Security, and Enterprise Control
Topics
- Enterprise
Data Governance Strategy
- Governance
principles and objectives
- Executive
sponsorship and decision rights
- Data
ownership and stewardship
- Governance
councils and operating structures
- DAMA-DMBOK-aligned
governance practices
- Strategic
Data Quality Management
- Accuracy,
completeness, consistency, validity, and timeliness
- Critical data
elements
- Data quality
ownership
- Quality
measurement and remediation
- Case study:
enterprise consequences of poor-quality data
- Metadata,
Data Catalogs, and Enterprise Lineage
- Metadata
management strategy
- Business
glossaries and data catalogs
- Technical and
business lineage
- Impact
analysis and transparency
- Exercise:
developing an enterprise metadata strategy
- Strategic
Cloud Security Architecture
- Shared
responsibility models
- Identity and
access management
- Least
privilege
- Encryption
and key management
- Security
architecture review exercise
- Privacy,
Compliance, and Responsible Data Management
- Personal and
sensitive data
- Data
classification
- Retention and
access requirements
- Privacy-by-design
principles
- Scenario
exercise: managing enterprise data privacy risks
- Governance
and IT Control Frameworks
- COBIT
governance concepts
- ISO/IEC 27001
information security principles
- ISO/IEC 38500
governance principles
- NIST
Cybersecurity Framework concepts
- Executive
control-mapping exercise
- Enterprise
Data Access and Control Strategy
- Role-based
and attribute-based access concepts
- Segregation
of duties
- Privileged
access management
- Data-sharing
controls
- Case study:
designing controlled analytics access
- Cloud
Analytics Risk Management
- Strategic
technology risks
- Data,
cybersecurity, vendor, financial, and operational risks
- Risk
registers and control frameworks
- Risk
ownership and escalation
- Workshop:
developing an enterprise cloud analytics risk framework
- Resilience,
Business Continuity, and Disaster Recovery
- Availability
and resilience requirements
- Recovery time
and recovery point objectives
- Backup and
disaster recovery strategies
- Critical
analytics workload identification
- Scenario
exercise: responding to an enterprise analytics outage
- Enterprise
Governance and Control Simulation
- Governance
maturity review
- Quality and
security control assessment
- Privacy and
compliance evaluation
- Risk
prioritization and executive escalation
- Simulation:
presenting governance priorities to senior leadership
Day
3: Performance, Cloud Strategy, Financial Management, and Operational
Excellence
Module:
Strategic Cloud Economics, Performance, Risk, and Operational Management
Topics
- Strategic
Cloud Analytics Performance Management
- Availability,
scalability, responsiveness, and reliability
- Business
impact of performance
- Service-level
indicators and service-level objectives
- Executive
performance dashboards
- Performance
governance exercise
- Cloud
Capacity and Scalability Strategy
- Elasticity
and workload management
- Demand
forecasting
- Capacity
planning
- Analytical
workload optimization
- Case study:
preparing for rapid data growth
- Cloud Cost
Architecture and Financial Drivers
- Compute,
storage, networking, and data-processing costs
- Consumption-based
pricing
- Data movement
and integration costs
- Cost
allocation and accountability
- Exercise:
identifying enterprise cloud cost drivers
- FinOps
Strategy for Cloud Analytics
- FinOps
principles and operating models
- Financial
accountability
- Cost
visibility and optimization
- Budgeting and
forecasting
- Workshop:
developing a cloud analytics FinOps framework
- Total Cost of
Ownership and Investment Strategy
- Cloud versus
on-premises economics
- Migration and
modernization costs
- Licensing and
operational expenditure
- Benefits
realization
- Investment
scenario analysis
- Cloud Vendor
and Sourcing Strategy
- Vendor
evaluation criteria
- Cloud
provider capabilities
- Managed
services and outsourcing
- Vendor
concentration and lock-in
- Executive
vendor strategy exercise
- Service-Level
Agreements and Supplier Governance
- SLA and
service-level objective concepts
- Availability
and support commitments
- Incident
management requirements
- Supplier
performance governance
- Case study:
evaluating a cloud analytics supplier relationship
- Operational
Excellence and Analytics Reliability
- Monitoring
and observability
- Incident and
problem management
- Automation
and operational controls
- Reliability
engineering concepts
- Scenario
exercise: establishing analytics operational resilience
- Strategic
Analytics KPIs and Performance Reporting
- Platform,
data, delivery, cost, and business KPIs
- Data quality
indicators
- Analytics
adoption and utilization
- Value
realization metrics
- Executive
dashboard design exercise
- Cloud
Analytics Financial and Operational Strategy Simulation
- Investment
trade-offs
- Cost and
performance considerations
- Risk and
resilience requirements
- Supplier and
operating-model decisions
- Executive
simulation: evaluating a strategic analytics portfolio
Day
4: Modernization, Advanced Analytics, DataOps, Innovation, and Organizational
