How School Leaders Can Use the Right Data at the Right Time

In the age of dashboards, spreadsheets, and survey tools, school leaders are surrounded by data. But not all data is created equal or equally useful. Understanding the difference between good and “not so good” data, and when to use quantitative versus qualitative data, is essential for making informed decisions that truly benefit students and staff.

📉 What Makes Data “Good” or “Bad”?

“Good” data is accurate, relevant, timely, and collected with a clear purpose. “Bad” data, on the other hand, might be outdated, incomplete, biased, or taken out of context. AI tools can help flag inconsistencies and cross-reference data points to ensure you’re working with reliable information.

🚩Red flag: You’re using survey data from three years ago to shape this year’s school improvement plan. While it may offer some historical perspective, it likely doesn’t reflect current needs or context.

📊 When to Use Quantitative Data

Quantitative data—test scores, attendance rates, behavior incidents, survey results—provides measurable evidence. It’s great for tracking trends, justifying funding requests, or identifying students at risk.

Examples of when to quantitative data:

• Applying for grants (e.g., increase in test scores show promising improvement or decreases show a need)

• Evaluating program impact (e.g., pre- and post-assessment scores)

• Allocating resources (e.g., which grade level has the highest need for tutors)

💬 When to Use Qualitative Data

Qualitative data—interviews, open-ended survey responses, observations, student stories—adds context and meaning. It helps you understand why a problem is occurring, how students and staff feel, and what changes might work in practice.

Examples of when to qualitative data:

• Making program design decisions (e.g., why students aren’t attending after-school tutoring)

• Gaining insight into school climate and culture

• Communicating needs to external stakeholders or board members

Illustrative Example: A high school principal noticed an uptick in behavior referrals during 6th period, supported by clear quantitative data. But it was a series of student focus groups (qualitative data) that revealed the root cause: students were not getting enough time to eat lunch due to long cafeteria lines and walking into class distracted and hungry. The school adjusted scheduling and lunch procedures—and saw referrals drop 30%.

⚖️ How to Balance Both

The most effective decisions are informed by both types of data. You can think of quantitative data as the what, and qualitative data as the why. Use them together to form a full picture before making changes or asking for resources.

Tips:

• Pair attendance data with family and student feedback to understand chronic absenteeism.

• Combine survey trends with test scores and classroom observations to assess instructional quality.

• Use AI tools like schoolops.ai to analyze large data sets and identify patterns that warrant deeper qualitative exploration.

Good leadership is data-driven and data-smart. Knowing what kind of data to use, and when, allows you to lead with both precision and empathy. By leveraging AI to blend numbers with narratives, you’ll make decisions that are not only evidence-based, but also student-centered.

When it comes to data, don’t just ask, “What do I have?” Ask, “Do I have the right data for this decision?”