Difference between revisions of "Analytics"

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* [[Natural Language Processing (NLP)]]
 
* [[Natural Language Processing (NLP)]]
 
* [http://thenextweb.com/podium/2019/12/30/2020-will-hopefully-be-the-year-ai-kills-company-dashboards/ 2020 will be the year AI kills company dashboards | Derek Steer - TNW (TheNextWeb)]
 
* [http://thenextweb.com/podium/2019/12/30/2020-will-hopefully-be-the-year-ai-kills-company-dashboards/ 2020 will be the year AI kills company dashboards | Derek Steer - TNW (TheNextWeb)]
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* [http://www.forbes.com/sites/forbestechcouncil/2019/07/23/will-ai-kill-the-dashboard/#196615a1cb5b Will AI Kill The Dashboard? | Ashok Santhanam - Forbes]
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* [http://www.eckerson.com/articles/beyond-the-dashboard-how-ai-changes-the-way-we-measure-business Beyond the Dashboard: How AI Changes the Way We Measure Business | Wayne Eckerson]
  
 
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. [http://www.anodot.com/blog/ai-analytics-will-replace-kpi-dashboards/ In the Automation Age: Use AI Analytics to Escape ‘Business KPI Dashboard Hell’ | Ira Cohen -anodot]
 
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. [http://www.anodot.com/blog/ai-analytics-will-replace-kpi-dashboards/ In the Automation Age: Use AI Analytics to Escape ‘Business KPI Dashboard Hell’ | Ira Cohen -anodot]

Revision as of 10:42, 31 December 2019

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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