Analytics

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• Analytics ... Visualization ... Graphical Tools ... Diagrams & Business Analysis ... Requirements ... Loop ... Bayes ... Network Pattern
• Data Science ... Governance ... Preprocessing ... Exploration ... Interoperability ... Master Data Management (MDM) ... Bias and Variances ... Benchmarks ... Datasets
• Agents ... Robotic Process Automation ... Assistants ... Personal Companions ... Productivity ... Email ... Negotiation ... LangChain
• Bot Framework
• Large Language Model (LLM) ... Natural Language Processing (NLP) ...Generation ... Classification ... Understanding ... Translation ... Tools & Services
• Artificial Intelligence (AI) ... Generative AI ... Machine Learning (ML) ... Deep Learning ... Neural Network ... Reinforcement ... Learning Techniques
• Conversational AI ... ChatGPT | OpenAI ... Bing/Copilot | Microsoft ... Gemini | Google ... Claude | Anthropic ... Perplexity ... You ... phind ... Ernie | Baidu
• 2020 will be the year AI kills company dashboards | Derek Steer - TNW (TheNextWeb)
• Will AI Kill The Dashboard? | Ashok Santhanam - Forbes
• Beyond the Dashboard: How AI Changes the Way We Measure Business | Wayne Eckerson
• Global Edition A CIO's guide to AI dashboards | Bill Siwicki - HealthcareITNews
• The new New Relic: Past BI and the dashboard, toward AI and AIOps | George Anadiotis - ZDNet
• 5 Big Data Analytics Dashboards that will Boost Your Career in 2020 | Sudipto Ghosh - AIthority
• What is the Difference between Machine Learning and Data Analytics? | S Akash - Analytics Insight

“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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• Gartner Identifies Top 10 Data and Analytics Technology Trends for 2019 | Susan Moore - Gartner
• NLP for Analytics: It's Not Just About Text | Lisa Morgan - InformationWeek

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

Mapping the Field of Algorithmic Journalism | Konstantin N. Dörr