Data quality for education

Strengthen the reliability of education data by enabling cross-database, cross-jurisdictional matches that improve program tracking and advance K-12 and P20-W SLDS initiatives.

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How bad data affects education?

87%

Cross-jurisdictional matching remains a challenge

87 percent of educators believe their colleges and universities won’t remain competitive over the next five years without integrating data across departments.

Inaccurate matches

States and educational institutions often miss a large number of matches during record linkage projects.

Departmental silos

Disparate data sources hinder interdepartmental visibility and obscure a unified organizational view.

Obsolete IT infrastructure

The absence of systems that can aggregate and prepare data effectively—without compromising student privacy—remains a major challenge.

Inefficient stakeholder reporting

Data silos and the lack of standards hinder effective communication with private and public stakeholders.

Wasted resources

Inaccurate data causes miscalculated fill rates across educational programs, leading to wasted resources.

Poor program evaluation

Missing, incomplete, and duplicate records make it difficult for educators to measure the effectiveness of assessments and programs.

Solution

DataMatch Enterprise – The key to enhancing interagency record linkage

DataMatch Enterprise is Data Ladder’s enterprise-ready data quality and matching solution that helps local education agencies, policymakers, and teachers link cross-jurisdictional datasets, increase student match rates, and remove duplicates to strengthen SLDS and improve educational outcomes.

Customer Stories

See what educational organizations are saying...

Business Benefits

What’s in it for you?

Increase enrollment rates

Access to quality data enables evaluation and improvement of programs that boost enrollment and support underperforming students.

Implement effective policies

Enhance data reliability to plan and execute large-scale education policies while ensuring proper allocation of funds and resources.

Establish master IDs

Define relevant match rules and criteria to effectively track students from preschool through the workforce across disconnected systems.

Reduce labor costs

Eliminate significant labor costs tied to inspecting, cleansing, and standardizing thousands of records across multiple databases.

Access insights faster

Cut manual data work and free up hundreds of hours, making insights available to stakeholders when they need them.

Secure program funding

Reliable data helps institutions obtain government funding for programs serving special-needs and underprivileged communities.

Want to know more?

Check out DME resources

Merging Data from Multiple Sources – Challenges and Solutions

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