Training course

Overview

Data-Driven Decision Making for Managers is a practical professional training course designed to strengthen managers’ ability to use reliable data, analytical evidence, performance information, and structured decision-making frameworks to improve organisational outcomes. The course develops the skills required to translate business, operational, financial, customer, workforce, programme, and performance data into clear insights and sound management decisions. Participants learn how to define decision problems, identify relevant evidence, evaluate data quality, interpret analytical outputs, assess alternatives, manage uncertainty, and communicate decisions effectively within their organisational context.

The course provides practical techniques for analysing management information using tools such as Microsoft Excel, pivot tables, formulas, charts, dashboards, KPI frameworks, variance analysis, decision matrices, prioritisation tools, scenario analysis, and sensitivity analysis. Participants explore widely used management and decision frameworks, including SMART objectives, the Balanced Scorecard, Results-Based Management, PDCA, root-cause analysis, risk-based decision making, evidence-to-action approaches, and structured problem-solving methods. Practical exercises and workplace scenarios enable managers to connect analytical techniques with budgeting, resource allocation, operational performance, customer experience, project management, workforce planning, service delivery, and strategic implementation.

Strong managerial decision making requires more than access to large amounts of data. This course therefore develops critical skills for evaluating evidence quality, identifying bias and misleading information, understanding trends and variances, interpreting descriptive statistics, correlation, regression, probability, confidence intervals, uncertainty, and forecasting outputs, and distinguishing correlation from causation. Managers examine common analytical risks such as incomplete data, missing values, outliers, selection bias, measurement error, confounding variables, inconsistent definitions, unsupported assumptions, and misleading visualisations. The course emphasises practical judgement, ensuring that managers can distinguish statistically significant findings from results that are operationally or strategically meaningful.

Through case studies, group exercises, management simulations, practical data-analysis activities, decision scenarios, and an applied capstone, participants progressively develop an end-to-end managerial decision-making approach. The training covers the complete cycle from problem definition and evidence gathering through analysis, alternative evaluation, risk assessment, decision communication, implementation, monitoring, and review. By the end of the five-day programme, managers will be better equipped to lead evidence-based discussions, challenge assumptions, prioritise actions, evaluate trade-offs, strengthen performance management, and make transparent decisions that are supported by credible and relevant evidence.

Course Duration

5 Days (40 Hours)

Target Participants

This course is suitable for:
• Managers, department heads, and organisational leaders
• Senior and middle managers responsible for operational or strategic decisions
• Programme and project managers
• Operations and service-delivery managers
• Finance, accounting, audit, risk, and management reporting managers
• Business performance, business intelligence, and reporting managers
• Marketing, sales, customer experience, and market research managers
• Human resources and workforce management professionals
• Strategy, planning, transformation, and organisational development managers
• Monitoring, Evaluation, Research and Learning (MERL/MEL) managers
• Supply chain, procurement, logistics, and resource planning managers
• Policy, planning, development, NGO, government, and public-sector managers
• Managers responsible for KPIs, dashboards, performance reviews, and management information
• Managers supervising analysts, researchers, consultants, or data teams
• Business owners and operational leaders seeking stronger evidence-based decision-making skills
• Professionals involved in budgeting, resource allocation, performance improvement, and risk management
• Consultants, advisers, and professional services managers
• Managers responsible for strategic planning and organisational performance
• Aspiring managers developing analytical and decision-making capabilities

Course Objectives

By the end of the training, participants will be able to:
• Explain the principles, value, and management applications of data-driven decision making
• Distinguish between data, information, evidence, insights, judgement, decisions, and actions
• Translate organisational problems and management priorities into structured decision questions
• Identify appropriate data sources and assess data accuracy, completeness, consistency, relevance, timeliness, and credibility
• Use Microsoft Excel and practical analytical tools to organise, analyse, summarise, and present management information
• Apply sorting, filtering, formulas, pivot tables, charts, dashboards, and summary techniques to managerial datasets
• Interpret KPIs, targets, benchmarks, ratios, trends, variances, gaps, exceptions, and performance indicators
• Apply descriptive statistics, correlation, regression, probability, confidence intervals, uncertainty, and basic forecasting concepts
• Distinguish correlation from causation and identify analytical conclusions that require additional evidence
• Recognise selection bias, missing data, outliers, measurement error, confounding, inconsistent definitions, and other evidence-quality problems
• Apply structured decision-making frameworks to define problems, identify alternatives, establish criteria, and assess trade-offs
• Use decision matrices, weighted criteria, prioritisation techniques, cost-benefit considerations, risk analysis, scenario analysis, and sensitivity analysis
• Apply root-cause analysis and structured problem-solving methods to investigate performance gaps and operational problems
• Integrate quantitative evidence with qualitative information, professional judgement, stakeholder knowledge, and contextual factors
• Evaluate assumptions, uncertainty, risks, constraints, and potential unintended consequences before making management decisions
• Develop management dashboards, decision summaries, and evidence-based recommendations
• Apply Balanced Scorecard, Results-Based Management, SMART, PDCA, risk-based decision making, and evidence-to-action principles appropriately
• Communicate analytical findings and decision rationale clearly to executives, teams, stakeholders, and other decision-makers
• Develop implementation, monitoring, feedback, and review mechanisms for data-driven decisions
• Apply ethical, responsible, transparent, and accountable approaches to the use of organisational data
• Complete an applied managerial decision-making exercise integrating analysis, evidence evaluation, decision selection, implementation, and monitoring

