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Augmented Analytics

Last updated: July 19, 2021

What Does Augmented Analytics Mean?

Augmented analytics refers to analytics processes that are enhanced by artificial intelligence (AI), machine learning (ML) and deep learning technologies.

An important goal of augmented analytics is to allow non-technical line of business (LOB) professionals to write queries in plain English (instead of SQL) and make data-driven decisions without needing help from their organization's data scientists or machine learning engineers (MLEs).

Augmented analytic is often a key competitive differentiator for self-service business intelligence (SSBI) platforms.

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Techopedia Explains Augmented Analytics

One of the main areas of capability empowered by augmented analytics is the democratization of big data handling and related insights. Augmented analytics platforms can clean, merge and transform data from multiple enterprise resource planning (ERP) systems. This allows less tech-savvy users to become analysts and citizen data scientists.

Proponents of an augmented analytics approach make the argument that there is a disconnect between business relevance and technical skills. Since data is becoming the most crucial asset for many companies, it makes sense to have more stakeholders empowered to use that data to drive business forward.

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