Data-Driven Decision Making

2.

The DDDM Framework and Process

2.1.

Problem Definition and Objective Setting

2.1.1.

Identifying Business Challenges

2.1.2.

Setting Clear Objectives

2.1.3.

Asking the Right Questions

2.1.3.1.

Framing Business Questions

2.1.3.2.

Clarifying Scope and Constraints

2.1.3.3.

Question Prioritization

2.1.4.

Formulating Testable Hypotheses

2.1.4.1.

Hypothesis Development

2.1.4.2.

Null and Alternative Hypotheses

2.1.4.3.

Hypothesis Validation Framework

2.1.5.

Translating Business Needs into Data Questions

2.1.5.1.

Operationalizing Business Goals

2.1.5.2.

Mapping Objectives to Data Requirements

2.1.5.3.

Success Criteria Definition

2.2.

Data Acquisition and Collection

2.2.1.

Identifying Data Sources

2.2.1.1.

Internal Data Sources

2.2.1.1.1.

Transactional Data

2.2.1.1.2.

Operational Data

2.2.1.1.3.

2.2.1.1.4.

2.2.1.1.5.

Human Resources Data

2.2.1.2.

External Data Sources

2.2.1.2.1.

Public Datasets

2.2.1.2.2.

Market Research Data

2.2.1.2.3.

Industry Reports

2.2.1.2.4.

Government Data

2.2.1.3.

Third-Party Data Providers

2.2.1.3.1.

Commercial Data Vendors

2.2.1.3.2.

Data Marketplaces

2.2.2.

Data Collection Methods

2.2.2.1.

Surveys and Forms

2.2.2.1.1.

Survey Design Principles

2.2.2.1.2.

Sampling Methods

2.2.2.1.3.

Response Rate Optimization

2.2.2.2.

2.2.2.2.1.

Legal and Ethical Considerations

2.2.2.2.2.

Technical Implementation

2.2.2.3.

2.2.2.3.1.

2.2.2.3.2.

Data Integration via APIs

2.2.2.3.3.

API Rate Limiting

2.2.2.4.

Sensor Data and IoT

2.2.2.4.1.

Types of Sensors

2.2.2.4.2.

2.2.2.4.3.

Real-Time Data Collection

2.2.2.5.

Transactional Systems

2.2.2.5.1.

Point-of-Sale Systems

2.2.2.5.2.

Enterprise Resource Planning Systems

2.2.2.5.3.

Customer Relationship Management Systems

2.2.3.

Data Collection Challenges

2.2.3.1.

Data Accessibility

2.2.3.2.

Data Quality at Source

2.2.3.3.

Data Volume and Velocity

2.2.3.4.

Cost Considerations

2.3.

Data Management and Preparation

2.3.1.

Data Storage Solutions

2.3.1.1.

Relational Databases

2.3.1.2.

NoSQL Databases

2.3.1.3.

2.3.1.4.

Data Warehouses

2.3.1.5.

2.3.1.6.

Cloud Storage Options

2.3.2.

Data Quality Assessment

2.3.2.1.

2.3.2.2.

Quality Dimensions

2.3.2.2.1.

2.3.2.2.2.

2.3.2.2.3.

2.3.2.2.4.

2.3.2.2.5.

2.3.3.

Data Cleaning and Preprocessing

2.3.3.1.

Missing Data Handling

2.3.3.1.1.

Imputation Techniques

2.3.3.1.2.

Deletion Methods

2.3.3.1.3.

Missing Data Patterns

2.3.3.2.

Outlier Detection and Treatment

2.3.3.2.1.

Statistical Methods

2.3.3.2.2.

Machine Learning Approaches

2.3.3.2.3.

Domain-Specific Rules

2.3.3.3.

Data Standardization

2.3.3.3.1.

Format Standardization

2.3.3.3.2.

Unit Conversion

2.3.3.3.3.

Categorical Data Encoding

2.3.3.4.

Duplicate Removal

2.3.3.4.1.

Exact Duplicates

2.3.3.4.2.

2.3.3.4.3.

2.3.4.

Data Transformation

2.3.4.1.

2.3.4.2.

2.3.4.3.

Feature Engineering

2.3.4.3.1.

Creating New Features

2.3.4.3.2.

Feature Selection

2.3.4.3.3.

Dimensionality Reduction

2.3.4.4.

Data Type Conversion

2.3.5.

Data Integration

2.3.5.1.

Data Merging Strategies

2.3.5.2.

Schema Matching

2.3.5.3.

Data Lineage Tracking

2.3.5.4.

Conflict Resolution

2.4.

Exploratory Data Analysis

2.4.1.

EDA Objectives and Approach

2.4.2.

Descriptive Statistics

2.4.2.1.

Measures of Central Tendency

2.4.2.1.1.

2.4.2.1.2.

2.4.2.1.3.

2.4.2.2.

Measures of Dispersion

2.4.2.2.1.

2.4.2.2.2.

Standard Deviation

2.4.2.2.3.

2.4.2.2.4.

Interquartile Range

2.4.2.3.

Distribution Shape

2.4.2.3.1.

2.4.2.3.2.

2.4.3.

Data Distribution Analysis

2.4.3.1.

Frequency Distributions

2.4.3.2.

Probability Distributions

2.4.3.3.

Distribution Testing

2.4.4.

Relationship Analysis

2.4.4.1.

Correlation Analysis

2.4.4.1.1.

Pearson Correlation

2.4.4.1.2.

Spearman Correlation

2.4.4.1.3.

2.4.4.2.

