Training course

Overview

Practical Data-Driven Decision Making is a hands-on professional training course designed to develop the practical skills required to transform everyday organisational data into clear insights, sound decisions, and measurable actions. The course focuses on the complete data-driven decision-making workflow, from defining a practical problem and identifying relevant evidence through data preparation, analysis, interpretation, option assessment, implementation, and performance monitoring. Participants work with realistic business, operational, financial, customer, workforce, project, programme, and performance scenarios to build confidence in applying evidence-based decision-making techniques in the workplace.

The course emphasises practical tools that professionals can immediately apply, including Microsoft Excel, sorting and filtering, formulas, pivot tables, charts, dashboards, KPI summaries, variance analysis, decision matrices, prioritisation tools, scenario analysis, sensitivity analysis, and structured decision templates. Participants learn how to convert raw information into useful management evidence, identify trends and performance gaps, compare alternatives, and develop concise decision-support outputs. The programme also introduces practical frameworks such as SMART objectives, PDCA, Balanced Scorecard, Results-Based Management, root-cause analysis, risk-based decision making, and evidence-to-action approaches.

Practical decision making also requires the ability to evaluate whether evidence is sufficiently reliable to support action. Participants therefore develop applied skills in descriptive statistics, trend analysis, variance analysis, correlation, basic regression interpretation, probability, confidence intervals, uncertainty, and forecasting concepts. The course addresses common data and analytical challenges including missing information, outliers, inconsistent records, measurement errors, selection bias, confounding, misleading visualisations, weak assumptions, and confusion between correlation and causation. Emphasis is placed on practical judgement, helping participants understand not only what the data shows, but also what it does not establish.

Through guided exercises, workplace case studies, practical datasets, group activities, decision simulations, and an integrated capstone, participants progressively apply the full data-driven decision-making process. The training connects data analysis with problem solving, risk assessment, resource allocation, performance improvement, stakeholder communication, implementation, and continuous learning. By the end of the five-day programme, participants will be able to analyse workplace evidence, identify meaningful insights, evaluate options, manage uncertainty, communicate decisions clearly, and implement practical actions supported by credible data and structured decision-making methods.

Course Duration

5 Days (40 Hours)

Target Participants

This course is suitable for:
• Professionals working with operational, business, financial, research, or performance data
• Data analysts and reporting professionals
• Business intelligence and management information professionals
• Researchers and research assistants
• Monitoring, Evaluation, Research and Learning (MERL/MEL) professionals
• Programme and project professionals
• Business performance and management reporting professionals
• Finance, accounting, audit, and risk professionals
• Marketing, sales, customer experience, and market research professionals
• Human resources and workforce professionals
• Operations and service-delivery professionals
• Supply chain, procurement, logistics, and resource planning professionals
• Policy, planning, development, NGO, government, and public-sector professionals
• Consultants, advisers, and professional services practitioners
• Supervisors and managers who interpret reports and performance information
• Professionals responsible for KPIs, dashboards, management information, and performance reviews
• Professionals involved in planning, budgeting, resource allocation, and operational improvement
• Professionals working with analysts, researchers, consultants, or external data providers
• Academics and postgraduate researchers working with applied organisational data
• Professionals seeking stronger practical evidence-based decision-making and analytical skills

Course Objectives

By the end of the training, participants will be able to:
• Explain the principles and practical value of data-driven decision making
• Distinguish between data, information, evidence, insights, judgement, decisions, and actions
• Define workplace problems, objectives, decision questions, and evidence requirements
• Identify relevant data sources and assess data accuracy, completeness, consistency, relevance, and timeliness
• Prepare and organise practical datasets for analysis and decision support
• Use Microsoft Excel for sorting, filtering, formulas, pivot tables, charts, summaries, and dashboards
• Analyse KPIs, targets, benchmarks, trends, variances, ratios, gaps, and exceptions
• Create practical tables, charts, dashboards, and evidence summaries for decision making
• Apply descriptive statistics and basic analytical techniques to workplace data
• Interpret correlation, basic regression outputs, probability, confidence intervals, and uncertainty
• Distinguish correlation from causation and recognise conclusions that require additional evidence
• Identify missing data, outliers, bias, measurement errors, inconsistent definitions, and other data-quality problems
• Apply structured decision-making tools including decision matrices, prioritisation methods, and weighted criteria
• Use scenario analysis, sensitivity analysis, and what-if techniques to assess possible outcomes
• Apply Five Whys, Fishbone analysis, Pareto analysis, and PDCA to investigate and address root causes
• Apply SMART, Balanced Scorecard, Results-Based Management, risk-based decision making, and evidence-to-action principles
• Integrate quantitative evidence with qualitative information, contextual knowledge, and professional judgement
• Assess assumptions, risks, uncertainty, constraints, and potential trade-offs before selecting an option
• Develop practical evidence-based recommendations and decision-support summaries
• Communicate analytical findings, options, risks, and decision rationale clearly to relevant stakeholders
• Design implementation, monitoring, feedback, and review processes for data-driven decisions
• Apply ethical, responsible, transparent, and appropriate practices when using organisational data
• Complete an end-to-end practical data-driven decision-making capstone

