Best Practices in Augmented Analytics

Best Practices in Augmented Analytics

Techniques for Driving Adoption

Augmented analytics uses artificial intelligence (AI) to make business intelligence (BI) and analytics tools easier to use to generate insights not possible with earlier generations of products. However, this doesn’t mean all business users will universally adopt all the new features. Analytics leaders need to understand the target audience for these features before rolling them out broadly.

To ensure widespread adoption, data leaders need to populate the tools with timely, relevant, and high quality data. BI and analytics tools can be unfairly tarnished if business users don’t trust the data. Leaders also need to provide adequate training and coaching to ensure business users get the support they need to understand and utilize the new features. Finally, BI administrators need to test the new features for scalability, performance, and ease of use.

This report shows how data and analytics leaders can increase adoption of augmented analytics capabilities. It presents an Analytics Adoption Framework that describes the major factors that contribute to widespread adoption of BI and analytics tools and features. It applies those factors to the implementation of augmented analytics, providing a guide for adoption.

Download the report below.