Start With The Why (Part 2): User Story First, Data Second

In Part 1, we explored how Karl Popper's insight about the need for guided observation resonates deeply with modern analytics. Just as scientists cannot make meaningful observations without a theoretical foundation, data analysts cannot extract value from data without a clear framework to guide their analysis. But how do we provide this structure without getting lost in academic formality?

Enter user stories - elegant hacks for this very challenge. They compress theory, research question, and analysis plan into perhaps two sentences, enabling us to deliver meaningful observations quickly and efficiently. Let me explain.

Making Analytics Work: Lessons from Research Design

To understand why user stories work so well, let's look at how researchers tackle a similar challenge: turning abstract questions into concrete findings. While the goals might differ, their systematic approach holds valuable lessons for business intelligence.

In my seven years of consulting and training on research design, methodology, and statistics, I've seen that good research - and by extension, good analysis - requires several key elements working together:

  1. A theoretical framework that provides context and understanding. Think "customer satisfaction depends on service quality" - a basic model of how things work.
  2. A research question (or several related questions) derived from this theory that guide our investigation. For example, "How does service response time affect customer satisfaction?"
  3. Specific hypotheses that narrow down these questions into testable predictions, like "Customers who wait longer than 24 hours for a response give lower satisfaction ratings."
  4. An analysis plan that outlines exactly how to test these hypotheses - "Compare satisfaction scores across different response time brackets using our customer feedback data."

And this is where user stories shine: they naturally encode all these elements, just in a much more streamlined way.

User Stories: Elegant Efficiency

To see exactly how this works, consider this detailed user story as we would write them at The Data School:

"As a customer service manager looking to improve team performance, I need to set appropriate response time targets for different types of customer inquiries. I can discover how response times affect customer satisfaction by comparing satisfaction ratings across different response time intervals and inquiry types."

This user story has all of the 4 components that make up an efficient user story:

  • Role: A customer service manager with the goal of improving team performance
  • Decision: Setting appropriate response time targets for different inquiry types
  • Insight: Understanding how response times affect customer satisfaction
  • Interaction: Comparing satisfaction ratings across response time intervals and inquiry types

Now, here's what makes this structure so powerful: By making sure we include those four components, we also captures all four key elements of good research design:

  1. The theoretical framework is built into the user story structure: We have a customer service manager who needs to make decisions about response times because they believe these affect satisfaction - this already establishes the theoretical relationship between service speed and customer satisfaction
  2. The research question emerges from the decision to be made: how should we set response time targets to optimize satisfaction?
  3. The underlying hypothesis is embedded in the insight we seek: different response times and inquiry types likely lead to different satisfaction levels
  4. The analysis plan is clearly outlined in the interaction: we'll analyze satisfaction ratings across response time intervals, segmented by inquiry type

In just two sentences, we've established a complete framework for a meaningful analysis. This is why user stories are ingenious hacks for good data analytics: They ensure rigorous and useful analysis by clarifying why we are looking at the data, what we are looking for, and how to derive the insight we need to enable more efficient business decisions. User stories keep us on the path of retaining the rigor of good research while speaking the language of business.

The Heart of Analytics: Investigation with Purpose

When trying to make sense of data, we might be tempted to start with the data and see where it leads us. But remember those confused students in Popper's lecture hall. Like them, we need a framework to guide our observation. With user stories, we've found an elegant way to provide exactly that for business intelligence - turning philosophical insight into practical efficiency.

Author:
Marcel Wiechmann
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