Data quality for education

Improve the reliability of education data and cross-jurisdictional matches across several databases to track and improve education programs or to enhance K-12 or P20-W SLDS initiatives.

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Did you know?

How bad data affects education?

87%

Cross-jurisdictional matching remains a challenge

87 percent of educators think that their colleges and universities will not be able to stay competitive without integrating their data across departments in the next five years.

Inaccurate matches

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

Departmental silos

Data residing across disparate sources reduces inter-departmental visibility across the entire organization.

Obsolete IT infrastructure

Lack of systems that can aggregate and prepare data in the required shape without compromising student privacy.

Inefficient stakeholder reporting

Data silos and absence of data standards can affect proper communication to private and public stakeholders.

Wasted resources

Inaccurate data leads to miscalculation of fill rates in various educational sections, resulting in wasted resources.

Poor program evaluation

Incomplete, missing, and duplicate records prevents educators from evaluating the effectiveness of assessments and education programs.

Solution

DataMatch Enterprise – The key to enhancing interagency record linkage

DataMatch Enterprise is Data Ladder’s enterprise-ready data quality and matching solution to help local education agencies, policy makers, and teachers link cross-jurisdictional datasets to find higher student matches and remove duplicates for enhancing SLDS and educational program outcomes.

Customer Stories

See what educational organizations are saying...

Business Benefits

What’s in it for you?

Increase enrollment rates

Access to quality data can help evaluate and improve programs to increase enrollment rates and retain under-performing students.

Implement effective policies

Enhance the reliability of data to plan and execute large-scale education policies and allocate appropriate funds and resources.

Establish master IDs

Define relevant match definitions and criteria to effectively track students from pre-school to the workforce across disconnected systems.

Reduce labor costs

Eliminate or cut significant labor costs associated with inspecting, cleansing, and standardizing thousands of records across multiple databases.

Access insights faster

Save hundreds of hours on manual data cleansing and analysis to ensure insights are accessible to stakeholders in a timely manner.

Secure program funding

Receive funding for educational programs from local or state governments for special-needs and underprivileged areas with reliable data.

Want to know more?

Check out DME resources

Merging Data from Multiple Sources – Challenges and Solutions