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Enterprise Intelligence Hub: Unlocking Trusted, Actionable and Prescriptive Business Insights
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Connecting the Dots: Data, Artificial Intelligence, and Cloud
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What it means to be an Analytics Advisor in today's data and computational environment
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Digital Twins with Enterprise Intelligence Hub
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Architecting enterprise intelligence

Transform Enterprise Data into Business Intelligence. Make intelligent business decisions armed with rich insights from your data.

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Data Science Advanced Analytics

Supercharge your enterprise decision-making with Advanced Analytics

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Create a competitive business environment with AI-driven decisioning systems

Take your Analytics game to the next level with cognitive networks and Graph analytics. Resolve critical, urgent, and recurring pain points with AI-driven decisioning systems that learn from historical data and predict future inconsistencies. Leverage the power of AI Accelerators to arm your enterprise with data mining capabilities, deep learning, intelligent automation, and computer vision analytics for enhanced predictive models. Explore vast data sets quickly by training bots, and turbocharge investigative analytics using Data Science practices like cognitive networks and Enterprise Knowledge Graphs.

Solve strategic business problems with AI-powered advanced analytics
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Customer Experience Excellence

Explore outcome scenarios, next-best-action programs for CRM with simulations run using enterprise-wide data.

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Risk Mitigation Excellence 

Mitigate risks, system vulnerabilities by detecting early warnings using Knowledge Graph analytics and intelligent automation.

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Revenue Excellence

Discover revenue opportunities from existing resources or alternative data using Knowledge Graphs.

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Operations Excellence

Identify and act on simulations to optimize operations quality, quantity, and turnaround time.

The Mastech InfoTrellis Methodology for Advanced Data Science Analytics
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Stage 1 - Define Business Problem and Data

Define business problems and data challenges that help analytics advisors conduct an assessment and strategy workshop for determining analytics needs.

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Stage 2 – Data preparation

Prepare data and establish benchmarks for qualifying new predictors with our expertise and AI Accelerators.

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Stage 3 - Content Tagging and Training Iterations

Improve model accuracy and reliability of features through a continuous learning, evolution model and intelligent automation.

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Stage 4 – Validation

Perform model tuning, testing, learning to modify processes, and refine approaches for better outcomes.

Step up with Advanced Analytics
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Graph Analytics

Power your decision-making with analytics solutions that include self-learning and semantic tagging for advanced predictions.

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Smart Automation

Maximize customer experience excellence by driving self-learning service desks and intelligent automation tools.

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AI-driven Simulation

Make better decisions regarding products, pricing, customers, supply chains, operations, and organization-culture with AI-powered Data Science practices.

Let's talk about what our Data Science offering can do for your business

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