Course Introduction
Data platforms have become strategic organizational capabilities that support analytics, artificial intelligence, reporting, operational intelligence, data sharing, and digital transformation. However, securing investment for a data platform requires more than presenting technical architecture or describing available technologies. Organizations need credible business cases that connect platform capabilities with measurable business outcomes, financial requirements, operational improvements, risk reduction, and long-term strategic priorities.
This training course provides a comprehensive framework for developing business cases and investment plans for modern data platforms. Participants will learn how to assess organizational data needs, identify investment opportunities, define platform objectives, estimate costs, quantify benefits, evaluate risks, and develop investment scenarios that enable decision-makers to understand the strategic and financial implications of data platform initiatives.
The course examines the complete business-case development lifecycle, from opportunity identification and current-state assessment through requirements analysis, target-state planning, investment estimation, benefits modelling, financial evaluation, risk analysis, implementation planning, and executive presentation. Participants will explore practical techniques for translating technical platform capabilities into business language that can support funding requests, investment committees, strategic planning processes, and portfolio decisions.
Participants will also examine the financial dimensions of data platform investment, including infrastructure, cloud consumption, software licensing, data engineering, security, governance, integration, implementation, support, skills development, and ongoing operational costs. Cost models will be connected to benefits such as improved decision-making, automation, productivity, revenue enablement, risk reduction, improved customer experiences, regulatory compliance, and faster delivery of analytics and AI capabilities.
Particular emphasis is placed on investment planning under uncertainty. Data platform initiatives can involve evolving requirements, changing technology costs, uncertain adoption levels, emerging AI capabilities, migration complexity, and dependencies on existing systems. The course therefore introduces scenario planning, sensitivity analysis, phased investment, prioritization, benefits realization, financial controls, and investment governance approaches that help organizations make informed decisions while maintaining flexibility.
By the end of the course, participants will be able to develop structured and evidence-based business cases for data platform initiatives and translate them into practical investment roadmaps. They will be equipped to communicate financial and strategic value to executives, compare alternative investment options, establish measurable benefits, and create sustainable funding and implementation approaches for data platforms in environments increasingly shaped by cloud computing, artificial intelligence, automation, and advanced analytics.
Duration
10 days
Who Should Attend
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Data managers responsible for developing or expanding organizational data platforms.
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IT managers involved in technology investment planning and enterprise data infrastructure.
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Chief data, digital, information, and technology professionals supporting strategic investment decisions.
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Business analysts responsible for developing business cases and investment proposals.
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Data architects contributing technical inputs to platform investment decisions.
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Enterprise architects evaluating target-state data platform capabilities and technology strategies.
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Finance professionals involved in technology budgeting, financial modelling, and investment appraisal.
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Project and programme managers responsible for data platform implementation initiatives.
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Data governance professionals supporting investment decisions involving data quality, security, privacy, and compliance.
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Analytics and business intelligence leaders planning scalable data and analytics capabilities.
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Data engineering managers responsible for platform architecture, pipelines, integration, and operational capabilities.
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AI and machine learning leaders evaluating infrastructure requirements for advanced analytics and intelligent applications.
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Procurement and commercial professionals supporting data platform sourcing and technology acquisition.
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Strategy and transformation professionals developing digital and data transformation investment portfolios.
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Risk and compliance professionals assessing the financial and operational risks associated with data platform investments.
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Senior business leaders evaluating the strategic value and financial sustainability of data initiatives.
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Consultants supporting data strategy, technology transformation, investment planning, and business-case development.
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Organizations seeking structured approaches for prioritizing and funding data platform modernization.
Course Objectives
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Develop a comprehensive understanding of data platform business cases, investment planning principles, strategic alignment, and organizational value creation.
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Assess current-state data capabilities, technology environments, operational challenges, and business opportunities to establish a strong investment rationale.
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Translate data platform capabilities into measurable business outcomes, strategic benefits, operational improvements, and risk-reduction opportunities.
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Develop structured investment scenarios covering technology, infrastructure, people, implementation, governance, security, integration, and ongoing operational requirements.
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Build realistic cost models covering capital expenditure, operating expenditure, cloud consumption, licensing, migration, support, and platform lifecycle expenses.
