Refers to integrated data, analytics, and information systems designed to help governments, employers, educators, workforce organizations, economic development agencies, and other stakeholders better understand workforce trends, labor market conditions, talent needs, skills demand, and employment outcomes.

Workforce intelligence systems typically combine information from multiple sources, including labor market analytics, workforce and employment records, postsecondary education data, credential and skills data, job postings, wage and earnings records, demographic information, economic and industry trend data.

These systems are increasingly used to support workforce planning, talent pipeline development, economic development strategies, education-to-employment alignment, skills-based hiring, credential transparency, career navigation, regional workforce analysis, policy development, and return on investment (ROI) analysis.

Workforce intelligence systems may be operated by state governments, workforce agencies, higher education systems, economic development organizations, labor market analytics firms, or multi-agency partnerships. Some systems function primarily as public dashboards or reporting tools, while others operate as broader cross-agency infrastructures that integrate workforce, education, and economic data.

While terms describing workforce and labor market data systems have existed for decades, newer terms such as workforce intelligence systems have emerged more recently to reflect increasing integration of education, workforce, credential, and labor market analytics used to support more real-time planning and decision-making across the learn-and-work ecosystem.

In recent years, advances in data integration, labor market analytics, artificial intelligence, and skills-based workforce strategies have expanded the role of workforce intelligence systems within the learn-and-work ecosystem. Many states and organizations are increasingly using these systems to identify workforce shortages, forecast talent needs, analyze credential supply and demand, and better align education and training systems with labor market conditions.

As workforce intelligence systems expand, they also raise considerations around data quality, governance, interoperability, privacy, transparency, algorithmic bias, cross-system compatibility, and responsible interpretation of workforce and earnings data.

See Topic Brief: The Development & Role of State Postsecondary Data Dashboards | Learn & Work Ecosystem Library