Augmented Analytics Is the Future of Data and Analytics

 

Augmented analytics, an approach that automates insights using machine learning and natural-language generation, marks the next wave of disruption in the data and analytics market. Data and analytics leaders should plan to adopt augmented analytics as platform capabilities mature.

 
Key Findings

 

  • Augmented analytics is a next-generation data and analytics paradigm that uses machine learning to automate data preparation, insight discovery and insight sharing for a broad range of business users, operational workers and citizen data scientists.
  • Augmented analytics will enable expert data scientists to focus on specialized problems and on embedding enterprise-grade models into applications. Users will spend less time exploring data and more time acting on the most relevant insights with less bias than is the case with manual approaches.
  • Both small startups and large vendors now offer augmented analytics capabilities that could disrupt business intelligence (BI) and analytics, data science, data integration and embedded analytic application vendors. Data and analytics leaders must therefore review their investments.
  • As augmented analytics tools and capabilities become more accessible, data and analytics leaders will need to adopt new approaches. They will also have to develop a strategy to address the impact of augmented analytics on currently supported data and analytics capabilities, roles, responsibilities and skills, and increase their investments in data literacy.

 

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