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Talk to almost anyone from the San Francisco Bay area, and they’ll gladly complain to you about their local transit system, the dreaded Bay Area Rapid Transit, or BART. BART trains are infamously crowded, late, and noisy, so much so that their horrible screech inspired some of the creepiest sounds in video game history. This is not a result of major mismanagement, corruption, or plain bad luck, but rather outdated and insufficient infrastructure.
Designed and built for what was previously a small seaside city, BART now shuttles hundreds of thousands of residents per day. Its record for one-day ridership — nearly 600,000 on Halloween in 2012 — is nearly equivalent to the city’s total population when the train system was built in 1972. The city itself even recognizes that much of the train track, electrical cabling, and other critical components have “long exceeded their design lives”.
Apprenticeship’s growth requires high-quality data
Much like the city of San Francisco’s booming population, Registered Apprenticeship Programs have expanded rapidly over the past decade, with further growth anticipated in the coming years.
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Since 2015, the number of active apprentices has nearly doubled to over 600,000.
The range of occupations and industries has broadened to include everything from advanced manufacturing to healthcare and beyond. Add to this a set of recent executive orders – notably one calling for an expansion to one million apprentices annually – and apprenticeship’s incredible momentum becomes clear.
With this growth comes the need for more accurate, timely, and widely available data. As a regulated program overseen by various state and federal agencies, Registered Apprenticeship relies on data for both its core functionality and continuous feedback cycles. High-quality programmatic data acts like a lighthouse, illuminating potential hazards and offering a reliable path forward. It can enable stakeholders to track progress on a small scale (how individual apprentices are progressing through technical instruction or on-the-job training) or at a high level (how different demographic groups are performing in apprenticeships across entire industries).
One of the benefits of the Registered Apprenticeship system is that with its significant governmental oversight comes robust data collection. The vast majority of quantitative information available on apprenticeships comes from the Registered Apprenticeship Partners Information System, more commonly known as RAPIDS. Developed by a third-party vendor but managed by the US Department of Labor, RAPIDS collects basic data on apprenticeship programs and individual apprentice registrations.
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RAPIDS was initially created to handle just registrations, but now acts as an information management system and – given it is the federal government’s principal source of apprenticeship data – an informal public accountability tool.
Like BART, RAPIDS is being stretched to perform at a level for which it wasn’t initially designed.
Where outdated data infrastructure falls short
While RAPIDS can be a powerful tool for aggregating apprenticeship information, it is outdated and therefore error prone. One such issue arises from the fact that states often have their own unique approach to apprenticeship data collection. Many states operate independent State Apprenticeship Agencies which oversee local programs, provide supportive services, and indeed manage their own apprenticeship data. Ten of these states and the District of Columbia have developed their own data management systems for apprenticeships in order to ease grant making, legislative reporting, or integration with their other databases.
In addition to collecting and processing their own data, states are also required to submit reports to the US Department of Labor, a process that lends itself to duplication, missing information, or other user errors.
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Ultimately, this bifurcation between data collection procedures is a persistent issue that can’t be easily solved: without updated legislation, the DOL has limited authority to mandate which data states must collect and submit.
Furthermore, apprenticeships are complex by nature, frequently operating as large organizational partnerships between employers, training providers, funders, and program participants themselves. This interconnected web of stakeholders introduces even more data collection, management, and reporting intricacies. Even small details such as varying formatting requirements can result in major inconsistencies. Other barriers like needing multiple data sharing agreements can slow progress or scupper these delicate partnerships altogether.
The consequences for poor data infrastructure
This lackluster data infrastructure, consistency, and quality has serious consequences:
- Key apprenticeship advocates and stakeholders such as policy makers and philanthropic organizations have a diminished ability to deploy human capital and financial resources most effectively.
- Advocates lacking crucial and clear data have a harder time defending the impact of apprenticeship to employers and legislators.
- Employers, tired of inefficient reporting procedures, are less likely to adopt or expand their apprenticeship programs.
- Perhaps most importantly, and especially with rising disdain for government institutions, poor data can lead to negative perceptions of the apprenticeship system and an erosion of public trust.
Refreshing and rebuilding Apprenticeship Data Systems
Fortunately, there are a number of improvements on the horizon. The US DOL created the State Apprenticeship Agency Portal which allows states to directly upload their data to RAPIDS. As of 2023, RAPIDS now contains individual apprentice records from all US states, territories, and even Military Apprenticeship Programs. As a possible next step, the DOL could designate one of the new and emerging software products for apprenticeship data management as a nationally recognized platform, potentially offering a consistent and streamlined data solution.
Apprenticeship advocates have also lobbied to implement a number of additional structural improvements. The Pathways Alliance, a coalition of education organizations supporting educator training programs, developed a robust set of recommendations for enhancing data infrastructure for Registered Apprenticeships. This includes: creating a transparent data model and dictionary that defines each reporting element and how it is calculated; establishing an interagency data task force, and; increasing technical assistance to help sponsors launch and maintain new programs. All together, these changes would transform the landscape of apprenticeship data.
Improving the data quality, infrastructure, and interoperability of the Registered Apprenticeship system is of critical importance. We must move beyond the systems of the past, especially when it comes to such valuable information that ultimately shapes program design, funding, and expansion. In San Francisco, the city is investing heavily in rebuilding BART’s infrastructure. So too should we refresh and enhance the data infrastructure of our Registered Apprenticeship system to meet the workforce needs of tomorrow.
