By Kaycee Lai | September 7, 2021

Organizations are collecting more and more data. But most of the insights it may contain will remain inaccessible, unless it is purposefully accessed by a data scientist or analyst. Considering the difficulty in hiring data scientists, this leaves companies in a bit of a bind. Furthermore, even companies with robust analytics teams find that it simply takes too long to get the insight they need. To remedy this situation, a new concept has emerged: augmented analytics.

According to Gartner, augmented analytics involves leveraging machine learning and artificial intelligence to expedite the processes of data preparation, insight generation and insight explanation through business intelligence (BI) platforms. In this way, it also augments the capabilities of not just the data scientist or analyst, but also the ‘citizen data scientist’—the non-analyst who seeks understanding through data—by automating many aspects of the analytics process.

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