Bringing Richer, Verifiable Candidate Data Into HR Systems: An ecosystem roadmap

Human Resource (HR) technology systems play a major role in mediating economic opportunity. The use of tools such as Applicant Tracking Systems (ATS) and Human Resource Information Systems (HRIS) defines how job candidates can present themselves and how likely they are to be identified as a potential fit for a role. Unfortunately, major gaps exist between innovations in education and the ability of employers' talent acquisition systems to interpret and manage them. Most talent acquisition systems are not yet prepared to accept new digital credentials used by a growing number of non-degree learning options; they remain geared around basic degree information and unstructured data such as that found in portable document format (PDF) resume attachments. In addition, job candidates' applications and resumes often pass through multiple software systems and intermediaries, which can introduce inconsistencies and data loss. Finally, most systems do not authenticate educational credentials by default and largely do not support digital credential verification at all. Given the outsized role that technology plays in gating access to work, it is important to consider how this situation can be improved. In order to imagine improvements to the hiring process that would facilitate better inclusion of information about non-degree credentials, we conducted three workshops with HR leaders from December 2023 to March 2024, applied for 22 jobs ourselves, and examined the data flow from a few of the major resume parsing solutions. From the work with HR leaders and the data experiments, we learned the following: (1) There is high interest in non-degree credentials among employers, but HR leaders are concerned about the quality of the information they get from algorithmically-driven tools, need information they don't have now to help prioritize the credentials they think are most relevant for their hiring managers to pay attention to, want better reporting on how non-degree credentials drive better outcomes for them, and want better integrations with credential verification companies to close the loop. (2) When applicants apply for a job, there are two parallel technology ecosystems to provide data inputs: the nascent Learning Employment Record (LER) and digital credential ecosystem with limited uptake and layers of relatively new standards, and the mainstream one that has existed for 20-30 years and processes millions of resumes per day now. (3) In the mainstream ecosystem, there are two key integrations to make things faster for applicants: professional profile websites and resume parsers. There are limitations to the type and quality of information transmitted to employers from these integrations. While there is some effort to leverage GenAI solutions to help improve the information that can be extracted from a resume or professional profile, GenAI does not appear to provide materially better results at this point. Instead, it would seem that there is a need for more and better data. This work informed recommendations in this report for how to imagine systems change that will allow for more and better data about applicants, notably including robust information on non-degree learning experiences, to flow through hiring processes and into the hands of managers making hiring decisions.