Lightcast Profile Analytics is built from individual profiles of over 120 million workers in the United States. Typical fields available are city/state/nation of residence, job history, education history, and skills. Many profiles also contain names, phone numbers, and email addresses, but these are not made available in bulk to Lightcast users.Profiles data can provide unprecedented levels of detail for labor market analytics, especially with regard to worker skill-sets, career paths, company-level human capital, school-level alumni outcomes, and more.

For proprietary and confidentiality/non-disclosure reasons, Lightcast cannot provide a detailed list of data sources. However, via our small number of main sources, Lightcast has aggregate access to a multitude of original sources, some of which provide thousands of profiles, and others millions.Our primary sources aggregate multiple other sources, and don’t always break down their own sources. Because of this, and for confidentiality reasons, we do not provide an exact source count. The total count of our deduplicated profiles (~120 million) is a much better measure of coverage than a count of sources.

First, data from all sources is standardized to a common format. This is necessary to simplify processing and enable deduplication across sources. Sources which do not support certain fields common to other sources are marked as having missing values.

We use several strongly unique and personally identifiable fields or combinations of fields such as name/email, name/phone, online URL(s), etc. to match profiles with each other. Matched profiles are collected into duplicate groups representing a single person. Our matching method prioritizes accurate matches over finding every single duplicate. Sometimes one of our data sources has already attempted deduplication; in those cases we still treat the resulting profile as a potential duplicate with other profiles. This process results in groups of interconnected profiles that we’ve determined are duplicates.Occasionally, due to bad source data, this process results in a duplicate group containing hundreds of source profiles. When this occurs, we have a threshold for acceptable group size and discard data from these groups — but this only affects a tiny fraction of source profiles.

Within each duplicate profile group that represents a single person’s “master” profile, we then need to merge the data for each field from multiple source profiles. We use customized similarity functions for each field to determine if a location, job, or degree is a duplicate of one already seen, then merge them together into a single sequence per master profile.

Finally, we classify various profile field values such as company and school into a smaller number of fixed categories to enable meaningful aggregation and analysis. We call this process “normalization.”An example of normalization would be to normalize free-form variations of “St. Louis, Missouri” as found in different profiles to “St. Louis. MO”. One person might list their location as “Saint Louis Missouri”; another might list “ST Louis MO”; and a third might list “St. Louis Missouri”. Normalization corrects all variations of this city name to “St. Louis, MO”. Without the normalization step, aggregate analysis of profiles would be impossible–searching for profiles in “St. Louis Missouri” would automatically exclude profiles where the person wrote their location as “Saint Louis Missouri”.

Lightcast uses Google Geocode API to standardize locations to city, state, and county.

Lightcast maintains a set of over 30,000 recognized skills and a context-aware extraction tool that identifies these skills in profile text.

For Alumni Outcomes and GoRecruit, Lightcast matches institutional data to this version of the profile database.