Garbage In, Garbage Out
The most sophisticated analytics can't overcome bad data. Missing records, inconsistent coding, duplicate entries, and outdated information corrupt every analysis built on them. Data quality is prerequisite to data value.
The early warning system flagged 200 students as high-risk—but when counselors investigated, they found errors everywhere. Students marked absent who were actually in class. Grades from previous years incorrectly attributed. Demographic data outdated by three years. The predictions weren't wrong; the data was.
Dimensions of Data Quality
Accuracy
Does the data reflect reality? Are attendance records correct? Are grades entered properly? Are demographic fields current?
Completeness
Is all required data present? Missing data creates blind spots. If 20% of students lack test scores, analyses of those scores are incomplete.
Consistency
Is data coded the same way everywhere? If one school codes suspensions differently than another, comparisons are invalid.
Timeliness
Is data current? Outdated information leads to outdated decisions. Attendance data from last month doesn't help identify today's problems.
Uniqueness
Are there duplicate records? Multiple entries for the same student corrupt counts and analyses.
Common Data Quality Issues
- Missing data: Required fields left blank
- Duplicate records: Same student entered multiple times
- Inconsistent coding: Different values for same concept
- Outdated information: Data not updated when changes occur
- Entry errors: Typos, wrong fields, incorrect values
- Integration failures: Systems not syncing properly
Resources & Guides
Access implementation guides, best practices, and training materials for your team.
Building Data Governance
Define Data Standards
Establish clear definitions and coding conventions. What counts as an absence? How should race/ethnicity be coded? Consistent standards enable consistent data.
Assign Data Stewardship
Designate owners responsible for data quality in their domains. Attendance data has an owner. Grade data has an owner. Someone is accountable for quality.
Implement Validation Rules
Build checks into data entry: required fields, valid value ranges, format requirements. Prevent errors at entry rather than fixing them later.
Regular Audits
Periodically review data quality. Run reports identifying missing data, outliers, and anomalies. Address issues before they corrupt analysis.
Training
Staff entering data must understand importance and standards. Training on proper entry procedures prevents many quality issues.
Data Security
Access Controls
Limit access to those who need it. Role-based permissions ensure teachers see their students, not all students; principals see their schools.
Audit Trails
Track who accesses what data. Audit logs enable monitoring for inappropriate access and investigation when issues arise.
Secure Transmission
Protect data in transit. Encryption, secure connections, and proper protocols prevent interception.
Data Integrations
Connect your existing SIS, assessment, and data systems seamlessly with AcumenEd.
The district fixed their data quality issues. They established standards, assigned stewardship, implemented validation, and trained staff. When the early warning system ran again, it flagged students who actually needed help. Good governance made good analytics possible.
Key Takeaways
- Data quality dimensions include accuracy, completeness, consistency, timeliness, and uniqueness.
- Governance requires standards, stewardship, validation, audits, and training.
- Security through access controls, audit trails, and secure transmission protects sensitive data.
- Invest in data quality before investing in analytics—it's the foundation of everything else.
James Okonkwo
Senior Implementation Specialist
Former charter school administrator with deep expertise in Michigan charter school accountability and authorizer relations.
