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?
Cross-jurisdictional matching remains a challenge
Only 37% of educators say they have all the information needed to address student needs, and 86% report that these gaps limit their ability to support academic progress and attendance.
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...
DataMatch™ has cut my cleaning time down from 10-14 days to approximately 16 hours.
DataMatch Enterprise ™ gives us many facilities for the integration issue. We had a problem of duplication of records in which it helped us and was fantastic to solve in a very simple way.
The idea of linking two groups of records was overwhelming for the research department. The process would be very time-consuming and threaten the timeliness and process of the research activities
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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Source-to-Target Mapping Best Practices for Accurate, Scalable Data Pipelines
Last Updated on January 21, 2026 Source- to-target mapping usually gets attention for about five minutes, right before a pipeline goes live. After that, it’s
Managing Nicknames, Abbreviations & Name Variants in Enterprise Entity Matching
Last Updated on January 13, 2026 A name might feel like the simplest identifier, but in enterprise datasets, it rarely is. In the US and
Source-to-Target Mapping Best Practices for Accurate, Scalable Data Pipelines
Last Updated on January 21, 2026 Source- to-target mapping usually gets attention for about five minutes, right before a pipeline goes live. After that, it’s
Managing Nicknames, Abbreviations & Name Variants in Enterprise Entity Matching
Last Updated on January 13, 2026 A name might feel like the simplest identifier, but in enterprise datasets, it rarely is. In the US and
Linking Similar Records with Incomplete Data: Proven Approaches for High-Accuracy Entity Matching
Last Updated on January 13, 2026 If record linkage were as simple as matching names and emails, organizations wouldn’t be sitting on mountains of unleveraged