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Apply financial appraisal techniques including return on investment, total cost of ownership, payback analysis, net present value, and sensitivity analysis.
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Identify, quantify, and prioritize tangible and intangible benefits associated with data platform modernization and strategic data investments.
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Evaluate alternative platform investment options using business, technical, financial, operational, security, scalability, and strategic criteria.
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Develop phased investment roadmaps that align platform capabilities, funding requirements, implementation dependencies, organizational readiness, and benefits realization.
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Assess investment risks including cost uncertainty, vendor dependency, cloud consumption variability, migration complexity, adoption challenges, and technology obsolescence.
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Establish governance mechanisms for investment approval, funding allocation, portfolio monitoring, benefits tracking, financial control, and investment review.
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Communicate data platform business cases effectively to executives, finance teams, investment committees, technical stakeholders, and other decision-makers.
Comprehensive Course Outline
Module 1: Foundations Of Data Platform Business Cases
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Principles and strategic purpose of developing business cases for organizational data platform investments.
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Relationship between data platforms, analytics, artificial intelligence, digital transformation, and business strategy.
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Components of a strong data platform business case and the evidence required to support investment decisions.
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Roles of business, technology, data, finance, risk, governance, and executive stakeholders in investment planning.
Module 2: Data Platform Strategy And Investment Drivers
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Identifying strategic drivers for data platform investment including growth, efficiency, innovation, risk management, and modernization.
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Linking platform investment objectives with organizational strategies, digital transformation programmes, and data strategies.
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Assessing business pressures created by fragmented systems, data silos, legacy technologies, and growing analytical demands.
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Identifying investment opportunities created by cloud adoption, artificial intelligence, automation, self-service analytics, and data sharing.
Module 3: Current-State Assessment And Capability Analysis
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Assessing existing data architecture, infrastructure, applications, data sources, pipelines, storage, analytics, and integration capabilities.
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Identifying technology limitations, operational bottlenecks, technical debt, scalability challenges, and duplicated data capabilities.
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Evaluating current data quality, governance, security, privacy, accessibility, reliability, and organizational data maturity.
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Developing current-state capability assessments and gap analyses to establish evidence for platform investment.
Module 4: Business Requirements And Target-State Planning
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Translating business priorities and user needs into data platform functional, technical, operational, and service requirements.
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Defining target-state platform capabilities for data ingestion, processing, storage, analytics, governance, integration, and AI workloads.
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Developing target-state architecture concepts that balance scalability, flexibility, performance, security, interoperability, and cost.
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Aligning future platform capabilities with expected organizational growth, data volumes, analytical workloads, and emerging requirements.
Module 5: Business Value And Benefits Identification
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Identifying direct, indirect, strategic, operational, financial, customer, and risk-related benefits from platform investments.
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Connecting data platform capabilities with productivity improvements, automation, decision quality, revenue opportunities, and service improvements.
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Developing benefit hypotheses and measurable indicators for evaluating expected platform outcomes.
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Distinguishing measurable financial benefits from qualitative and strategic benefits requiring structured evidence and assumptions.
Module 6: Data Platform Cost Modelling
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Identifying platform cost categories including infrastructure, cloud services, software, licensing, integration, migration, and implementation.
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Estimating people-related costs covering data engineering, architecture, security, governance, support, training, and change management.
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Modelling recurring operational costs such as cloud consumption, maintenance, monitoring, support, storage, and data transfer.
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Developing transparent cost assumptions, cost drivers, contingency allowances, and lifecycle cost estimates for investment proposals.
Module 7: Financial Analysis And Investment Appraisal
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Applying return on investment, payback period, total cost of ownership, and cost-benefit analysis to data platform investments.
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Understanding net present value, discounting, cash-flow modelling, investment horizons, and financial assumptions for technology initiatives.
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Developing sensitivity analysis to examine how changes in costs, adoption, benefits, usage, and implementation timelines affect investment outcomes.
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Comparing alternative financial scenarios and presenting investment implications clearly to finance and executive stakeholders.
Module 8: Investment Options And Scenario Planning
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Developing alternative investment scenarios such as build, buy, modernize, migrate, outsource, hybrid, and phased approaches.
