Be the first to know when new training courses are scheduled or dates are updated.
We run this course regularly across Nairobi, Mombasa, Kampala, Dar es Salaam, Kigali, Johannesburg, Dubai, Singapore, China and many more locations. The next intake dates will be published here shortly.
Need it sooner? Reach out and we'll fast-track a session for you or your team.
Prefer email? Submit a scheduling request and we'll get back to you shortly.
The Data-Driven Leadership Executive Course is a comprehensive executive development programme designed to equip Chief Executive Officers, board members, directors, senior managers, policymakers, strategy leaders, and organisational decision-makers with the leadership competencies required to transform organisational data into strategic intelligence, evidence-based decisions, measurable performance improvements, and sustainable organisational value. The course integrates data-driven leadership, data strategy, business intelligence, data analytics, Artificial Intelligence, Generative AI, predictive analytics, executive dashboards, data visualisation, strategic decision-making, performance management, data governance, and digital transformation. Participants develop the executive capability to ask the right questions of organisational data, interpret analytical evidence, challenge assumptions, evaluate data quality, identify meaningful trends, and combine quantitative evidence with strategic judgement when making complex leadership decisions.
The programme provides practical approaches for developing enterprise data strategies, strengthening data literacy, establishing data governance, improving data quality, building executive dashboards, defining Key Performance Indicators, and integrating analytics into strategic planning and organisational performance management. Participants explore descriptive analytics, diagnostic analytics, predictive analytics, prescriptive analytics, business intelligence, statistical interpretation, forecasting, scenario analysis, benchmarking, data storytelling, visualisation, performance dashboards, and decision-support systems from an executive rather than highly technical perspective. Particular emphasis is placed on translating organisational strategy into measurable indicators, identifying reliable data sources, evaluating analytical results, distinguishing correlation from causation, recognising bias and uncertainty, and using evidence responsibly to improve financial, operational, customer, workforce, programme, project, and institutional decisions.
The Data-Driven Leadership Executive Course further examines Artificial Intelligence, Generative AI, machine learning, intelligent automation, big data, cloud analytics, real-time data, digital platforms, data governance, data privacy, cybersecurity, responsible AI, algorithmic bias, data ethics, and emerging technologies transforming executive decision-making. Participants examine how leaders can establish effective governance structures for organisational data and analytics while protecting confidentiality, privacy, security, accuracy, integrity, accessibility, and appropriate use. The programme also addresses organisational data culture, leadership accountability, cross-functional collaboration, data democratisation, analytical talent, data literacy, stakeholder communication, change management, innovation, and the organisational capabilities required to embed evidence-based decision-making throughout an institution.
By the end of the Data-Driven Leadership Executive Course, participants will be equipped to lead organisations in which reliable data, analytics, Artificial Intelligence, business intelligence, and human judgement work together to improve strategy, performance, innovation, accountability, and stakeholder outcomes. Through executive presentations, practical analytical exercises, dashboard interpretation, data-driven decision simulations, scenario analysis, collaborative activities, facilitated discussions, and relevant general case studies, participants translate data leadership concepts into practical organisational actions. The programme culminates in the development of a Data-Driven Leadership Executive Action Plan integrating strategic data priorities, governance, analytics, performance indicators, dashboards, Artificial Intelligence opportunities, organisational capabilities, ethical safeguards, stakeholder responsibilities, and a practical roadmap for strengthening data-driven decision-making.
By the end of the Data-Driven Leadership Executive Course, participants will be able to:
1. Understand the principles of data-driven leadership, evidence-based management, business intelligence, data analytics, Artificial Intelligence, and their strategic importance to modern organisations.
2. Develop an organisational data strategy aligned with corporate strategy, strategic priorities, operational requirements, stakeholder expectations, and measurable organisational outcomes.
3. Apply descriptive, diagnostic, predictive, and prescriptive analytics concepts to executive decision-making, strategic planning, forecasting, risk management, and organisational performance.
4. Interpret executive dashboards, Key Performance Indicators, analytical reports, visualisations, trends, forecasts, benchmarks, and statistical evidence for strategic decision-making.
