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ClearPATH

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The Problem

Workforce data is supposed to guide decisions. Too often, it confuses instead. Labels like IT or Advanced Manufacturing sound neat and singular, but they hide messy clusters of very different jobs. Industry data can even make matters worse as jobs get double-counted, categories overlap, and people are left asking impossible questions: Is IT or Finance a better bet, when half of Finance is really FinTech?

The truth is that career paths no longer stay neatly inside industry lines. Skills are portable, and mobility is essential. A software developer may work for a bank, a hospital, or a school, but the work done is the same. Insisting on rigid industry experience often disqualifies talent for no good reason.

Industry may be the language of business, but skills are the language of workers.

Our Solution

ClearPATH replaces jargon with a practical roadmap. It provides a data framework that complements traditional industry data while enabling us to build a new skills-based one. Clusters use our existing vernacular: IT, Advanced Manufacturing, Life Sciences, or STEM but now we can provide a single, shared definition. The taxonomy is designed to be adaptable and respond to the needs of workforce practitioners. We can refine definitions, change terminology, or add additional taxonomic ranks to dive into a subset of occupations within a cluster.

ClearPATH also simplifies analysis for those who are not data experts. Wages, job counts, barriers to entry, growth rates, automation risks are all important, but weighing them together is hard. Our Opportunity Score does that work. It distills the complexity into a single number, from 0–100, so users can compare career clusters and industries at a glance.

We have also started the process to  map education to occupations so that we can clearly show individuals how to pursue career pathways and let employers validate the needs and providers to ensure we are truly setting everyone up for success.

Why it Matters

ClearPATH turns workforce data into a usable language. For job seekers, it highlights careers that pay a living wage and build resilience. For educators and training providers, it shows the real-world value of their programs by tying them directly to demand. For employers and economic developers, it exposes skill gaps and talent pipelines with clarity.

And this is only the start. Future versions will sharpen employer-level insights, trace career progression, and help us turn data from noise into a practical guide for action.

About the Data

The ClearPATH dataset is a proprietary, non-public workforce intelligence corpus constructed from nearly 800 Standard Occupational Classification (SOC) codes. Each SOC code is normalized, categorized, and hierarchically aggregated into defined career clusters to enable both cross-occupational comparison and cluster-level analysis.For each occupation, the dataset integrates a multidimensional set of indicators, including but not limited to:

  • Demographic Composition: Age, gender, race/ethnicity, and labor force participation distributions derived from official labor market and population survey sources.
  • Earnings Data: Median and percentile wage estimates benchmarked against local living-wage thresholds.
  • Turnover Metrics: Separation and retention rates derived from longitudinal employment data.
  • Job Posting Analytics: Real-time demand indicators extracted from employer job postings, including frequency, intensity, and skill keyword analysis.
  • Talent Supply Data: Enrollment, completion, and credential output measures from postsecondary institutions, secondary CTE pathways, and approved short-term training providers.
  • Additional Attributes: Occupational growth projections, automation risk scores, barriers to entry (e.g., education/experience requirements), and employment-to-posting ratios.

Data is stored at the occupation level, with the ability to aggregate to cluster-level taxonomic ranks or disaggregate to more granular sub-clusters as required. This structure supports comparative scoring models (e.g., Opportunity Score) and custom analytical frameworks for workforce planning, career navigation, and economic development.

If you have feedback or are interested in digging into the data further please reach out to our Chief Impact Officer.

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