Standardizations

Learn how Visier's standardization matches are made, and how they contribute to advanced analysis and industry benchmarking.

Overview

Standardization is a data enrichment service that matches your unique data to well defined names and taxonomies. This way, your unique data can be used in structured analysis. Standardizations make it possible to make comparisons between your organization and Visier's industry benchmarks.

The following table describes the standardizations that are available.

Standardization	Description
Employee Standardized Location	An automated data enrichment service that matches internal location values with standardized locations from Visier’s taxonomy.
Requisition Standardized Location	An automated data enrichment service that matches requisition locations with standardized locations from Visier’s taxonomy.
Visier Employee Standardized Contract Type	An automated data enrichment service that matches internal contract types with standardized contract types from Visier’s taxonomy.
Visier Employee Standardized Employment Type	An automated data enrichment service that matches internal employment types with standardized employment types from Visier’s taxonomy.
Visier Employee Standardized Occupation	An automated data enrichment service that matches internal job titles with standardized job titles from Visier’s taxonomy.
Visier Requisition Standardized Occupation	An automated data enrichment service that matches requisition job titles with standardized job titles from Visier’s taxonomy.

Standardization examples

The following examples illustrate how the Visier Employee Standardized Occupation and Employee Standardized Location standardizations would be used to standardize unique job titles and location names.

Standardized Occupation

The diversity of job titles within an organization complicates structured analysis within an organization, as well as industry benchmarking across organizations. There is an overwhelming number of unique values in every organization, which makes it difficult to manually standardize job names.

For example, let's say your organization uses the job titles Senior HR Business Partner - Marketing, HR Business Partner I, and Sr. HR Business Partner for the same job. With Visier Employee Standardized Occupation, these unique job titles are matched to our expansive and evolving taxonomy of more than 5,000 standardized jobs. In this example, the employees would be matched to the job title HR Business Partner, in the subdomain HR Business Partners and the domain HR Professionals.

Standardizing your data unlocks valuable and detailed insights. They create a single, common view of the distribution of jobs across the organization in order to better analyze the current situation, trends, and forecast needs. For example, you can receive answers to high-level staffing questions, such as “what is our total headcount of HR Business Partners?”, “what is the total headcount of administrative support workers?”, or “what is the total headcount of technical trades and production workers?”

Beyond your own organization, you can use standardized occupations to make comparisons to industry benchmarks. For example, you can compare resignation rates of nurses after the first 90 days of hire in your organization to the equivalent resignation rates of nurses across your industry.

Job standardization leverages machine learning and AI algorithms, which were trained on Visier’s unique data to produce comprehensive and accurate matches. However, it is possible that job standardization may not fully identify a job within the taxonomy. If this occurs, the employee will be matched to a job title called Unclassified. If the standardization can only partially identify the job, the employee will be matched to a job title called Unclassified, followed by the specific domain or subdomain of the job in parentheses.

Standardized Location

Similarly, your organization may have multiple names for locations in your data. For example, let’s say some employees are located in South London, while others are located in Main London. Using Employee Standardized Location, these unique location names are matched to Visier’s taxonomy of standardized location names. Similar to standardized occupations, this unlocks the potential for structured analysis and the ability to compare to industry benchmarks. For example, you can use standardized location to look at industry benchmarks of resignation rates in the local labor market.

Manually override or create standardization matches

Automatic matches occur when our data enrichment services identify a high confidence score between your data and the standardized data. While standardizing your data, our data enrichment services may not find a sufficient match. Administrators can override automatic matches and manually create standardization matches. For more information, see Override a Standardization Match.