DQM1.0 - Diagnostic assessment
A diagnostic assessment to prioritize high-value business processes that suffer from data quality issues and strategize around critical pain points.
DQM2.0 - Statistical Process Control Framework
Each data quality process is redesigned to quantify the impact of data quality issues using a Statistical Process Control Framework with continuous monitoring and reporting against SPC measures.
DQM3.0 - ML-driven Automation
ML-driven bots are developed to manage data quality issues through Data Intelligence, which handles different data quality processes and drives applications for multiple business users.
DQM4.0 – Universal management with Analytics Center of Excellence
The task of managing a swarm of data quality bots across the enterprise is consolidated and normalized in a centralized Mastech InfoTrellis ACoE that renders Operational Intelligence.