Data Before Narrative

A framework for analyzing data objectively by letting the data tell its own story rather than forcing preconceived narratives onto it. Based on insights from a successful Silicon Valley executive.

Core Principle

• Look at data first and ask "What story is the data telling me?" rather than starting with a story and finding data to support it
• Remove personal biases and preconceptions before analyzing data
• Focus on understanding what the data actually reveals, not what you want it to reveal

Common Pitfalls to Avoid

• Starting with a predetermined narrative or story
• Cherry-picking data points that support existing beliefs
• Ignoring contradictory data that doesn't fit the narrative
• Making decisions before fully analyzing available information

Implementation Process

• Post the raw data first before any interpretation
• Look at the full dataset, not selective pieces
• Ask specific questions about what information is actually needed
• Identify gaps in information before drawing conclusions
• Be willing to change direction based on what the data shows

Benefits

• Leads to better decision making
• Removes personal biases from analysis
• Creates more accurate understanding of situations
• Prevents confirmation bias
• Helps identify true root causes of issues

Real World Application

• When investigating business performance issues:
  • Look at full data set first
  • Don't let team members present conclusions without showing underlying data
  • Question assumptions about cause and effect
  • Be willing to discover unexpected insights
• For monthly/quarterly reviews:
  • Start with raw numbers
  • Look for patterns in the data
  • Let insights emerge naturally from analysis

Key Mindset Shift

• Move from "proving what we believe" to "discovering what is true"
• Accept that data may contradict initial assumptions
• Stay open to unexpected insights and conclusions
• Focus on learning rather than confirming