Training course

Overview

Data-Driven Decision Making is a professional training course designed to equip managers, analysts, professionals, and organisational leaders with the knowledge and practical skills required to make better decisions using reliable data and evidence. The course develops a structured approach to converting organisational data into actionable insights, enabling participants to define decision problems, identify relevant evidence, evaluate alternatives, manage uncertainty, and translate analytical findings into practical actions. Participants explore the complete decision-making cycle from problem definition and data requirements through analysis, interpretation, communication, implementation, monitoring, and continuous improvement.

The course provides practical techniques for working with business, operational, financial, customer, workforce, programme, project, and performance data. Participants learn to use Microsoft Excel, pivot tables, formulas, charts, dashboards, KPI frameworks, decision matrices, scenario analysis, sensitivity analysis, and structured analytical templates to support everyday and strategic decisions. Established frameworks such as the PDCA cycle, Balanced Scorecard, Results-Based Management, SMART objectives, root-cause analysis, risk-based decision-making, and evidence-to-action approaches are incorporated to help participants connect analytical work with organisational priorities and measurable outcomes.

A major focus of the training is analytical judgement and evidence quality. Participants learn to assess data relevance, accuracy, completeness, consistency, timeliness, source credibility, assumptions, and limitations before relying on evidence for important decisions. They explore descriptive statistics, trends, variance analysis, correlation, regression, probability, confidence intervals, uncertainty, and forecasting concepts while learning to distinguish correlation from causation and statistical evidence from practical or strategic significance. The course also addresses cognitive bias, confirmation bias, anchoring, availability bias, selection bias, missing data, outliers, confounding factors, and other issues that can undermine decision quality.

Through realistic case studies, practical exercises, group discussions, analytical simulations, and an applied capstone, participants progressively develop the ability to make transparent, defensible, and evidence-based decisions. The course concludes with an integrated decision-making exercise requiring participants to diagnose a real-world organisational problem, analyse relevant evidence, compare alternatives, evaluate risks and uncertainty, communicate a decision rationale, and establish implementation and monitoring measures. By combining data analysis, critical thinking, decision frameworks, risk management, and performance monitoring, Data-Driven Decision Making helps organisations strengthen analytical maturity and build more consistent evidence-based decision processes.

Course Duration

5 Days (40 Hours)

Target Participants

This course is suitable for:
• Managers, department heads, and organisational leaders
• Business and data analysts
• Business intelligence and reporting professionals
• Strategy, planning, and transformation professionals
• Programme, portfolio, and project managers
• Operations and service-delivery managers
• Finance, accounting, audit, and risk professionals
• Marketing, sales, customer experience, and market research professionals
• Human resources and workforce analytics professionals
• Monitoring, Evaluation, Research and Learning (MERL/MEL) professionals
• Policy, planning, and development professionals
• Performance management and organisational improvement professionals
• Supply chain, procurement, logistics, and operations professionals
• NGO, government, development, and public-sector professionals
• Consultants, advisers, and professional service providers
• Executives responsible for strategic planning and organisational performance
• Supervisors and team leaders responsible for operational decisions
• Professionals responsible for KPIs, dashboards, management reports, and performance reviews
• Researchers and academics working with organisational or applied data
• Professionals seeking to strengthen evidence-based decision-making and analytical judgement

Course Objectives

By the end of the training, participants will be able to:
• Explain the principles, value, and organisational role of data-driven decision making
• Distinguish between data, information, evidence, insight, judgement, decisions, and actions
• Define decision problems, objectives, constraints, stakeholders, and decision criteria clearly
• Translate organisational challenges into measurable analytical questions and decision requirements
• Identify appropriate data sources and assess data relevance, reliability, quality, and limitations
• Apply Microsoft Excel and practical analytical tools to organise, analyse, and interpret decision-support data
• Use formulas, pivot tables, charts, dashboards, KPIs, and summary tables to support evidence-based decisions
• Analyse trends, patterns, variances, ratios, benchmarks, exceptions, and performance gaps
• Apply descriptive statistics, correlation, regression, probability, confidence intervals, and uncertainty concepts appropriately
• Distinguish correlation from causation and identify unsupported or potentially misleading conclusions
• Recognise cognitive biases, analytical biases, sampling problems, measurement issues, missing data, outliers, and confounding factors
• Apply structured decision-making frameworks to compare alternatives and select appropriate courses of action
• Use decision matrices, weighted criteria, cost-benefit thinking, risk analysis, scenario analysis, and sensitivity analysis
• Apply root-cause analysis, Five Whys, Fishbone Analysis, Pareto Analysis, and PDCA to operational and strategic problems
• Integrate quantitative, qualitative, financial, operational, customer, workforce, and external evidence where appropriate
• Evaluate uncertainty, assumptions, risks, trade-offs, constraints, and potential unintended consequences
• Develop dashboards and performance indicators that support ongoing decision monitoring
• Communicate analytical findings, decision options, recommendations, risks, and limitations clearly to different audiences
• Establish implementation, monitoring, feedback, and review mechanisms for data-driven decisions
• Apply ethical, transparent, responsible, and accountable principles when using data for organisational decisions
• Develop and present a complete evidence-based decision-making solution through an applied capstone exercise

