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As analyst and journalist Bernard Marr put it, “Without data scientists on staff or available to interpret data and turn the intel into solid business activity, the benefits of data could remain unlocked.” Augmented analytics promises to improve the ability of organizations to derive benefits from data by:
- Taking more mundane analytics from Data Science teams so they can focus on more complex problems
- Shortening the data analytics lifecycle
- Giving decision-makers fast access to actionable intelligence
At a high level, augmented analytics involves using machine learning and natural language processing (NLP) to assist with data prep, analysis, and reporting, eliminating the things that slow down analytics. But it promises to do more than just that – augmented analytics also involves rewriting the entire analytics and BI workflow.
Read the full article at Dataversity