How AI Can Harness State Longitudinal Data to Empower Constituents

Most states today have an active statewide longitudinal data system (SLDS). Predominantly used to integrate educational and workforce data, these systems track employment and wage outcomes compared to an individual’s education and training, giving insights about program and policy outcomes for public sector stakeholders to improve decision-making.

When states combine targeted AI tools with their SLDS, a world of effective solutions opens to better serve constituents.

Quick facts:

  • 49 states have an active SLDS.
  • 34 states make and utilize connections within secondary, postsecondary, and workforce data.
  • Several states also connect data from public health, social services, criminal justice, and other agencies.

Longitudinal data plus AI produces actionable insights

SLDSs already contain an abundance of data that, when combined with AI tools, can generate incredible insights not just for states and policymakers but for citizens themselves. Expertly developed recommendation engines can bring new SLDS-driven insights directly to state residents to connect them with relevant job and training opportunities, healthcare options, social services, early education providers, and much more.

These personalized recommendations remove numerous obstacles that currently prove daunting to those seeking job and training opportunities, impeding the outcomes both states and constituents seek.

Artificial intelligence for personalized recommendations

Resultant has built an AI tool to dramatically improve a state’s ability to connect UI claimants with job and training opportunities. This current version, integrated within the state’s UI claimant portal, leverages individual, state agency, and labor market data combined with a custom algorithm for accurate recommendations.

The recommendation engine utilizes a class of machine learning that filters data to predict and locate what an individual is looking for among an exponentially growing number of options.

Scalable machine learning tools reveal solutions for other dilemmas

Because it harnesses the breadth and volume of state government data, the recommendation engine far surpasses the level any other tools have reached, providing users with insight into where they fit according to other job seekers with similar knowledge, skills, experiences, education, and credentials in the same location.

This tool is portable and scalable—it grows and adapts as state needs become better defined and change over time. It can be used to address other areas in which states would like to improve services or address issues, such as early child care, health and human services, K-12 education, corrections, and more.

Now is an ideal time for states to expand their thinking about SLDS and how these systems can directly benefit residents.

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