Training course

Overview

Data-Driven Decision Making for Professionals is a practical professional training course designed to strengthen the ability of workplace professionals to use reliable data, analytical evidence, and structured reasoning when making operational, tactical, and strategic decisions. The course focuses on the complete decision-making process, from identifying a business or organisational problem and defining the information required to evaluating evidence, comparing alternatives, managing uncertainty, and translating decisions into measurable actions. Participants develop practical analytical judgement that can be applied across business, finance, operations, projects, programmes, customer service, human resources, research, performance management, and public-sector environments.

The course provides hands-on techniques for working with common workplace data using Microsoft Excel, formulas, sorting and filtering, pivot tables, charts, dashboards, KPIs, performance reports, decision matrices, prioritisation tools, scenario analysis, and structured decision templates. Participants learn how to convert operational and management information into meaningful insights by analysing trends, variances, benchmarks, ratios, performance gaps, exceptions, and emerging issues. Practical frameworks including SMART objectives, PDCA, Balanced Scorecard, Results-Based Management, root-cause analysis, risk-based decision making, and evidence-to-action approaches are incorporated to help professionals connect analytical findings with workplace objectives and measurable results.

Strong emphasis is placed on evidence quality and professional analytical judgement. Participants learn how to assess the accuracy, completeness, consistency, relevance, timeliness, and credibility of data before using it to support important decisions. The course introduces descriptive statistics, correlation, regression, probability, confidence intervals, uncertainty, and basic forecasting concepts while explaining how to interpret analytical outputs appropriately. Participants also examine common decision-making challenges including confirmation bias, anchoring, availability bias, selection bias, missing data, outliers, measurement errors, confounding variables, misleading visualisations, and unsupported assumptions.

Through workplace-oriented case studies, practical exercises, group activities, realistic scenarios, and an applied capstone, participants progressively develop the ability to make transparent and defensible evidence-based decisions. The final application requires participants to define a professional decision problem, identify and analyse relevant evidence, compare practical alternatives, assess risks and uncertainty, communicate the decision rationale, and establish appropriate implementation and monitoring measures. By integrating data analysis, critical thinking, decision frameworks, risk assessment, performance monitoring, and professional communication, the course equips participants to make more consistent, informed, and accountable decisions in their day-to-day roles.

Course Duration

5 Days (40 Hours)

Target Participants

This course is suitable for:
• Professionals working with business, operational, financial, research, or performance data
• Data analysts, reporting officers, and business intelligence professionals
• Researchers, research assistants, and analytical professionals
• Monitoring, Evaluation, Research and Learning (MERL/MEL) professionals
• Programme, project, and operations 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
• Supply chain, procurement, logistics, and operations professionals
• Policy, planning, and development professionals
• NGO, government, development, and public-sector professionals
• Consultants, advisers, and professional service providers
• Supervisors and managers who regularly interpret reports and performance information
• Professionals responsible for KPIs, dashboards, management information, and performance reviews
• Professionals supporting planning, budgeting, resource allocation, and operational improvement
• Professionals working with researchers, analysts, consultants, or external data providers
• Academics and postgraduate researchers working with applied organisational data
• Professionals seeking stronger 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 in professional environments
• Distinguish between data, information, evidence, insight, judgement, decisions, and actions
• Define workplace decision problems, objectives, constraints, stakeholders, and decision criteria
• Translate professional and organisational challenges into clear analytical questions
• Identify appropriate data sources and assess their relevance, reliability, quality, and limitations
• Use Microsoft Excel and practical analytical tools to organise and analyse workplace data
• Apply sorting, filtering, formulas, pivot tables, charts, dashboards, and summary tables for decision support
• Analyse KPIs, targets, benchmarks, ratios, trends, variances, exceptions, and performance gaps
• Apply descriptive statistics and basic correlation, regression, probability, and uncertainty concepts
• Interpret statistical and analytical outputs without overstating what the evidence demonstrates
• Distinguish correlation from causation and identify unsupported conclusions
• Recognise confirmation bias, anchoring, availability bias, selection bias, measurement error, missing data, and outliers
• Apply structured decision-making frameworks to evaluate alternatives and practical courses of action
• Use decision matrices, weighted criteria, prioritisation, risk analysis, scenario analysis, and sensitivity analysis
• Apply Five Whys, Fishbone Analysis, Pareto Analysis, PDCA, and other root-cause and improvement techniques
• Integrate quantitative, qualitative, financial, operational, customer, workforce, and external evidence where appropriate
• Evaluate assumptions, uncertainty, risks, trade-offs, constraints, and potential unintended consequences
• Develop professional dashboards, evidence summaries, decision briefs, and management recommendations
• Communicate analytical findings and decision rationales clearly to colleagues, managers, clients, and stakeholders
• Establish practical implementation, monitoring, feedback, and review mechanisms for workplace decisions
• Apply ethical, transparent, responsible, and accountable principles when using data for professional decisions
• Develop and present an end-to-end evidence-based workplace decision through an applied capstone exercise

