The next step in analytics — augmented analytics — brings automation and new degrees of insight into an analytics initiative.
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.
Business intelligence plus artificial intelligence
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 articleprovides 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.
Compelling reasons to pilot
Gartner estimates that augmented analytics will grow at twice the rate of those that are not, and will deliver twice the business value. Analytics and BI leaders should begin planning for augmented analytics pilot projects and adoption. Notably, augmented analytics complements rather than replaces existing enterprise BI, self-service BI and data science platforms. Augmented analytics can also be embedded into line of business applications to improve decision-making processes.
As the saying goes, seeing is believing. Analytics automation might initially be resisted out of fear and reasonable skepticism. To understand augmented analytics’ potential, try these solutions with your own data. Validate identified insights and review recommended alternatives. The evaluation exercise should provide rapid value while concurrently educating and enlightening your team.
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