Cross-Tabulation

2.4.4.3.

Contingency Tables

2.4.5.

Pattern Recognition

2.4.5.1.

2.4.5.2.

Seasonality Detection

2.4.5.3.

Anomaly Identification

2.4.6.

Data Visualization for EDA

2.4.6.1.

Univariate Visualizations

2.4.6.1.1.

2.4.6.1.2.

2.4.6.1.3.

2.4.6.2.

Bivariate Visualizations

2.4.6.2.1.

2.4.6.2.2.

2.4.6.2.3.

2.4.6.3.

Multivariate Visualizations

2.4.6.3.1.

2.4.6.3.2.

Parallel Coordinates

2.5.

Data Analysis and Modeling

2.5.1.

Analytical Approach Selection

2.5.1.1.

Descriptive Analytics

2.5.1.2.

Diagnostic Analytics

2.5.1.3.

Predictive Analytics

2.5.1.4.

Prescriptive Analytics

2.5.2.

Statistical Analysis Methods

2.5.2.1.

Hypothesis Testing

2.5.2.1.1.

2.5.2.1.2.

Chi-Square Tests

2.5.2.1.3.

2.5.2.1.4.

Non-Parametric Tests

2.5.2.2.

Regression Analysis

2.5.2.2.1.

Simple Linear Regression

2.5.2.2.2.

Multiple Regression

2.5.2.2.3.

Logistic Regression

2.5.2.2.4.

Polynomial Regression

2.5.2.3.

Time Series Analysis

2.5.2.3.1.

2.5.2.3.2.

Seasonal Decomposition

2.5.2.3.3.

Forecasting Models

2.5.3.

Machine Learning Applications

2.5.3.1.

Supervised Learning

2.5.3.1.1.

Classification Problems

2.5.3.1.2.

Regression Problems

2.5.3.2.

Unsupervised Learning

2.5.3.2.1.

Clustering Analysis

2.5.3.2.2.

Association Rules

2.5.3.3.

Model Selection and Evaluation

2.5.3.3.1.

Cross-Validation

2.5.3.3.2.

Performance Metrics

2.5.3.3.3.

Model Comparison

2.5.4.

Advanced Analytics

2.5.4.1.

Predictive Modeling

2.5.4.2.

Optimization Techniques

2.5.4.3.

Simulation Methods

2.5.4.4.

Scenario Analysis

2.6.

Interpretation and Insight Generation

2.6.1.

Results Interpretation

2.6.1.1.

Statistical Significance

2.6.1.2.

Practical Significance

2.6.1.3.

Effect Size Analysis

2.6.2.

Business Context Integration

2.6.2.1.

Relating Findings to Objectives

2.6.2.2.

Industry Benchmarking

2.6.2.3.

Competitive Analysis

2.6.3.

Causal Analysis

2.6.3.1.

Correlation vs. Causation

2.6.3.2.

Causal Inference Methods

2.6.3.3.

Confounding Variables

2.6.4.

Uncertainty Quantification

2.6.4.1.

Confidence Intervals

2.6.4.2.

Prediction Intervals

2.6.4.3.

Sensitivity Analysis

2.6.5.

Insight Validation

2.6.5.1.

Reproducibility Testing

2.6.5.2.

Peer Review Process

2.6.5.3.

Stakeholder Validation

2.7.

Communication and Visualization

2.7.1.

Data Storytelling Principles

2.7.1.1.

Narrative Structure

2.7.1.2.

Audience Analysis

2.7.1.3.

Key Message Identification

2.7.2.

Visualization Design

2.7.2.1.

Chart Selection Guidelines

2.7.2.2.

Visual Hierarchy

2.7.2.3.

2.7.2.4.

Accessibility Considerations

2.7.3.

Dashboard Development

2.7.3.1.

Dashboard Design Principles

2.7.3.2.

Interactive Elements

2.7.3.3.

Real-Time Updates

2.7.3.4.

Mobile Responsiveness

2.7.4.

Presentation Techniques

2.7.4.1.

Executive Summaries

2.7.4.2.

Technical Reports

2.7.4.3.

Stakeholder Presentations

2.7.4.4.

2.8.

Implementation and Decision Making

2.8.1.

Decision Framework Application

2.8.1.1.

Multi-Criteria Decision Analysis

2.8.1.2.

Risk Assessment

2.8.1.3.

Cost-Benefit Analysis

2.8.2.

Solution Deployment

2.8.2.1.

Model Implementation

2.8.2.2.

System Integration

2.8.2.3.

2.8.3.

Change Management

2.8.3.1.

Stakeholder Buy-In

2.8.3.2.

Process Redesign

2.8.3.3.

Communication Strategy

2.9.

Monitoring and Continuous Improvement

2.9.1.

Performance Tracking

2.9.1.1.

2.9.1.2.

Outcome Measurement

2.9.1.3.

Success Metrics

2.9.2.

Model Maintenance

2.9.2.1.

Model Drift Detection

2.9.2.2.

Retraining Schedules

2.9.2.3.

Performance Degradation

2.9.3.

Feedback Integration

2.9.3.1.

User Feedback Collection

2.9.3.2.

Process Refinement

2.9.3.3.

Lesson Learned Documentation

2.9.4.

Experimentation Framework

2.9.4.1.

A/B Testing Design

2.9.4.2.

Experimental Controls

2.9.4.3.

Statistical Power Analysis

2.9.4.4.

Results Interpretation

Previous

1. Introduction to Data-Driven Decision Making

Go to top

Next

3. Statistical and Analytical Foundations