P20W refers to pre-kindergarten through college and into the workforce.

P20W data systems refer to the various state-level educational databases that collect student data across pre-kindergarten through college and into the workforce to help education leaders make policy decisions, the best use of resources, and support individuals throughout their life stages.

State Longitudinal Data Systems (SLDS) connect statewide information from early childhood through K–12 education, postsecondary education, and the workforce. These state-level data infrastructures in the U.S. securely bring together cross-agency data that enable leaders, practitioners, and community members to better understand the progress, predictors, and performance of learners throughout their educational and employment pathways. According to the Data Quality Campaign, SLDS:

• Reside in different places depending on the state context, but best practice is that the SLDS itself is not owned by any one contributing agency alone.
• Are Longitudinal (capture data from the same population over multiple years); Individual Level (include data that is specific to individual people but may contain identifiable information or be anonymous); and Statewide (bring together and connect data or records from multiple state agencies).
• Are supported in large part by a number of federal grants awarded to states to develop or improve their data systems in order to effectively measure the success of educational programs. Longitudinal data is needed for effective measurement.
• Are designed to help school districts, schools, and teachers make informed, data-driven decisions to improve student learning.
• Leverage stakeholders and partners of education, training, and employment programs to create a system which provides data to support the research and evaluation of programs to improve the outcomes of individuals provided service.

Data developments are fraught with challenges. Most state P20W and SLDS are custom built. They require technology and personnel investments to develop their systems from scratch—this can take years to materialize. Maintaining data systems over time creates financial strain and risks long-term durability of such a system. Examples of problems endemic to these developments:

1. They are unable to realize the promise of connecting full ecosystem of data sets across agencies.
2. They often have performance constraints due to an inability to keep up with the latest innovations in technology and data management.
3. They may have significant security vulnerabilities.
4. Data retooling of systems is expensive, and time-consuming. States rely on continuing infusion of federal funding, raising issues around funding reliability over time.