5. Establish effective data governance covering ownership, accountability, quality, standards, security, privacy, accessibility, integration, retention, and responsible data use.
6. Evaluate Artificial Intelligence, Generative AI, machine learning, predictive analytics, intelligent automation, and emerging data technologies from an executive leadership perspective.
7. Strengthen organisational performance management by connecting strategic objectives with reliable data, KPIs, targets, executive dashboards, performance reviews, and corrective actions.
8. Build a data-driven organisational culture through executive leadership, data literacy, analytical capability, employee engagement, collaboration, accountability, and evidence-based management.
9. Apply data ethics, responsible AI, cybersecurity, privacy, transparency, human oversight, and risk management principles to organisational analytics and automated decision-support systems.
10. Develop a practical Data-Driven Leadership Executive Action Plan that strengthens data governance, analytics capabilities, executive decision-making, organisational performance, innovation, and sustainable value creation.
Organizations whose executives participate in this training will benefit through:
1. Stronger executive capacity to use data, analytics, business intelligence, and Artificial Intelligence to support strategic planning and organisational decision-making.
2. Improved quality, consistency, speed, transparency, and accountability of executive decisions through structured use of reliable organisational evidence.
3. Stronger enterprise data strategies aligned with organisational objectives, digital transformation, operational priorities, customer requirements, and stakeholder outcomes.
4. Improved organisational performance management through strategically aligned KPIs, executive dashboards, targets, benchmarking, analytics, and performance-review systems.
5. Enhanced data governance, data quality, information security, privacy, accountability, regulatory compliance, and responsible organisational data management.
6. Improved forecasting, scenario planning, risk analysis, customer intelligence, workforce analytics, financial analysis, operational analytics, and strategic performance assessment.
7. Stronger executive understanding of Artificial Intelligence, Generative AI, predictive analytics, machine learning, intelligent automation, and emerging data technologies.
8. Development of a data-driven organisational culture characterised by evidence-based management, analytical thinking, data literacy, collaboration, innovation, learning, and accountability.
9. Reduced risk of poor decisions caused by unreliable data, misleading metrics, analytical bias, incorrect interpretation, inappropriate AI use, weak governance, and inadequate executive oversight.
10. Development of practical data and analytics initiatives that improve organisational productivity, strategic performance, service delivery, innovation, stakeholder value, and long-term sustainability.
The Data-Driven Leadership Executive Course is designed for Chief Executive Officers (CEOs), Managing Directors, Executive Directors, Board Members, Directors, Deputy Directors, Commissioners, Permanent Secretaries, Principal Secretaries, senior government officials, policymakers, Chief Operating Officers (COOs), Chief Financial Officers (CFOs), Chief Strategy Officers, Chief Information Officers (CIOs), Chief Technology Officers (CTOs), Chief Digital Officers (CDOs), Chief Data Officers, Chief Risk Officers, strategy directors, planning directors, finance directors, monitoring and evaluation directors, programme directors, project directors, digital transformation directors, data and analytics directors, regional directors, country directors, heads of departments, business unit leaders, senior managers, strategy managers, performance managers, data managers, business intelligence managers, programme managers, project managers, public-sector executives, NGO executives, development-sector leaders, financial institution executives, institutional leaders, consultants, entrepreneurs, and other senior decision-makers responsible for organisational strategy, performance, data, analytics, technology, transformation, governance, or evidence-based decision-making.
1. Understanding data-driven leadership, evidence-based management, data analytics, business intelligence, decision intelligence, digital leadership, and their contribution to organisational performance.
2. Understanding the executive role in transforming raw data into information, analytical insight, strategic intelligence, informed decisions, organisational actions, and measurable outcomes.
3. Distinguishing data-driven, data-informed, experience-based, intuition-based, and judgement-based decision-making and determining when each approach is appropriate.
4. Developing executive data literacy covering metrics, analytical terminology, data interpretation, trends, probabilities, uncertainty, statistical evidence, visualisations, and analytical limitations.
