Augmented analytics enables machine learning and AI assisted data preparation, insight generation, and insight explanation to augment how business people, analysts, explore and analyze data in analytics and BI platforms. It also augments the expert and citizen data scientists by automating many aspects of data science and ML model development, management and deployment.

By 2020, augmented analytics will be a dominant driver of new purchases of analytics and business intelligence as well as data science and machine learning platforms, and of embedded analytics.

Gartner Predicts

What does Augmented Analytics Enable?

  • Augmented analytics automates aspects of finding and surfacing the most important insights or changes in the business (in a user’s context) to optimize decision making. It does this in a fraction of the time, with less data science and ML skills, and without prior knowledge of the relationships in data that is required when using manual approaches.

  • Augmented analytics uses ML/AI techniques to automate key aspects of data science and ML/AI modeling, such as feature engineering and model selection (autoML), as well as model operationalization, model explanation and, ultimately, model tuning and management. 

  • Many autogenerated and human-augmented ML models created through augmented analytics are being embedded in enterprise applications This helps to optimize the decisions and actions of all employees, not just those of analysts and data scientists.

  • Augmented analytics capabilities are advancing rapidly to mainstream adoption, as a key feature of data preparation, broader data management, modern analytics and BI as well as data science and ML platforms.

  • Augmented analytics can also be deployed with NLP and conversational interfaces (also a top 10 trend), to allow more people across the organization to interact with, make predictions and get actionable from data and insights without requiring advanced skills. It will make deep insights available to people who do not have the skills or access to ask their own questions from analytics and BI platforms.

How Does This Impact Your Organization and Skills?

  • Augmented analytics will democratize insights from analytics (including AI) to all business roles. While this will reduce the reliance on expert analytics, data science and ML skills, this trend will require an increased focus on data literacy across the organization.

  • Augmented analytics reduces the requirement for specialized data science and ML skills to generate, operationalize and manage an advanced analytics model. It also opens up data science and ML content creation to “citizen data scientists” (including business analysts and application developers who must embed ML/AI into applications). This makes expert data scientists more productive and collaborative, freeing them for high-value tasks. Putting in place processes to promote collaboration across roles leveraging augmented analytics capabilities will be critical to success.

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Relevant Sessions

  • Augmented Data Catalogs: Now an Enterprise Must-Have for Data and Analytics Leaders
  • The Future of Analytics and BI: The Augmented Consumer
  • AI Talent: Recruiting, Hiring, Organizing, Training and Retaining
  • From Self-Service to Enterprise to Augmented Data Preparation — Understanding the Data Preparation Tools Market
  • Magic Quadrant for Analytics and BI and Data Science and ML

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