Course Content

Day 1: Foundations of Data-Driven Decision Making

Module 1: Foundations of Data-Driven Decision Making

1.      Understanding Data-Driven Decision Making and Its Organisational Value

2.      Data, Information, Evidence, Insight, Judgement, Decisions, and Actions

3.      The Data-Driven Decision-Making Lifecycle

4.      Defining Decision Problems, Objectives, Constraints, and Stakeholders

5.      Translating Business and Organisational Problems into Analytical Questions

6.      Identifying Decision Criteria, Alternatives, Trade-Offs, and Expected Outcomes

7.      Data Sources, Data Types, Metadata, Context, and Evidence Requirements

8.      Assessing Data Quality: Accuracy, Completeness, Consistency, Timeliness, and Relevance

9.      Decision-Making Frameworks: SMART Objectives, PDCA, Balanced Scorecard, Results-Based Management, and Evidence-to-Action

10.  Case Study and Practical Exercise: Diagnosing an Organisational Decision Problem and Building a Data Requirements Plan

Day 2: Practical Data Analysis and Decision Support

Module 2: Practical Data Analysis and Decision Support

1.      Preparing and Structuring Data for Decision Analysis

2.      Microsoft Excel Techniques for Data-Driven Decision Making

3.      Sorting, Filtering, Formulas, Data Validation, and Conditional Formatting

4.      Pivot Tables, Summary Tables, Aggregation, and Decision-Support Calculations

5.      Developing Charts and Visualisations for Comparative Decision Analysis

6.      Analysing KPIs, Targets, Benchmarks, Ratios, Rates, and Performance Variances

7.      Identifying Trends, Patterns, Exceptions, Anomalies, and Performance Gaps

8.      Designing Management Dashboards and Decision-Support Scorecards

9.      Selecting Relevant Evidence and Building a Structured Decision Evidence Pack

10.  Case Study and Exercise: Analysing Organisational Performance Data and Identifying Evidence-Based Decision Options

Day 3: Analytical Judgement, Statistics, and Evidence Quality

Module 3: Analytical Judgement, Statistics, and Evidence Quality

1.      Descriptive Statistics for Management and Operational Decisions

2.      Distributions, Averages, Percentiles, Variation, and Data Segmentation

3.      Correlation and Regression for Understanding Relationships

4.      Interpreting Statistical Outputs, Coefficients, Confidence Intervals, and Effect Sizes

5.      Probability, Uncertainty, and Risk in Data-Driven Decisions

6.      Statistical Significance versus Practical and Strategic Significance

7.      Correlation versus Causation and the Role of Confounding Variables

8.      Sampling Bias, Selection Bias, Measurement Error, Missing Data, and Outliers

9.      Cognitive and Analytical Biases: Confirmation Bias, Anchoring, Availability Bias, and Framing Effects

10.  Case Study and Exercise: Critically Evaluating Conflicting Evidence Before Making a High-Impact Decision

Day 4: Advanced Decision Analysis, Risk, and Scenario Planning

Module 4: Advanced Decision Analysis, Risk, and Scenario Planning

1.      Structured Decision Analysis and Evaluation of Multiple Alternatives

2.      Decision Matrices, Weighted Criteria, Scoring Models, and Prioritisation

3.      Cost-Benefit Analysis, Resource Allocation, and Value-Based Decision Considerations

4.      Risk Identification, Risk Assessment, Risk Matrices, and Risk-Based Decision Making

5.      Scenario Analysis for Strategic and Operational Decision Planning

6.      Sensitivity Analysis, Assumption Testing, and Decision Robustness

7.      Root-Cause Analysis Using Five Whys, Fishbone Analysis, Pareto Analysis, and PDCA

8.      Integrating Quantitative, Qualitative, Financial, Customer, Operational, and External Evidence

9.      Advanced Case Study: Making Decisions Under Uncertainty, Constraints, and Conflicting Evidence

10.  Group Exercise: Developing, Defending, and Stress-Testing an Evidence-Based Decision Recommendation

Day 5: Strategic Data-Driven Decisions, Implementation, and Capstone

Module 5: Strategic Data-Driven Decisions, Implementation, and Capstone

1.      Strategic Decision Making with Integrated Organisational Evidence

2.      Developing Executive Decision Briefs, Evidence Packs, and Management Recommendations

3.      Communicating Data-Driven Decisions to Executives, Managers, Teams, and Stakeholders

4.      Presenting Evidence, Assumptions, Uncertainty, Risks, Trade-Offs, and Decision Limitations

5.      Translating Decisions into Action Plans, Responsibilities, Resources, and Implementation Milestones

6.      Developing KPIs, Monitoring Frameworks, Feedback Loops, and Decision Review Mechanisms

7.      Applying PDCA, Results-Based Management, Balanced Scorecard, and Continuous Improvement Principles

8.      Data Governance, Ethical Data Use, Accountability, Transparency, and Responsible Decision Making

9.      Capstone Exercise: End-to-End Data-Driven Decision Making from Problem Definition to Action and Monitoring

10.  Capstone Presentation, Peer Review, Decision Challenge, Lessons Learned, and Workplace Action Plan