5. Identifying common executive decision-making problems including confirmation bias, selective evidence, misleading metrics, poor data quality, overconfidence, information overload, and inappropriate reliance on analytics.
6. Executive Case Study: Evaluating a major organisational decision where senior executives receive conflicting analytical reports, incomplete information, stakeholder pressure, and competing recommendations.
1. Understanding enterprise data strategy and aligning data priorities with organisational vision, corporate strategy, strategic objectives, business requirements, stakeholder expectations, and digital transformation.
2. Identifying critical organisational data assets, information requirements, decision points, data sources, analytical priorities, performance measures, and strategic data gaps.
3. Developing data strategy components covering governance, architecture, quality, integration, accessibility, analytics, technology, people, security, privacy, and organisational capabilities.
4. Establishing data priorities and use cases according to strategic value, decision-making requirements, feasibility, organisational readiness, expected benefits, implementation cost, and risk.
5. Developing data and analytics roadmaps containing strategic initiatives, capabilities, investments, responsibilities, technology requirements, milestones, performance measures, and implementation timelines.
6. Executive Case Study: Developing an enterprise data strategy for an organisation where departments operate separate databases, executives receive inconsistent reports, and strategic decisions are delayed by unreliable information.
1. Understanding data governance principles covering ownership, stewardship, accountability, standards, policies, decision rights, data quality, accessibility, security, privacy, and responsible data use.
2. Establishing data governance structures including executive committees, data owners, data stewards, technology teams, business units, risk functions, compliance teams, and governance responsibilities.
3. Managing data quality through accuracy, completeness, consistency, validity, timeliness, uniqueness, integrity, relevance, and fitness-for-purpose requirements.
4. Understanding data lifecycle management covering collection, creation, classification, storage, integration, access, sharing, retention, archiving, disposal, and continuous quality improvement.
5. Developing master data management, metadata management, data dictionaries, common definitions, data standards, reference data, and organisational information-management practices.
6. Executive Case Study: Resolving an organisational data-quality crisis where finance, operations, human resources, and programme departments report different figures for the same executive performance indicators.
1. Understanding business intelligence, management information systems, executive information systems, analytical reporting, dashboards, scorecards, and organisational decision-support systems.
2. Designing executive dashboards that provide timely, relevant, reliable, concise, actionable, and strategically aligned information for leadership decision-making.
3. Selecting meaningful Key Performance Indicators, strategic measures, targets, thresholds, benchmarks, trends, leading indicators, lagging indicators, and performance alerts.
4. Applying data visualisation principles to charts, graphs, maps, tables, scorecards, dashboards, trend lines, comparative analysis, and executive presentations.
5. Developing data storytelling capabilities that combine analytical evidence, organisational context, visualisation, narrative, interpretation, recommendations, and strategic implications.
6. Executive Case Study: Redesigning an overloaded executive dashboard containing hundreds of indicators into a focused strategic dashboard highlighting critical performance, risk, financial, customer, workforce, and operational information.
1. Understanding descriptive analytics and using historical data to evaluate organisational performance, trends, patterns, distributions, comparisons, outcomes, and operational results.
2. Applying diagnostic analytics to understand why performance changes occurred and identify relationships, contributing factors, underlying causes, anomalies, and organisational issues.
3. Understanding averages, percentages, ratios, growth rates, variance, distributions, correlations, segmentation, benchmarking, and other analytical measures used in executive reporting.
4. Applying trend analysis, variance analysis, root-cause analysis, comparative analysis, cohort analysis, segmentation, benchmarking, and performance decomposition.
5. Distinguishing correlation from causation and recognising analytical limitations, confounding factors, incomplete evidence, misleading comparisons, outliers, and inappropriate conclusions.
6. Executive Case Study: Diagnosing why organisational revenue, customer satisfaction, service quality, employee productivity, or programme performance has declined despite apparently favourable headline indicators.
1. Understanding predictive analytics, forecasting, probability, predictive models, machine learning, risk prediction, demand forecasting, and forward-looking organisational intelligence.
2. Applying forecasting approaches to revenue, costs, customer demand, staffing requirements, project performance, service utilisation, operational capacity, financial performance, and organisational risks.