Course Content

Day 1: Foundations of Data-Driven Decision Making

Module 1: Foundations of Data-Driven Decision Making

1.      Understanding Data-Driven Decision Making in Management

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

3.      Defining Management Problems, Objectives, and Decision Questions

4.      Identifying Stakeholders, Constraints, Assumptions, and Decision Requirements

5.      Identifying Data Sources, Management Information, and Evidence Requirements

6.      Assessing Data Quality, Relevance, Completeness, Accuracy, and Timeliness

7.      Establishing KPIs, SMART Objectives, Targets, Benchmarks, and Performance Measures

8.      Management Decision-Making Frameworks and Evidence-to-Action Principles

9.      Case Study: Diagnosing a Management Problem Using Organisational Evidence

10.  Practical Exercise: Building a Structured Data-Driven Decision Framework

Day 2: Practical Data Analysis and Managerial Decision Support

Module 2: Practical Data Analysis and Managerial Decision Support

1.      Microsoft Excel for Managerial Data Analysis and Decision Support

2.      Data Preparation, Sorting, Filtering, Validation, and Structured Management Tables

3.      Excel Formulas, Functions, Conditional Formatting, and Management Calculations

4.      Pivot Tables, Pivot Charts, Summaries, and Management Reporting

5.      Analysing Trends, Variances, Ratios, Growth Rates, and Performance Gaps

6.      Building Practical Management Dashboards and KPI Scorecards

7.      Selecting Effective Tables, Charts, and Visualisations for Management Decisions

8.      Decision Matrices, Prioritisation Tools, and Weighted Evaluation Criteria

9.      Scenario Analysis, Sensitivity Analysis, and Management What-If Modelling

10.  Practical Exercise: Developing a Managerial Decision-Support Dashboard

Day 3: Analytical Evidence, Statistics, and Managerial Judgement

Module 3: Analytical Evidence, Statistics, and Managerial Judgement

1.      Descriptive Statistics for Managerial Decision Making

2.      Interpreting Distributions, Averages, Percentages, Rates, and Variation

3.      Correlation and Regression for Understanding Business and Operational Relationships

4.      Interpreting Statistical Significance, Confidence Intervals, and Uncertainty

5.      Practical Significance, Operational Significance, and Strategic Significance

6.      Correlation Versus Causation in Management Decisions

7.      Identifying Bias, Missing Data, Outliers, Confounding, and Measurement Problems

8.      Evaluating Forecasts, Analytical Reports, Models, Dashboards, and Research Findings

9.      Triangulating Quantitative Data, Qualitative Evidence, and Professional Judgement

10.  Case Study and Exercise: Evaluating Conflicting Evidence Before a Management Decision

Day 4: Advanced Decision Analysis, Risk, and Problem Solving

Module 4: Advanced Decision Analysis, Risk, and Problem Solving

1.      Advanced Decision Structuring, Alternatives, Criteria, and Trade-Off Analysis

2.      Root-Cause Analysis Using Five Whys, Fishbone Diagrams, and Pareto Analysis

3.      Risk-Based Decision Making and Management Risk Assessment

4.      Scenario Planning, Sensitivity Analysis, and Decision Robustness

5.      Cost-Benefit Thinking, Resource Allocation, and Investment Prioritisation

6.      Managing Uncertainty, Conflicting Indicators, and Incomplete Evidence

7.      Cognitive Biases, Assumptions, Groupthink, and Analytical Decision Risks

8.      Applying Balanced Scorecard, Results-Based Management, PDCA, and Evidence-to-Action Frameworks

9.      Case Study: Selecting and Stress-Testing Management Options Under Uncertainty

10.  Practical Exercise: Developing an Advanced Decision Analysis and Risk Assessment

Day 5: Strategic Decision Intelligence, Implementation, and Capstone

Module 5: Strategic Decision Intelligence, Implementation, and Capstone

1.      Integrating Data, Evidence, Strategy, Risk, and Managerial Judgement

2.      Developing Evidence-Based Management Recommendations and Decision Briefs

3.      Communicating Data Insights, Options, Risks, and Decision Rationale

4.      Presenting Analytical Findings to Executives, Teams, and Key Stakeholders

5.      Designing Implementation Plans, Responsibilities, Resources, and Timelines

6.      Establishing Decision KPIs, Monitoring Systems, Feedback Loops, and Review Mechanisms

7.      Evaluating Decision Outcomes, Learning, Accountability, and Continuous Improvement

8.      Ethical, Responsible, Transparent, and Governed Use of Management Data

9.      Integrated Case Study: End-to-End Data-Driven Management Decision Simulation

10.  Capstone Exercise: Analyse Evidence, Evaluate Alternatives, Make and Communicate a Data-Driven Management Decision