Scale Data Quality with AI-powered automation
Data Quality Intelligence evolves existing Data Quality initiatives at a client to meet a modern data-driven Enterprise needs. 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. Mastech InfoTrellis’ multi-stage Data Quality Management (DQM) process helps create a comprehensive Enterprise framework for AI-powered Data Quality.
A phased approach for Data Quality automation
Mastech InfoTrellis uses a phased approach to apply AI automation, enterprise-wide, across all data quality initiatives, where applicable.
DQM1.0 – A diagnostic assessment to prioritize high-value business processes that suffer from data quality issues.
DQM2.0 – Individual data quality processes are re-designed using a statistical process control (SPC) framework to rigorously quantify the impact of data quality issues in a pipeline. The objective is to identify and map a data quality issue and construct a data quality process. To do this requires the implementation, monitoring, and reporting against SPC measures deployed across an end-to-end quality process.
DQM3.0 – ML-driven bots are developed to manage data quality issues through automation throughout the enterprise on a rolling schedule, based on incremental business value. A portfolio of ML-driven data quality bots is developed to handle different data quality processes as part of a managed service. These bots are deployed across multiple enterprise-wide quality processes to drive multiple applications for various audiences (e.g., IT, Business, Data Science). Since these bots will need to manage and improved continuously over time, they will be delivered through a Mastech InfoTrellis Analytics Center of Excellence (ACoE).
DQM4.0 – The task of managing a swarm of data quality bots across the enterprise is consolidated and normalized in a centralized Mastech InfoTrellis ACoE.
Meet multiple data quality challenges
- IT-Ops – Eliminate service quality and customer experience issues from an IT point-of-view. Here, “customer” often translates to other parts of your business.
- Business-Ops – Source and reduce data errors that directly/indirectly impact revenues, costs, services, or customer experiences.
- Analytics & Data Science – Drive Better and More Reliable Insights, Improve Performance, Minimize Bias, Mitigate Data & Model Assumption Mismatches, and so on.