In recent years, schools have been drowning in data. From attendance to exam results, the numbers keep piling up — and so does the demand for smarter ways to analyze them. Gartner predicts that by 2025, around 80% of primary and secondary schools will be using digital tools for educational analytics.

The hottest approach right now is data-driven decision-making (DDDM) — where choices are guided by statistics and evidence rather than gut feeling, emotions, or intuition.

Take this example: if test scores suddenly drop, the knee-jerk reaction might be to make tasks easier. But a closer look at the data could reveal the real story: maybe the curriculum needs adjustment, teaching methods require tweaking, or certain students need one-on-one support.

School data: what’s out there?

So what kind of data does a school actually have? At the teacher’s level: attendance logs, grades, lesson plans, peer feedback, and student comments. Analyzing this can highlight how a teacher works in class or track the progress of an individual student. 

On the school level, the dataset gets much bigger — covering an entire academic year. It’s not just grades and attendance but also demographic details, CRM data, medical records, behavior tracking, and even building access logs.

Add to that the external sources: surveys, reports from education authorities, parent feedback, and sometimes even insights from students’ social media. All of it can be used to build models and test ideas.

Four faces of data analytics

Not all analytics is created equal. In education, there are four main types: descriptive, diagnostic, predictive, and prescriptive.

The difference? The more advanced the analytics, the less manual work it requires. Descriptive analytics needs people to interpret it, while prescriptive analytics can go as far as suggesting what actions to take.

Let’s break them down.

Descriptive analytics

A look in the rear-view mirror: attendance sheets, grade lists, or which classrooms are free for a substitute teacher today.

Diagnostic analytics

The “why” behind the numbers. For instance, splitting a class into two English groups — did it help their results? How do those results compare to external exams?

Predictive analytics

Peeking into the future. Predicting if a student will reach their learning goals or calculating how many textbooks the school will need next year.

Prescriptive analytics

Going one step further: recommending courses for a student, advising the school to open new positions, or planning specialized learning tracks.

Data headaches schools face

Big data in schools sounds great, but reality is messy. Three major challenges stand out:

1. Every system stores data differently. That’s why tech that centralizes and organizes it all is crucial.
2. Not all data is reliable. Schools may polish reports; students may provide fake info (Italy’s university attendance logs often feature a certain “Giuseppe Garibaldi”).
3. Collecting data is only the beginning. Turning it into insights — and acting on them — is the real challenge for school leaders and teachers.

How schools can go data-driven

To truly become data-driven, schools need more than just software. They need resources, skills, and a clear plan. Here’s a roadmap:

1. Map out your data sources. Check accuracy, timeliness, and relevance.
2. Pick the right tools. Excel or Google Sheets may work for basics, but advanced analytics calls for PowerBI, Tableau, or dedicated school systems with built-in analytics.
3. Build the infrastructure. Centralize storage, set up databases, and make sure all data flows into one platform.
4. Train your team. Find motivated staff who’ll lead the way. Support them with training and guidance.
5. Visualize the story. Graphs and dashboards make patterns visible. For instance, a student’s grade chart can tell a clearer story than a list of numbers.
6. Use the right models. Small groups don’t fit large-scale statistics — choose wisely.
7. Remember: one drop in grades can have many causes. Holidays, late arrivals, competitions — data is just the starting point. What matters is how you use it to spark dialogue and solutions.
   In the end, data won’t replace teaching. But it will give schools sharper insights, help solve problems faster, and create better conditions for students to grow.

Used wisely, data allows schools to measure performance more precisely, uncover challenges, and take meaningful action. It’s not a substitute for learning, but a powerful way to understand needs, raise quality, and unlock student potential.