The next fundamental shift in the evolution of analytics is beginning. Similar to the second wave of self-service business intelligence disrupting the first wave of traditional BI, augmented analytics technologies in the third wave will change the game once again. Early adopters of augmented analytics tout unprecedented speed to insight and enhanced competitive advantage.
Augmented analytics uses machine-learning automation to supplement human intelligence across the entire analytics life-cycle. Last week Gartner released a report called "Augmented Analytics is the Future of Data and Analytics". If you are not a Gartner subscriber, Rita Sallam's public article provides an brief overview.
Automating analytics is not a novel idea. This old concept is vastly improving thanks to advances in artificial intelligence, search, natural language, and other modern computing technologies. Numerous vendors already suggest data visualizations, reveal outliers, embed simple forecasting and clustering within visual analytics tools. Augmented analytics delves deeper.
Next generation augmented analytics capabilities can automatically prepare and cleanse data, perform feature engineering, find key insights and hidden patterns. Automation expedites investigation across millions of variable combinations that would be too time consuming for a human to do manually. Often new discoveries are exposed in the process. Furthermore, artificial intelligence algorithms interpret results and present unbiased alternatives along with actionable recommendations.
[Read the rest of the article at InformationWeek]