Trusaic runs thousands of tests on HR data to identify data quality issues across five key dimensions: completeness, validity, consistency of information across data silos and between data fields, and anomaly detection.

There are four core data silos that we focus on: Payroll, HR, time and attendance, and health and benefits. Our patent supported Workforce Analytics Machine® (WAM) technology quickly and easily extracts workforce data from structured and unstructured data sources and works with any payroll or HR system.

Workforce Analytics Machine® (WAM) AI algorithms leverage two patents and over 20 years of operating experience in data quality management to automatically detect errors and root cause so that we can correct or instruct our clients what to repair at the data field level. Our machine learning algorithms improve the technology by improving the statistical models used to determine whether an anomaly is signal or noise.

HR technology is primarily focused on siloed systems based on inputs and outputs that are often prone to human error. Trusaic has devoted decades developing proactive issue detection technology and has the ability to analyze and clean your data across all platforms.

It’s expensive and time consuming for organizations to make decisions based on missing or inaccurate data. IRS penalties, employee turnover and poor reputation are just some of the risks your organization faces when running analytics on dirty data. We can help you improve ROI, customer retention and satisfaction, and we only charge for issues found.

If you are a midmarket to enterprise level organization or an HR platform and would like help with data quality, simply contact us for a complementary data quality assessment.