Augmented Analytics Drives Next Wave of AI, Machine Learning, BI
Business intelligence will move beyond dashboards, and AI and machine learning will become easier for less skilled workers as augmented analytics are embedded into platforms.
Enterprises struggling to get their data management and machine learning practices up to speed in an era of more and more data may be in for a nice surprise. After years of bending under the weight of more data, more need for insights, and a shortage of data science talent, augmented analytics is coming to the rescue. What’s more, it could also help with putting machine learning into production, something that has been an issue for many enterprises.
Identified as a major trend by Gartner at its Symposium event last year, augmented analytics has been around for several years already, according to Rita Sallam, distinguished research VP and Gartner fellow. But in recent years the concept has expanded to encompass automation of many of the processes that are required by the entire data pipeline. That includes tasks such as profiling, cataloging, storage, data management, generating insights, assisting with data science and machine learning models, and operationalization, according to Sallam, who was set to present a session about augmented analytics at the now postponed Gartner Data and Analytics Summit that has been rescheduled for September.
The trend comes in the years after business intelligence (BI) data dashboards and visualizations have become mainstream, popularized by vendors such as Tableau and Qlik. These tools provided a way for users to look at data and drill down into the information they needed to figure out what actions to take next, what areas required more focus, and how they could be more productive. Now nearly every BI vendor has this capability, Sallam told InformationWeek, and Microsoft has taken it further, offering it at a very low cost, further disrupting the market.
That’s about to evolve even more as vendors look to differentiate and solve another problem that users have.
“Data is increasingly large and complex. The variables that we need to explore and the different levels of aggregation that we need to explore is just far greater than a human brain can do,” Sallam said. As good as KPI dashboards and visualizations are, they do require a level of skill to be able to drill down and understand what the causes are and what are the best next actions to take.
Now tools are evolving further to make the whole process easier for users. Big vendors are making acquisitions to add data prep and automation into their platforms. For instance, Data Robot acquiring Paxata, and Tableau acquiring Empirical Systems.