Capability
Module:
Strategic Cloud Analytics Transformation and Enterprise Modernization
Topics
- Cloud
Analytics Modernization Strategy
- Legacy
platform challenges
- Rehosting,
replatforming, refactoring, and replacement
- Modern
warehouse and lakehouse strategies
- Modernization
sequencing
- Case study:
developing a legacy analytics modernization strategy
- Enterprise
Cloud Migration Strategy
- Migration
readiness assessment
- Application
and data dependency mapping
- Workload
classification
- Migration
waves and transition planning
- Workshop:
developing a cloud analytics migration strategy
- Real-Time
Analytics and Event-Driven Strategy
- Batch and
streaming architectures
- Event-driven
analytics
- Real-time
operational intelligence
- Streaming use
cases
- Strategic
scenario: designing a real-time analytics capability
- Advanced
Analytics and Machine Learning Strategy
- Descriptive,
diagnostic, predictive, and prescriptive analytics
- Machine
learning business cases
- Model
lifecycle governance
- Data and
model dependencies
- Executive
case study: scaling predictive analytics
- Generative AI
and Enterprise Data Strategy
- Relationship
between generative AI and enterprise data
- Retrieval-augmented
generation concepts
- Natural-language
analytics
- AI
governance, privacy, security, and accountability
- Strategic
exercise: evaluating enterprise generative AI opportunities
- Data Products
and Self-Service Analytics Strategy
- Data product
concepts
- Product
ownership and accountability
- Semantic
layers and shared definitions
- Self-service
analytics governance
- Case study:
scaling trusted self-service analytics
- DataOps,
Automation, and Continuous Delivery
- DataOps
principles
- CI/CD for
analytics
- Automated
testing and deployment
- Workflow
orchestration
- Strategic
operating-model exercise
- Analytics
Organizational Capability and Talent Strategy
- Data and
analytics roles
- Capability
and skills assessment
- Data literacy
- Centers of
excellence
- Workshop:
developing an enterprise analytics capability plan
- Change
Management and Transformation Leadership
- Stakeholder
alignment
- Organizational
readiness
- Communication
and adoption
- Resistance
and change risks
- Executive
transformation simulation
- Enterprise
Innovation and Modernization Case Study
- Current-state
assessment
- Modernization
options
- Advanced
analytics opportunities
- Governance
and organizational implications
- Workshop:
developing a strategic modernization proposal
Day
5: Enterprise Transformation, Roadmaps, Governance, and Strategic Capstone
Module:
Enterprise Cloud Data Analytics Transformation and Strategic Leadership
Topics
- Enterprise
Cloud Data Analytics Transformation Strategy
- Strategic
vision and transformation objectives
- Business
outcomes and measurable benefits
- Transformation
principles
- Strategic
priorities and decision criteria
- Strategy
development workshop
- Target-State
Cloud Analytics Architecture
- Target-state
data platform
- Architecture
principles
- Integration
and analytical capabilities
- Governance,
security, and resilience requirements
- Architecture
assessment exercise
- Enterprise
Analytics Operating Model
- Centralized,
decentralized, and federated models
- Roles and
accountability
- Governance
structures
- Data and
analytics centers of excellence
- Operating-model
design workshop
- Strategic
Data Governance Roadmap
- Governance
priorities
- Data
ownership and stewardship
- Quality and
metadata capabilities
- Security,
privacy, and compliance controls
- Governance
roadmap exercise
- Cloud
Analytics Investment and Funding Roadmap
- Strategic
investment priorities
- Business
cases and benefits
- Funding
models
- Cost and
value tracking
- Investment
planning exercise
- Enterprise
Risk, Resilience, and Security Roadmap
- Strategic
risk priorities
- Security
improvement initiatives
- Resilience
and recovery capabilities
- Compliance
and assurance
- Scenario-based
risk roadmap development
- Analytics
Transformation Roadmap and Implementation Sequencing
- Short-,
medium-, and long-term initiatives
- Dependencies
and milestones
- Quick wins
and foundational capabilities
- Transformation
waves
- Practical
roadmap development workshop
- Executive
Performance, Governance, and Benefits Realization
- Executive
KPIs
- Strategic
performance reviews
- Benefits
realization
- Governance
checkpoints
- Executive
dashboard and reporting exercise
- Strategic
Capstone: Enterprise Cloud Data Analytics Transformation Plan
- Comprehensive
organizational case study
- Current-state
maturity and capability assessment
- Target-state
architecture and operating model
- Governance,
security, financial, and risk strategy
- Development
of an integrated enterprise transformation plan
- Capstone
Presentation, Strategic Review, and Leadership Action Plan
- Presentation
of the cloud analytics strategy
- Stakeholder
challenge and scenario analysis
- Roadmap,
investment, governance, and performance review
- Refinement of
strategic priorities
- Final
leadership action plan for sustainable cloud data analytics transformation