Data vs. Information vs. Insight
Data is raw facts. Information is data organized meaningfully. Insight is understanding that drives action. The goal of education analytics isn't more data—it's better decisions that improve learning.
Principal Hernandez had access to thousands of data points about her students. State test scores. Attendance records. Discipline incidents. Course grades. Benchmark assessments. The data existed—but what did it mean? Which students needed help? Which interventions were working? Where should she focus resources?
Education data analytics bridges the gap between having data and using it effectively.
Types of Education Data
Academic Data
- • State assessment results
- • Benchmark/interim assessments
- • Course grades and GPA
- • Credit accumulation
- • Formative assessment data
Behavioral Data
- • Attendance records
- • Discipline incidents
- • Office referrals
- • Engagement indicators
Demographic Data
- • Race/ethnicity
- • Income status
- • English learner status
- • Special education status
- • Mobility patterns
Program Data
- • Intervention participation
- • Extracurricular involvement
- • Course enrollment patterns
- • Service delivery records
All AcumenEd Features
Explore our complete suite of data analytics tools designed for Michigan charter schools.
Analytics Approaches
Descriptive Analytics
What happened? Descriptive analytics summarizes past performance: proficiency rates, attendance averages, graduation rates. This is the foundation—understanding current state.
Diagnostic Analytics
Why did it happen? Diagnostic analytics explores causes: Why did math scores decline? What predicts chronic absenteeism? Where are the biggest achievement gaps?
Predictive Analytics
What might happen? Predictive analytics identifies risk: Which students are likely to fail? Who might drop out? Where are problems developing?
Prescriptive Analytics
What should we do? Prescriptive analytics recommends action: Which intervention matches this student? What resources should be allocated where?
Analytics Maturity
Key Analytics Questions
Effective analytics starts with questions, not data. Common education analytics questions include:
- • Which students are at risk of falling behind?
- • Are achievement gaps widening or narrowing?
- • Which interventions produce the best results?
- • Where should we allocate additional resources?
- • Are students on track for graduation?
- • What early warning signs predict later problems?
Resources & Guides
Access implementation guides, best practices, and training materials for your team.
Building Analytics Capacity
Data Infrastructure
Analytics requires accessible data: integrated student information systems, data warehouses that connect sources, and tools that make data queryable and visualizable.
Data Literacy
Staff at all levels need skills to interpret data: understanding what metrics mean, identifying patterns, avoiding common misinterpretations, and translating insights into action.
Analytics Culture
Data use should be embedded in how the organization operates: regular data review routines, decisions grounded in evidence, continuous improvement orientation.
Principal Hernandez invested in analytics capacity. She implemented dashboards that surfaced key metrics, trained her team to interpret data, and built routines for regular data review. The thousands of data points became actionable insights—students who needed intervention, programs that were working, resources that should be reallocated.
That's the promise of education data analytics: not more information, but better decisions.
Key Takeaways
- Education data spans academic, behavioral, demographic, and program domains.
- Analytics progresses from descriptive (what) to diagnostic (why) to predictive (what might) to prescriptive (what should).
- Start with questions, not data—analytics should answer decision-relevant questions.
- Building capacity requires data infrastructure, data literacy, and analytics culture.
Marcus Johnson
Director of Data Science
Data scientist specializing in educational analytics with expertise in growth modeling and predictive analytics for student outcomes.