3. Understanding assumptions, confidence levels, prediction ranges, uncertainty, model accuracy, historical limitations, data requirements, and factors affecting forecast reliability.
4. Applying scenario planning to develop baseline, optimistic, pessimistic, disruption, growth, and alternative future scenarios for strategic decision-making.
5. Using sensitivity analysis, stress testing, assumptions testing, trend analysis, early-warning indicators, and predictive information to strengthen organisational preparedness.
6. Executive Case Study: Developing strategic scenarios for an organisation facing uncertain funding, changing customer demand, inflation, technology disruption, workforce constraints, and regulatory changes.
1. Understanding prescriptive analytics and using analytical evidence to identify recommended actions, optimise resources, evaluate alternatives, and improve strategic decisions.
2. Applying decision models, optimisation concepts, cost-benefit analysis, multi-criteria analysis, risk-adjusted evaluation, scenario comparison, and strategic option assessment.
3. Combining financial, operational, customer, workforce, risk, stakeholder, environmental, and strategic data when evaluating complex executive decisions.
4. Developing structured executive decision frameworks that integrate analytical evidence, organisational strategy, risk, stakeholder interests, ethics, feasibility, uncertainty, and leadership judgement.
5. Establishing decision-monitoring mechanisms that track implementation outcomes, test assumptions, compare expected and actual results, and enable corrective action.
6. Executive Case Study: Selecting between competing strategic investments using financial projections, stakeholder data, operational capacity, organisational risks, expected benefits, and long-term strategic value.
1. Understanding Artificial Intelligence, machine learning, Generative AI, Large Language Models, natural language processing, predictive models, AI assistants, AI agents, and intelligent automation.
2. Identifying executive AI applications in strategic planning, forecasting, finance, human resources, customer service, marketing, operations, procurement, risk management, research, and performance reporting.
3. Applying Generative AI to executive research, information synthesis, document analysis, scenario development, communication, reporting, knowledge management, and decision support.
4. Evaluating AI-generated insights and understanding hallucinations, bias, model limitations, data dependency, uncertainty, inappropriate automation, and the continuing importance of human judgement.
5. Establishing responsible AI governance covering accountability, transparency, explainability, fairness, privacy, cybersecurity, intellectual property, ethical use, human oversight, and regulatory compliance.
6. Executive Case Study: Evaluating whether an organisation should deploy an AI-powered executive decision-support system while balancing productivity benefits against accuracy, bias, privacy, cybersecurity, governance, and accountability risks.
1. Translating organisational strategy into measurable objectives, Key Performance Indicators, targets, milestones, scorecards, performance dashboards, and accountability mechanisms.
2. Aligning corporate, departmental, programme, project, team, and individual performance measures with organisational strategic priorities and expected outcomes.
3. Developing Balanced Scorecards, Objectives and Key Results, results frameworks, performance scorecards, executive dashboards, and integrated performance-management systems.
4. Conducting performance reviews using trend analysis, variance analysis, benchmarking, forecasts, strategic risks, operational evidence, financial data, and stakeholder outcomes.
5. Establishing corrective-action mechanisms that identify performance gaps, analyse root causes, assign responsibilities, define interventions, establish timelines, and monitor improvement.
6. Executive Case Study: Conducting a quarterly executive performance review where several strategic KPIs are below target and leadership must identify causes, prioritise interventions, reallocate resources, and establish corrective actions.
1. Understanding data ethics, responsible data use, privacy, confidentiality, transparency, fairness, accountability, consent, proportionality, and ethical executive decision-making.
2. Identifying data risks including unauthorised access, data breaches, inaccurate information, inappropriate sharing, excessive collection, misuse, discrimination, manipulation, and privacy violations.
3. Understanding cybersecurity governance and protecting organisational data through access controls, authentication, encryption, classification, monitoring, incident response, employee awareness, and executive oversight.
4. Managing analytical bias including selection bias, measurement bias, sampling bias, historical bias, algorithmic bias, interpretation bias, and unintended discriminatory outcomes.
