Augmented data analytics overshadows traditional BI process
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Data growth, citizen data scientists spur push to augment BI
Augmented analytics is a new technology category outlined by Gartner in 2017, consisting of various technologies that are being added to BI and analytics software to help users find, prepare and analyze data. The consulting company predicted that, by 2020, support for augmented data analytics will become "a dominant driver" of buying decisions for both BI and data science platforms.
The push to augment BI tools with automated data discovery and analysis capabilities is being driven by two trends: the skyrocketing growth of corporate data volumes and the rise of so-called citizen data scientists in business units. That combination makes augmented analytics "crucial for presenting to operational users only what is important for them in the context to act upon at that moment," Gartner analyst Jim Hare said in a February 2019 interview with TechTarget reporter Brian Holak.
But it's still early days for the augmented approach, with vendors offering a bevy of products that have yet to be put to the test by many users. This handbook provides further insight on the emergence of augmented analytics.
First, we recount advice on how to effectively deploy augmented technologies -- and overcome their current limitations. Next, we look more deeply at Gartner's take on augmented analytics and other notable data trends. We close with an overview of how BI vendors are incorporating augmented data analytics features into their products.