Data Analysis Process Overview

General Steps in Data Analysis

• The data analysis process is consistent across different types of analyses.
• Illustrated using the example of an employee engagement survey.

Step 1: Asking Questions

• Purpose: Understand the needs of leaders and stakeholders.
• Key Questions:
  • What problem are we solving?
  • What is the purpose of the analysis?
  • What do we hope to learn?

Step 2: Preparing Data

• Determine Data Needs: Identify type of data required (quantitative, qualitative, cross-sectional, longitudinal).
• Data Collection:
  • Determine if new data collection is necessary or if existing data can be used.
  • For surveys, use both quantitative and qualitative questions.

Step 3: Processing Data

• Data Cleaning: Initial analysis to understand data structure and quality.
  • Run quality assurance checks.
  • Address missing data and outliers.
  • Code data properly.
• Importance: Critical for appropriate and responsible data analysis.

Step 4: Analyzing Data

• Objective and unbiased analysis is essential.
• Follow a pre-planned analysis process based on initial questions.
• Analysts must allow data to "speak for itself" without preconceived biases.
• Analysts as "storytellers" conveying data insights objectively.

Step 5: Sharing Insights

• Share high-level findings with stakeholders (e.g., executive team).
• Provide a comprehensive view of organizational sentiment.
• Ensure no surprises when stakeholders examine deeper insights.

Step 6: Taking Action

• Critical Step: Acting on results is essential.
• Use insights for interventions at both organizational and team levels.
• Address areas of improvement and build on strengths.

Importance of Following the Entire Process

• Skipping steps can result in missed insights.
• The process is rigorous and lengthy but essential for impactful analysis.

Personal Reflection

• The speaker expresses a deep passion for data and the insights it provides.