The problem isn’t that we lack data in education, but rather that most dashboards show us the past–-not the path ahead. It’s like trying to drive while only looking in the rearview mirror, writes Curt Merlau, Ed.D., vice president of the education practice at Resultant, an education technology company, in an eSchool News essay.
Every district has dashboards. We can see attendance rates, assessment scores, and demographic breakdowns. These tools tell us what happened, which is useful–-but increasingly insufficient for the challenges facing K-12 schools. By the time we’re reacting to chronic absenteeism or declining grades, we’re already behind. And when does an educator have time to sit down, pull up multiple dashboards, and interpret what they say about each student?
The power of any data dashboard isn’t in the dashboard itself. It’s in the conversations that happen around it. This is where data literacy becomes essential.
Data literacy means asking better questions and approaching data with curiosity. It requires recognizing that the answers we get are entirely driven by the questions we ask. A teacher who asks, “Which students failed the last assessment?” will get very different insights than one who asks, “Which students showed growth but still haven’t reached proficiency, and what patterns exist among them?”
We must also acknowledge the emotional dimension of data in schools. Some educators have been burned when data was used punitively instead of for improvement. The solution is to approach data with both curiosity and courage, questioning it in healthy ways while embracing it as a tool for problem-solving.
Let’s distinguish between types of analytics. Descriptive analytics tell us what happened: Jorge was absent 15 days last semester. Diagnostic analytics tell us why: Jorge lives in a household without reliable transportation, and his absences cluster on Mondays and Fridays.
The game-changers are predictive and prescriptive analytics. Predictive analytics use historical patterns to forecast what’s likely to happen: Based on current trends, Jorge is at 80 percent risk of chronic absenteeism by year’s end. Prescriptive analytics help the educator understand what they should do to intervene. If we connect Jorge’s family with transportation support and assign a mentor for weekly check-ins, we can likely reduce his absence risk by 60 percent.
The technology to do this already exists. Machine learning can identify patterns across thousands of student records that would take humans months to discern. AI can surface early warning signs before problems become crises. These tools amplify teacher judgment, serving up insights and allowing educators to focus their expertise where it matters most.
Before any school rushes to adopt the next analytics tool, it’s worth pausing to ask: What actually happens when someone uses data in their daily work?
How data is used is deeply human. It’s about noticing patterns, interpreting meaning, and deciding what to do next. That process is shaped by the environment in which educators work: how much time they have to meet with colleagues, how easily they can access the right data, and whether the culture encourages curiosity or compliance.
Technology can surface patterns; culture determines whether those patterns lead to action. The same dashboard can spark collaboration in one school and defensiveness in another. New tools require attention to governance, trust, and professional learning-–not just software configuration.
The goal isn’t simply to use data more often, but to use it more effectively. Practical steps:
1) Moving toward this future requires a fundamental shift in how we think about data: from a compliance exercise to a strategic asset. The most resilient schools in the coming years will have cultures where data is pervasive, shared transparently, and accessible in near real-time to the people who need it.
2) This means moving away from data locked in the central office, requiring a 10-step approval process to access. Instead, imagine a decentralized approach where a fifth-grade team can instantly generate insights about their students’ reading growth, or where a high school counselor can identify seniors at risk of not graduating with enough time to intervene.
3) Data democratization requires significant change management. It demands training, clear protocols, and trust. The payoff: educators who are empowered to make daily decisions grounded in timely, relevant information.
The schools that harness their data effectively will be able to identify struggling students earlier, personalize interventions more effectively, and use educator time more strategically. But this future requires us to move beyond the dashboard and invest in the human capacity to transform data into wisdom. That transformation starts with K-12 educators becoming data literate: curious, critical consumers of information who can ask powerful questions and interpret results within the rich context of their professional expertise.
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