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Data Quality Intelligence

Scale Data Quality with AI-powered automation

Data Quality Intelligence to meet modern data-driven enterprise needs

Meet modern data-driven enterprise needs and establish robust Data Quality processes powered by AI and Intelligence Architecture. The unbridled growth of data and the complexity of data-driven applications has led to an exponential cost-curve to maintain data quality pipelines that can only be scaled through AI-powered automation. Create a comprehensive Enterprise framework for AI-powered Data Quality with our multi-stage Data Quality Management (DQM) process. 

Transforming Data Analytics

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A phased approach for Data Quality automation

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.

Meet multiple data quality challenges

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Data Quality Intelligence offerings for varied challenges
Delight customers with a holistic Customer Experience strategy across the enterprise
IT Ops

Eliminate service quality and internal business user experience issues from an IT point of view with our efficient automated systems and intelligence infrastructure.


Source and reduce data errors that directly/indirectly impact revenues, costs, services, or customer experiences with robust Data Intelligence systems in place.

Analytics and Data Science

Drive better and more reliable insights, improve performance, minimize bias, mitigate data & model assumption mismatches with Operational Intelligence and proven Data Science tools.

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