The Real Power of Data: Why Interpretation Is Everything

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It’s been two weeks* since the Data in Schools Conference, and one idea continues to echo loudly in my mind: 

“It’s the interpretation of data that matters.”

These words, shared by Professor John Hattie in his keynote, cut through the noise and landed with a ‘solidness’ that imbued comfort. In an era where schools (and systems) are swimming in dashboards, test scores, and surveys, Hattie reminded us that data is only as powerful as the thinking it provokes.

We don’t need more data. We need better questions, deeper interpretation, and stronger alignment between what we measure and what we actually value.

Soup, Striving, and Self-Assessment

Hattie’s soup metaphor stuck with many of us who attended the Conference - a playful, pointed way to reframe assessment. If the cook tastes the soup, it’s formative. If the guests taste it, it’s summative. But if the recipe alone dictates the flavour, that’s ascriptive - the most limiting form of all.

This reminded me how easy it is to fall into the trap of “checking” student learning, instead of creating conditions for it. We often treat data as a finish line when it should be a starting point for dialogue, curiosity, and feedback.

Another standout idea I heard repeatedly through the Conference: helping students become interpreters of their own learning. Not just collecting progress data, but actively teaching students how to make sense of it. Can your students answer:

• What do I already know?

• What don’t I know yet?

• What will help me get there?

When students can ask and answer these questions, they move from passive participants to self-aware, evaluative learners.

From What Works to What Works Best

One of Hattie’s provocations was a challenge to move from "what works" to "what works best." It’s not enough to know that an approach has an effect - we need to know when, for whom, and why it works. As educators, this requires us to keep aligning our strategies with where students are in their learning journey - surface, deep, or transfer.

We also need to stop using data as a weapon. Hattie warned against weaponising assessment in ways that devalue teacher expertise or student experience. Instead, we need to foster collective efficacy, which remains one of the most powerful influences on student learning.

The Role of AI? Error Detection, Not Replacement

AI was certainly a big topic of conversation. Not as a shiny solution, but as a vehicle with great potential. Hattie noted that the most powerful use of AI in education is error detection, spotting patterns that teachers might miss (like spelling slips or feedback inconsistencies). But the data interpretation still sits with humans. AI can highlight the “what,” but educators must decide the “so what” and “now what.”

Two weeks on, what lingers is not a list of new tools or platforms - it’s a renewed commitment to using data with intention. To question more deeply. To align more carefully. And to create spaces where students and teachers alike feel safe to fail, reflect, and grow.

That’s the real power of data. Not in the collection, but in the conversation it sparks.

*Props to those wonderful individuals who can churn out a blog post mere hours after an event; I always need a little time to marinate in the great ideas and conversations before a written piece emerges.