In the increasingly complex and high-stakes environment of Public-Private Partnerships (PPPs), adopting Data-Driven Decision Making in PPP Procurement is no longer an option but a strategic imperative for governments seeking to optimize outcomes, mitigate risks, and ensure transparency and accountability throughout the entire project lifecycle. Moving beyond intuition and anecdotal evidence, leveraging robust data analytics provides invaluable insights into market conditions, bidder performance, financial viability, and risk allocation, enabling public authorities to make more informed, objective, and defensible choices that maximize value for money. This comprehensive training course is meticulously designed to equip public sector officials, procurement specialists, financial analysts, and project managers with the advanced knowledge and practical skills required to collect, analyze, interpret, and apply data effectively to every stage of PPP procurement, from project identification to contract award and beyond. Without a systematic approach to Data-Driven Decision Making in PPP Procurement, public authorities risk suboptimal project structuring, inefficient resource allocation, and a failure to secure the full benefits of private sector engagement, underscoring the vital need for specialized expertise in this critical domain.

Duration: 10 Days

Target Audience

• Public Sector Procurement Officers and Specialists
• PPP Unit Professionals
• Government Officials involved in project appraisal and evaluation
• Data Analysts and Business Intelligence Specialists in government
• Financial Analysts and Modellers
• Project Managers overseeing PPP procurement
• Auditors and Oversight Body Personnel
• Policy Makers and Regulators
• Consultants providing data analytics and procurement advisory services
• Anyone seeking to enhance their analytical capabilities for PPP decision-making.

Objectives

• Understand the fundamental principles and benefits of data-driven decision making in PPPs.
• Learn about the types of data relevant to each stage of PPP procurement.
• Acquire skills in collecting, validating, and managing high-quality PPP data.
• Comprehend techniques for applying statistical analysis and modeling to procurement data.
• Explore strategies for leveraging data for market sounding and project structuring.
• Understand the importance of data analytics in bid evaluation and VfM assessment.
• Gain insights into using data to identify and mitigate procurement risks.
• Develop a practical understanding of performance benchmarking in PPPs.
• Master data visualization techniques for effective communication of insights.
• Acquire skills in establishing a data governance framework for PPPs.
• Learn to apply international best practices in data-driven procurement.
• Comprehend techniques for building analytical capacity within public institutions.
• Explore strategies for ensuring transparency and accountability through data.
• Understand the importance of continuous learning and improvement from procurement data.
• Develop the ability to lead and implement data-driven approaches to PPP procurement.

Course Content

Module 1: Introduction to Data-Driven Decision Making in PPPs

• Rationale for data-driven approaches in complex PPPs.
• Benefits: improved VfM, reduced risks, enhanced transparency, better accountability.
• Challenges: data availability, quality, analytical capacity.
• Overview of the data lifecycle in PPP procurement.
• The role of data in moving from intuition to evidence-based decisions.

Module 2: Types of Data in PPP Procurement

• Project-specific data (feasibility studies, financial models, contracts).
• Market data (bidder information, market trends, financing costs).
• Performance data (KPIs, SLAs, operational metrics).
• Risk data (risk registers, incident logs, mitigation effectiveness).
• Public sector comparator (PSC) data and historical procurement data.

Module 3: Data Collection, Validation, and Management

• Designing data collection frameworks for PPPs.
• Sources of data: internal systems, external databases, market surveys.
• Data validation techniques to ensure accuracy and reliability.
• Data cleaning and standardization.
• Establishing robust data management systems and protocols.

Module 4: Data Governance and Ethics

• Principles of data governance: ownership, quality, security, privacy.
• Legal and ethical considerations in collecting and using PPP data.
• Data sharing protocols and confidentiality agreements.
• Ensuring data integrity and preventing manipulation.
• Role of data protection regulations.

Module 5: Data Analytics for Project Identification and Screening

• Using historical data to identify suitable sectors and project types for PPPs.
• Analyzing market trends and investor appetite.
• Geospatial data for site selection and impact assessment.
• Predictive analytics for early risk identification.
• Informing the project pipeline development.

Module 6: Data-Driven Market Sounding and Strategy

• Analyzing market responses to RFIs and market engagement events.
• Identifying key market concerns and preferences from data.
• Using data to refine project scope, risk allocation, and procurement strategy.
• Benchmarking against comparable projects and market conditions.
• Informing the design of competitive tender documents.

Module 7: Data Analytics in Bid Evaluation (Technical)

• Quantifying technical evaluation criteria where possible.
• Analyzing bidder experience and track record data.
• Using scoring models and weighting systems effectively.
• Identifying patterns in technical proposals.
• Supporting objective technical assessment.

Module 8: Data Analytics in Bid Evaluation (Financial)

• Advanced analysis of bidders' financial models.
• Stress testing financial assumptions and sensitivities.
• Comparing financial proposals using NPV, IRR, and DSCR metrics.
• Data-driven Value for Money (VfM) assessment against the PSC.
• Identifying financial outliers and potential risks.

Module 9: Risk Analytics in Procurement

• Using data to identify and quantify procurement-specific risks (e.g., bid non-compliance, challenges).
• Analyzing historical data on project failures and their root causes.
• Developing risk models and probability assessments.
• Data-driven insights for risk allocation and mitigation strategies.
• Monitoring risk indicators during procurement.

Module 10: Performance Benchmarking and Post-Award Data

• Establishing benchmarks for project costs, timelines, and performance.
• Collecting and analyzing operational data post-award (KPIs, SLAs).
• Comparing actual performance against contractual targets and forecasts.
• Using data to identify underperformance and trigger contractual remedies.
• Informing future project structuring and procurement.

Module 11: Data Visualization and Reporting

• Principles of effective data visualization for complex PPP data.
• Designing clear and actionable dashboards and reports.
• Communicating insights to diverse stakeholders (e.g., policymakers, public, private sector).
• Tools for data visualization (e.g., Excel charts, specialized software).
• Storytelling with data for persuasive communication.

Module 12: Building Analytical Capacity in Public Institutions

• Assessing current analytical capabilities within PPP units and procurement agencies.
• Training programs for data literacy and advanced analytics skills.
• Recruiting and retaining data professionals.
• Fostering a culture of data-driven decision making.
• Leveraging external expertise where needed.

Module 13: Technology Enablers for Data-Driven PPPs

• Role of e-procurement platforms in data collection.
• Business intelligence (BI) tools and data warehouses.
• Cloud computing for data storage and processing.
• Introduction to AI/ML for advanced analytics (conceptual).
• Cybersecurity considerations for digital data.

Module 14: International Best Practices in Data-Driven Procurement

• Review of data-driven approaches in leading PPP markets.
• Case studies of successful data utilization in complex infrastructure projects.
• Lessons learned from challenges in implementing data-driven strategies.
• Guidelines and recommendations from international organizations.
• Adapting global best practices to local contexts.

Module 15: Case Studies and Practical Exercises in Data Analytics

• In-depth analysis of real-world PPP data sets.
• Workshop: Hands-on exercises in data cleaning, analysis, and visualization using Excel or other tools.
• Applying analytical techniques to hypothetical bid evaluation scenarios.
• Developing a data-driven decision-making framework for a specific PPP stage.
• Group discussions on challenges and opportunities for data in PPPs.

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: info@skillsforafrica.org, training@skillsforafrica.org Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

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

Certification

Participants will be issued with Skills for Africa Training Institute 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: info@skillsforafrica.org, training@skillsforafrica.org Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 7 working days before commencement of the training.