Course Content

Day 1: Foundations of Professional Data-Driven Decision Making

Module 1: Foundations of Professional Data-Driven Decision Making

1.      Understanding Data-Driven Decision Making in Professional Workplaces

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

3.      The Professional Data-Driven Decision-Making Lifecycle

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

5.      Translating Professional Challenges 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.      Professional Decision Frameworks: SMART Objectives, PDCA, Balanced Scorecard, Results-Based Management, and Evidence-to-Action

10.  Case Study and Exercise: Defining a Workplace Decision Problem and Developing an Evidence Requirements Plan

Day 2: Practical Data Analysis and Professional Decision Support

Module 2: Practical Data Analysis and Professional Decision Support

1.      Preparing and Structuring Workplace Data for Decision Analysis

2.      Microsoft Excel Techniques for Professional Data-Driven Decision Making

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

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

5.      Developing Charts and Visualisations for Workplace Decision Analysis

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

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

8.      Designing Professional Dashboards, Scorecards, and Management Information Displays

9.      Building Evidence Summaries and Decision Packs for Professional Use

10.  Case Study and Exercise: Analysing Workplace Performance Data and Developing Evidence-Based Decision Options

Day 3: Analytical Evidence, Statistics, and Professional Judgement

Module 3: Analytical Evidence, Statistics, and Professional Judgement

1.      Descriptive Statistics for Professional Decision Making

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

3.      Correlation and Regression for Understanding Workplace Relationships

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

5.      Probability, Uncertainty, and Risk in Professional Decisions

6.      Statistical Significance versus Practical and Workplace Significance

7.      Correlation versus Causation and the Role of Confounding Factors

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

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

10.  Case Study and Exercise: Evaluating Conflicting Workplace Evidence Before Making a Professional Decision

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

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

1.      Structured Decision Analysis and Evaluation of Multiple Workplace Alternatives

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

3.      Cost-Benefit Thinking, Resource Allocation, Opportunity Costs, and Professional Trade-Offs

4.      Identifying, Assessing, and Managing Operational and Professional Decision Risks

5.      Scenario Analysis for Workplace Planning and Alternative Decision Outcomes

6.      Sensitivity Analysis, Assumption Testing, and Decision Robustness

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

8.      Integrating Financial, Operational, Customer, Workforce, Research, and External Evidence

9.      Advanced Case Study: Making a Professional Decision Under Uncertainty, Resource Constraints, and Conflicting Evidence

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

Day 5: Professional Decision Communication, Implementation, and Capstone

Module 5: Professional Decision Communication, Implementation, and Capstone

1.      Integrating Evidence for Professional and Strategic Workplace Decisions

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

3.      Communicating Data-Driven Decisions to Managers, Teams, Clients, 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 Indicators, 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 Professional Decision Making

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

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