Augmented analytics capabilities are disrupting analytics, BI, data science and machine learning markets. Tools leverage ML/AI to transform how analytics content is developed, consumed and shared. Data and analytics leaders should plan to adopt augmented analytics as capabilities mature.

Key findings:

• Augmented analytics use machine learning (ML) to automate data preparation, insight discovery, model development and insights sharing for a broad range of business users, operational workers and citizen data scientist.
• Augmented analytics tools can identify the most insights, based on statistical significance, and, in more advanced tools, users’ preferences and business context/relevancy (location, role, time, etc.).
• Analytics and business intelligence (ABI) and data science and machine learning (DSML) solutions and platforms often complement augmented capabilities with natural language processing (NLP) and conversational interfaces, allowing all users to interact with data and insights without requiring advanced skills.

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