Data Dive #4: Augmented Analytics and Automation 🤖
Thanks to machine learning, automation, and natural language generation (NLG), data scientists are no longer the only ones who can identify business trends using predictive analytics.
Augmented Analytics is an approach to data analytics that automates analysis processes customarily done by a specialist or data scientist.
Thanks to machine learning, automation, and natural language generation (NLG), data scientists are no longer the only ones who can identify business trends using predictive analytics.
One of the hottest BI trends is automation, which accelerates delivery pipelines as analytics scale up with larger and more complicated data volumes. The speedier ML-driven algorithms offer automated search responses and start event-driven workflows. With some basic technical knowledge, anyone can conduct data discovery and analytics.
However, automation brings additional costs, reports, and duplication, which, if not monitored correctly, can do more harm than good. Here, you need a platform that helps you achieve the desired level of efficiency within your BI systems. (Say hello to Datalogz!)
Business intelligence is derived from any information source imaginable, such as website visits, IoT-enabled machine monitoring, consumer feedback, application interactions, and more. It all comes together through application integration, which relies on APIs to aggregate insights as we link to other BI, ERP, and CRM systems without a hitch.
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