Augmented analytics, automated tools facilitate data analysis
Augmented analytics sounds futuristic — and BI and advanced analytics vendors are increasingly moving to make it a here-and-now reality, along with automated machine learning, search-based querying and other technologies that have a common goal to simplify and streamline the analytics process for users.
In particular, augmented analytics and its cohorts are designed to make it easier for business users and the growing population of so-called citizen data scientists — business analysts and other workers who lack formal data science training but are building predictive models themselves — to drill down into data sets and gain valuable insights from BI and analytics applications.
These emerging technologies do arrive at the same point, but they come at it from different directions. Augmented analytics — or augmented intelligence, as some vendors characterize it — embeds machine learning algorithms and other advanced analytics functionality into BI tools to point users toward relevant data, help them prepare it for analysis and aid in creating data visualizations. On the other hand, automated machine learning platforms provide under-the-hood functionality to help analysts with limited expertise build and train machine learning models for more-advanced data mining and predictive analytics uses.
But while the simplification push encompasses a broad set of tools and approaches, it all boils down to enabling users — especially less tech-savvy ones — to uncover information faster than before. These technologies also pave the way for more people to get involved in analytics initiatives and can be “a big productivity booster” for organizations, TDWI analyst Fern Halper wrote in a September 2017 report on advanced analytics trends.
According to Gartner, augmented analytics includes a combination of natural language processing (NLP), augmented data preparation, automated analytics and visual-based data discovery. As a whole, those capabilities have yet to become a mainstream factor in software buying decisions, Gartner said in its 2018 “Magic Quadrant” report on BI and analytics platforms.
But the consulting and market research firm predicted that augmented analytics, by 2020, “will be a dominant driver of new purchases” of BI tools, embedded analytics software, and data science and machine learning platforms. And by then, Gartner expects 50% of analytical queries to be automatically generated or created through searches, NLP applications or voice commands instead of conventional coding.
Longer term, Gartner sees technology-assisted analytics becoming even more prevalent. “Ten years from now, probably, you won’t find a single business application that isn’t enabled in some way by machine learning and AI, and analytics is no different,” Gartner analyst Rita Sallam said in a recent webinar on augmented analytics.
Some users are already on board. In a TDWI survey conducted in June 2017, 27% of 222 respondents said their organizations were using analytics tools that suggest data visualizations to users. And in another survey done by TDWI in January 2018, 16% of 387 respondents said their organizations had deployed automated predictive modeling software, while an additional 40% said they expected to start using it in the next few years.
Chevron Corp. is an early adopter of Google’s Cloud AutoML technology, which is designed to help users with limited machine learning expertise build and train analytical models. Earlier this year, a seismic processing and imaging team at Chevron used the alpha version of an AutoML Vision image analysis tool to help analyze internal documents as part of the process of evaluating new opportunities for oil drilling, according to Laura Bandura, a research geophysicist who works on the team.
