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“Applying AI algorithms to analytics will prove transformative, but the complex merger requires a roadmap,” the report stresses.      
8 top artificial intelligence and analytics trends for 2019 | David Weldon | HealthData Management

Instead of long investigations and analysis through multiple business Key Performance Indicator (KPI) dashboards and making manual correlations, business analysts can rely on AI analytics to probe deeper into the data and correlate simultaneous anomalies, revealing critical insights into operations. A real-time, large-scale automated anomaly detection system using machine learning methods can free data analysts from constant manual monitoring around just a few KPIs. When working with thousands or millions of metrics, you can’t just hire a staff of thousands of analysts to analyze your data for key decisions. Using automated significance ranking of detected anomalies, data analysts can focus in on the most important business incidents. In the Automation Age: Use AI Analytics to Escape ‘Business KPI Dashboard Hell’ | Ira Cohen -anodot

Augmented Analytics

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An augmented analytics engine can automatically go through a company’s data, clean it, analyze it, and convert these insights into action steps for the executives or marketers with little to no supervision from a technical person. Augmented analytics therefore can make analytics accessible to all SMB owners. Augmented Analytics Demystified | Bill Su - Medium

figure-fig1_W640.jpg Mapping the Field of Algorithmic Journalism | Konstantin N. Dörr