5. Establishing governance for Artificial Intelligence and automated decision systems covering human oversight, accountability, transparency, validation, monitoring, escalation, and responsible use.
6. Executive Case Study: Responding to a data governance incident where sensitive organisational information is improperly accessed and an automated analytical system produces potentially biased recommendations affecting stakeholders.
1. Understanding the characteristics of a data-driven organisational culture including evidence-based decision-making, curiosity, transparency, experimentation, learning, collaboration, accountability, and responsible data use.
2. Strengthening organisational data literacy among executives, managers, employees, technical teams, programme staff, and other decision-makers through structured capability development.
3. Developing analytical talent and organisational capabilities covering data analysts, data scientists, business intelligence professionals, data engineers, data stewards, technology specialists, and business translators.
4. Breaking down organisational data silos through cross-functional collaboration, common data standards, integrated platforms, shared accountability, accessible information, and executive leadership.
5. Leading organisational change associated with data transformation by managing resistance, communicating benefits, building employee confidence, establishing incentives, and reinforcing evidence-based behaviours.
6. Executive Case Study: Transforming an organisation where decisions are traditionally based on hierarchy and intuition into a collaborative, evidence-based organisation supported by reliable data, analytics, leadership accountability, and employee data literacy.
1. Integrating data strategy, analytics, Artificial Intelligence, business intelligence, performance management, governance, technology, people, organisational culture, cybersecurity, and strategic leadership.
2. Assessing organisational data maturity across leadership, governance, quality, technology, analytics, skills, culture, accessibility, security, privacy, Artificial Intelligence, and decision-making capabilities.
3. Prioritising data and analytics investments according to strategic importance, organisational value, decision-making requirements, implementation feasibility, capability requirements, risk, and expected benefits.
4. Establishing data leadership governance covering executive sponsorship, decision rights, investment priorities, accountability, organisational responsibilities, performance monitoring, and continuous improvement.
5. Developing measurable data leadership priorities covering data quality, executive dashboards, analytics adoption, Artificial Intelligence, data literacy, governance, decision-making, performance improvement, and organisational value.
6. Executive Case Study and Capstone Exercise: Developing and presenting a comprehensive Data-Driven Leadership Executive Action Plan integrating strategic data priorities, governance, data quality, analytics, executive dashboards, Artificial Intelligence, performance indicators, workforce capabilities, ethical safeguards, cybersecurity, accountability, and a 90-day implementation roadmap.
1. Customized Training: All our courses can be tailored to meet the specific needs of participants.
2. Language Proficiency: Participants should have a good command of the English language.
3. Comprehensive Learning: Our training includes well-structured presentations, practical exercises, web-based tutorials, and collaborative group work. Our facilitators are seasoned experts with over a decade of experience.
4. Certification: Upon successful completion of training, participants will receive a certificate from Foscore Development Center (FDC-K).
5. Training Locations: Training sessions are conducted at Foscore Development Center (FDC-K) centers. We also offer options for in-house and online training, customized to the client's schedule.
6. Flexible Duration: Course durations are adaptable, and content can be adjusted to fit the required number of days.
7. Onsite Training Inclusions: The course fee for onsite training covers facilitation, training materials, two coffee breaks, a buffet lunch, and a Certificate of Successful Completion. Participants are responsible for their travel expenses, airport transfers, visa applications, dinners, health/accident insurance, and personal expenses.
8. Additional Services: Accommodation, pickup services, flight booking, and visa processing arrangements are available upon request at discounted rates.
9. Equipment: Tablets and laptops can be provided to participants at an additional cost.
10. Post-Training Support: We offer one year of free consultation and coaching after the course.
11. Group Discounts: Register as a group of more than two and enjoy a discount ranging from 10% to 50%.
12. Payment Terms: Payment should be made before the commencement of the training or as mutually agreed upon, to the Foscore Development Center account. This ensures better preparation for your training.
13. Contact Us: For any inquiries, please reach out to us at training@fdc-k.org or call us at +254712260031.
14. Website: Visit our website at www.fdc-k.org for more information.