• States across the U.S. are ramping up their data efforts in deploying statewide longitudinal data systems (SLDS) that contain information on public school students. The records are collected over the course of the students’ life from the time they join a public institution to the time they enter the workforce. The data obtained is used to asses student progress and identify areas of improvement in the education ecosystem using a data-driven, scientific approach.

To achieve this objective, SLDS grants are offered for states to either improve their existing data systems or to deploy the SLDS. In both cases, the state will have to initiate the process by managing and upgrading their data. And herein lie the heart of the challenge.

Three Key Data Challenges States Face with the SLDS System

Longitudinal data is long-term data that tracks the same entity or in this case, a student over different points in time. Public institutions and educational systems have the responsibility of obtaining accurate and relevant data, however, over time, longitudinal data can become obsolete, reflect redundant or duplicated information thus making it difficult for stakeholders to make efficient use of the data.
In a nutshell, stakeholders of the SLDS system have to struggle with:


    1. Poor Data Quality: Even today, data recording is done manually. During this process, it’s highly possible for a human to type in the wrong spelling, to use abbreviations and punctuations were not required or even to enter data in a format they are familiar with. Moreover, there is a high chance for duplication especially if the data entry operator makes a mistake in assigning the right unique key identifier. All these are seemingly minor issues that become severe bottlenecks. Poor data cannot be ignored.


    1. Difficulty in Interagency Record Matching: At the heart of SLDS challenges lies this struggle of record linkage or record matching of data between agencies. An individual’s longitudinal data is recorded by the multiple agencies, schools, districts or states that he or she has lived in over the course of their lives. The SLDS will require all this data to be an accurate match of the individual it represents. It is here, that challenges in data quality will make it extremely difficult for involved stakeholders to make an accurate match.In fact, SLDS staff spend a significant amount of their time just matching records. States across the U.S. are testing and employing several methods to overcome this problem. Some states use commercial tools, some create their own matching processes while some use a combination of both. Despite the use of several processes and tools, most states have reportedly been struggling with accurate matching. The SLDS requires a state to meet 95% of data match accuracy to receive a grant!


  1. Identity Resolution Remains a Challenge: Poor identity resolution is the consequence of poor data quality and poor matching accuracy. With limited budgets, government institutes cannot afford to hire data specialists or data solutions that could cost millions of dollars. This is why they have to resort to making do with limited team members relying on manual processes to resolve identity matching issues.Significant time is wasted on identifying information and matching them to the relevant people leaving little room for analysis or reporting – two fundamental purposes of the SLDS. Add to this the duplication of records from multiple agencies and you have a data management crisis.

What is the Role of Data Ladder in Record Linkage and How Does it Help with States with SLDS Grants?

Data Ladder is an enterprise-level data quality solution that offers data preparation, data cleaning and data matching as its core services. We’ve worked with the Department of Education of several states requiring a data match tool to help them meet the 95% match rate accuracy to acquire the SLDS grant.
Data matching relies on accurate information. If data sets show erroneous, incomplete, incorrect information, it will be impossible to get accurate data matches. Hence, the starting point of any data matching activity lies not in the matching itself but in ensuring that the data quality is up to the mark. For this purpose, Data Ladder’s flagship DataMatch Enterprise takes the user through a step-by-step module which starts with the integration of the data source, followed by data preparation, data cleaning, data matching and finally data merging.
Using Data Ladder, project leads at state departments have been able to:

    1. Dedupe Redundant and Duplicated Data Across Data Sources: Interagency data is bound to have redundant and duplicate data especially since data privacy laws prevent the sharing of unique identifiers as SSNs to identify individuals. This leaves agencies with no choice but to create their own unique identifiers, which leads to duplicated information if another agency has the same information of the individual. Moreover, human errors in data entry can accidentally cause duplicates which are hard to track with deterministic matching algorithms. Data Ladder’s flagship software DataMatch Enterprise uses four different types of algorithms in combination with its proprietary algorithm to return a matching rate of 96% – the highest in the industry.


    1. Data Quality Issues & Data Standardization: Data quality remains a persistent challenge with each new project having to be cleansed before it can be matched. This means states need a quick solution that can allow for regular data cleaning without involving staff. Upon integration, the DME software automatically scans data and highlights data health. The user is then prompted to use pre-defined business rules to clean their data. This would include a basic action as capitalizing first letter of a name to an advance step as defining business rules to sort data. Data Ladder allows users to standardize data across integrated data sources with just a few clicks.


  1. Get the Highest Data Matching Rate: In an experiment conducted by the State of Connecticut to determine whether the NSC or a commercial vendor tool delivered higher matching rates, Data Ladder returned a 100% match rate – higher than the NSC. In another sample, a state missed out on nearly half of the matches with its in-house matching algorithm. With Data Ladder, the sample saw a 20% increase in data matching. A high match rate reflects the accuracy of data, which is the key indicator of the effectiveness of the SLDS program. Without accurate data match, the SLDS will not be able to derive accurate insights, which are important in identifying gaps that need to be addressed and improvements that need to be implemented.

Read this whitepaper to see how Data Ladder helps with linking student information across several databases, enhance PSIS in tracking educational experiences and evaluate the impact of students’ secondary and postsecondary education on their experiences in the workforce.

Data Matching for SLDS

The Role of Data Matching in Implementing and Enhancing Interagency SLDS

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For a state to deploy a successful SLDS program, it’s imperative they have the right tool to get the job done without costing millions of dollars. Data Ladder is the only affordable best-in-class data quality solution with the highest data matching accuracy in the industry. To say it’s at the heart of a successful SLDS deployment is to state the fact.

Farah Kim is an ambitious content specialist, known for her human-centric content approach that bridges the gap between businesses and their audience. At Data Ladder, she works as our Product Marketing Specialist, creating high-quality, high-impact content for our niche target audience of technical experts and business executives.