Augmented Analytics Is the Future of Analytics

Augmented analytics leverages machine learning and AI techniques to transform how analytics content is developed, consumed and shared. Data and analytics leaders should plan to adopt augmented analytics as platform capabilities mature.

Key  Findings

  • Augmented analytics in ABI platforms (augmented ABI) transforms and democratizes how business people explore, analyze and act on insights by surfacing key insights in data through ML and AI assisted data preparation, insight generation and insight explanation. 
  • By automating many aspects of DSML development, management and deployment augmented analytics in DSML platforms (augmented DSML), expert data scientists become more productive. This also extends DSML model building to a broader range of less skilled users including new citizen data science roles (business analysts, developers and others). 
  • Explainability is increasingly becoming an important capability to give users confidence in using machine-generated insights and recommendations. 
  • Natural language interfaces such as search via text and voice combined with augmented analytics make analytics more accessible and consumable by a broader set of users.

Gartner recommends a data and analytics leader planning to use augmented analytics to modernize solutions to:

  • Find opportunities to complement existing data and analytics initiatives by piloting augmented analytics for high value, but time-consuming, manual analysis. 
  • Build trust in augmented DSML models by fostering collaboration between expert and citizen data scientists to back test and prove value. 
  • Understand the limitations of machine-assisted models, which work best with proven algorithms versus cutting-edge techniques. 
  • Monitor the augmented analytics roadmaps of established data and analytics providers, enterprise application vendors and startups. Assess upfront setup, data preparation, openness and explainability of insights and models, range of algorithms, and model accuracy. 
  • Plan for new roles and expand data literacy skills to support wider adoption due to augmented analytics by people who don’t currently make decisions based on insights from analytics and BI platforms or from data science and ML models.

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