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Augmented analytics refers to analytics processes that are enhanced for the 21st century. It specifically refers to analytics that are powered by machine learning or similar technologies.
The idea behind augmented analytics is that companies and stakeholders can use machine learning, deep learning and artificial intelligence to improve the analytics process or add capability.
One of the main areas of capability empowered by augmented analytics is the “democratization” of big data handling and related insights. In other words, with more autonomous designs, less tech-savvy users can perform augmented analytics and become analysts, or in the words of some experts, “citizen data scientists.”
Proponents of an augmented analytics approach talk about a more “egalitarian” business tech world. There is a sort of consensus that since data is becoming the most crucial asset to companies, it makes sense to have it be handled in greater sets of hands – to have more people empowered to use that data to drive business forward. Experts make the argument that the best programmers and engineers are not often tied to the heart of the business goals, so that there is a sort of disconnect between skill and relevance. In that sense, augmented analytics is expected to be a game changer in the coming years.