Course Content

Day 1: Practical Foundations of Data-Driven Decision Making

Module 1: Practical Foundations of Data-Driven Decision Making

1.      Understanding the Practical Data-Driven Decision-Making Process

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

3.      Defining Practical Workplace Problems and Decision Questions

4.      Establishing Objectives, Expected Results, Constraints, and Evidence Requirements

5.      Identifying Data Sources, Records, Reports, and Supporting Information

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

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

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

9.      Case Study: Turning a Workplace Performance Problem into an Evidence-Based Decision

10.  Practical Exercise: Building an End-to-End Decision-Making Workflow

Day 2: Practical Data Analysis, Visualisation, and Decision Support

Module 2: Practical Data Analysis, Visualisation, and Decision Support

1.      Microsoft Excel for Practical Data-Driven Decision Making

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

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

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

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

6.      Creating Practical Charts, Tables, Dashboards, and KPI Summaries

7.      Selecting Appropriate Visualisations for Different Decision Questions

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

9.      Scenario Analysis, Sensitivity Analysis, and What-If Decision Techniques

10.  Practical Exercise: Building a Data-Driven Decision-Support Dashboard

Day 3: Practical Statistical Interpretation and Evidence Evaluation

Module 3: Practical Statistical Interpretation and Evidence Evaluation

1.      Descriptive Statistics for Practical Workplace Decision Making

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

3.      Comparing Performance Across Teams, Periods, Locations, and Categories

4.      Understanding Relationships, Correlation, and Basic Regression Outputs

5.      Interpreting Probability, Confidence Intervals, and Uncertainty

6.      Correlation Versus Causation in Practical Decision Making

7.      Identifying Missing Data, Outliers, Bias, Measurement Error, and Inconsistent Records

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

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

10.  Case Study and Exercise: Evaluating the Strength and Limitations of Workplace Evidence

Day 4: Advanced Practical Decision Analysis and Problem Solving

Module 4: Advanced Practical Decision Analysis and Problem Solving

1.      Structured Decision Analysis, Alternatives, Criteria, and Trade-Offs

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

3.      Pareto Analysis for Prioritising Problems and Improvement Opportunities

4.      PDCA and Continuous Improvement for Data-Driven Problem Solving

5.      Risk-Based Decision Making and Practical Risk Assessment

6.      Resource Allocation, Prioritisation, and Cost-Benefit Considerations

7.      Scenario Planning, Sensitivity Analysis, and Decision Robustness

8.      Managing Uncertainty, Conflicting Evidence, and Incomplete Information

9.      Case Study: Solving a Recurring Operational Problem Using Data and Root-Cause Analysis

10.  Practical Exercise: Developing and Stress-Testing an Evidence-Based Action Plan

Day 5: Practical Decision Communication, Implementation, and Capstone

Module 5: Practical Decision Communication, Implementation, and Capstone

1.      Integrating Data, Evidence, Risk, and Professional Judgement

2.      Developing Practical Evidence-Based Recommendations

3.      Preparing Decision Briefs, Performance Summaries, Dashboards, and Action Reports

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

5.      Adapting Evidence Communication for Managers, Teams, Clients, and Stakeholders

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

7.      Establishing KPIs, Monitoring Indicators, Feedback Loops, and Follow-Up

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

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

10.  Capstone Exercise: Analyse Workplace Evidence, Evaluate Alternatives, Make a Data-Driven Decision, and Develop an Implementation and Monitoring Plan