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Comparing cloud, on-premises, hybrid, managed, and platform-as-a-service investment models using organizational requirements.
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Evaluating trade-offs between upfront investment, recurring expenditure, flexibility, control, scalability, and operational complexity.
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Applying scenario planning to address uncertainty around technology evolution, business growth, data volumes, adoption, and future workloads.
Module 9: Investment Prioritization And Portfolio Planning
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Developing criteria for prioritizing data platform investments based on strategic value, urgency, feasibility, risk, cost, and expected benefits.
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Establishing investment portfolios that balance foundational platform capabilities with high-value analytics and AI use cases.
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Sequencing initiatives according to dependencies, organizational readiness, technical complexity, funding availability, and expected value.
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Developing prioritization frameworks that support transparent allocation of limited financial and organizational resources.
Module 10: Risk, Governance And Investment Controls
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Identifying financial, technical, operational, security, privacy, compliance, vendor, and implementation risks affecting platform investments.
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Developing risk assessment models covering probability, impact, mitigation actions, ownership, and residual exposure.
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Establishing investment governance structures for approval, funding control, change management, monitoring, and escalation.
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Designing investment controls that maintain accountability while allowing data platform programmes to respond to changing requirements.
Module 11: Cloud Economics And Data Platform FinOps
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Understanding cloud consumption models and their implications for data storage, processing, analytics, integration, and AI workloads.
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Developing cloud cost allocation, forecasting, monitoring, budgeting, and optimization approaches for data platforms.
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Identifying cost drivers including compute utilization, storage growth, data transfer, workload patterns, licensing, and managed services.
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Integrating FinOps principles into investment planning, platform architecture decisions, operational governance, and benefits management.
Module 12: Business Case Development And Executive Communication
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Structuring compelling business cases with executive summaries, strategic rationale, options, financial analysis, risks, and recommendations.
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Presenting complex technical and financial information in language appropriate for executives, boards, investment committees, and finance teams.
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Developing investment narratives that connect platform capabilities with measurable organizational priorities and expected outcomes.
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Preparing presentation materials, financial summaries, decision papers, supporting evidence, and responses to investment challenges.
Module 13: Implementation Roadmaps And Funding Models
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Developing multi-phase data platform implementation roadmaps aligned with funding availability, dependencies, organizational readiness, and business priorities.
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Designing funding approaches for platform foundations, migration programmes, analytics capabilities, AI workloads, and continuous modernization.
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Establishing implementation milestones, investment gates, release priorities, adoption targets, and benefits realization checkpoints.
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Connecting investment plans with programme management, procurement, resource planning, architecture governance, and operational readiness.
Module 14: Benefits Realization And Investment Performance
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Establishing benefits realization frameworks for tracking financial, operational, strategic, analytical, and organizational outcomes.
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Developing key performance indicators and dashboards for monitoring investment progress, platform adoption, utilization, and business value.
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Comparing actual expenditure and realized benefits against business-case assumptions and approved investment plans.
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Managing benefit gaps through corrective actions, reprioritization, optimization, scope adjustment, and investment review processes.
Module 15: Data Platform Sustainability And Lifecycle Investment
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Planning long-term platform sustainability through technology refresh, capacity management, skills development, support models, and architecture evolution.
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Evaluating technology lifecycle risks including platform obsolescence, technical debt, vendor dependency, licensing changes, and migration requirements.
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Incorporating security, privacy, resilience, disaster recovery, sustainability, and regulatory requirements into long-term investment planning.
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Developing continuous investment strategies that balance platform stability with innovation, modernization, experimentation, and emerging technology adoption.
Module 16: Emerging Trends And Future Of Data Platform Investment
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Evaluating investment implications of generative AI, agentic systems, autonomous analytics, vector databases, and AI-native data architectures.
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Assessing emerging data platform models including data mesh, data fabric, lakehouse architectures, real-time platforms, and composable data ecosystems.
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Exploring AI-assisted financial modelling, intelligent investment planning, automated cost optimization, and predictive platform capacity management.
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Preparing investment strategies for rapidly evolving technologies, sustainability requirements, regulatory changes, cybersecurity threats, and changing enterprise data needs.
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.
