Optimizing Business Decisions Through Data Analysis

Introduction

All professionals aim to make informed, high-quality decisions. This is typically achieved through a careful evaluation of relevant information, which often comes from the analysis of statistical data. However, only a small number of professionals possess the quantitative reasoning skills needed to properly interpret these statistical findings or to critically evaluate the interpretations provided by others.

A lack of proficiency in quantitative analysis can impede a professional’s decision-making ability, ultimately affecting efficiency in business contexts.

The “Business Decision-Making Using Data Analysis” program was created to enhance professionals' understanding of the importance of quantitative methods in management decision-making. By participating in this initiative, they will acquire significant skills that enable them to make better choices, using relevant knowledge to support their judgments.

Objectives

At the end of the Business Decisions Using Data Analysis course, trainees should be capable of:

• Appreciating Data Analysis’s significance as a crucial decision-making tool.
• Explaining the scope, boundaries, and structures of Statistics.
• Understanding why data quality matters during the analysis process.
• Selecting the appropriate method under different conditions, such as management levels.
• Effectively using various tools for Data Analysis.
• Interpreting statistical outputs to guide good decision-making.
• Confidently evaluating and critiquing statistical findings.
• Engaging meaningfully with analysts concerning data.
• Embarking on Data Analysis projects with confidence.
• Supporting strategic initiatives with appropriate techniques.

Training Methodology

1. Interactive lectures on basic concepts of data analysis
2. case studies
3. Group discussions and peer-to-peer learning sessions
4. Simulated scenarios
5. project work
6. Quizzes and assessments to reinforce key concepts
7. Individual and group presentations on findings and strategies

Course Outline

Unit 1: The Scene is Set and Decisions Need Observing

• Framing Quantitative Methods
• Business Practice: Applications of Statistical Thinking
• Components of Quantitative Management
• Data, Data Quality, and Their Importance

Unit 2: Using Excel to Present Your Data Visually

• Tables & Graphs for Summarizing Data
• Single-way, Two-way, and N-way Pivot Tables
• Graphics for Display Purposes and Breakdown Analysis
• Numeric Summaries
• Mean, Median, Mode, and Other Measures: Variability, Skewness, Kurtosis, Shape, Symmetry, Normality, Dependence
• Box Plot as a Graphical Summary Technique

Unit 3: Statistical (Inferential) Decision Making - Harnessing Uncertainty

• Inference Using Sample Information in Management Situations
• Uncertainty Measurement
• The Role of Sampling
• Statistical Decision-Making Techniques: Confidence Interval and Hypothesis Testing
• Techniques: Z-Scores, T-Scores, ANOVA, Chi-squared
• Solving Real Management Issues: Estimation Differences, Multiple Sample Comparisons

Unit 4: Predictive Decision Making - Using Models to Build Relationships

• Exploiting Statistical Relationships Between Variables for Forecasting
• The Value of Statistical Modelling
• Modelling Approaches: Autoregressive Models, Time Series Analysis, Regression

Unit 5: Data Mining – A Preview

• Data Mining Overview
• The Data Mining Process: Data Preparation
• Functions of Data Mining: Prediction, Estimation, Classification, Descriptive
• Methodology and Interpretation: Likely Applications
• Techniques: Cluster Analysis, Discriminant Analysis, Logistic Regression, Classification Trees, Neural Networks
• Applications: Market Basket Analysis, Customer Relationship Management
• Overview of Selected Data Mining Techniques (NCSS Analysis)
• Segmentation Strategies: Descriptive Modelling Approaches
• Classification, Estimation, and Prediction Strategies: Predictive Modelling
• Typical Applications

Unit 6: Decision Analysis for Management Judgement

• Decision Models for Structuring and Evaluating Complex Scenarios
• Multi-Criteria Decision Modelling: Illustrations from Practical Tools
• Simple Multi-Attribute Rating Technique (SMART)
• Analytical Hierarchy Process (AHP)