Definition
Augmented analytics is the use of statistical and linguistic technologies to improve data management performance, from data analysis to data sharing and business intelligence. It is somehow connected to the ability to transform big data into smaller, more usable, datasets.
However, in this case, the main focus of augmented analytics stays in its assistive role, where technology does not replace humans, but supports them, enhancing our interpretation capabilities.
How does it work
Data analytics software with augmented analytics makes use of machine learning and NLP to understand and interact with data as humans would do but on a large scale. The analysis process often starts with data collection from public or private sources. You can think of the web or of a private database.
After data is gathered, it needs to be prepared and analyzed in order to extract insights, that should be then shared with the organization, together with action plans to do something with the learning.
Who does it
All these tasks are usually performed by data scientists, who spend 80% of their time on collection and preparation of data, and just the remain 20% on finding insights. The goal of augmented analytics is to automate the processes of data collection and data preparation in order to save data scientists 80% of the time. However, the real, ultimate goal of augmented analytics is to completely replace the data science teams with AI, taking care of the entire analysis process from data collection to business recommendations to decision makers.
To make it very clear, you could imagine asking the augmented analytics tool to find online reviews about one of your products and tell you what you should improve to sell more of it, having the machine responding to you with a clear textual answer and some compelling charts.
Making the Impossible Possible
Data Cognition Engines will change the world of Big Data and analytics forever, giving users unprecedented abilities to work with these immense datasets. First off, they open the door for interactive data exploration with millisecond response times, only querying the more expensive, slower Big Data system directly when very precise detail is required. They also compress terabytes of data into a model that occupies less than 5 megabytes for each terabyte. Once in place, the tiny DNNs require no access to the underlying data, eliminating the need for storage, processing power, and bandwidth.
Additionally, since systems like this don’t retain any knowledge about the lowest level of detail in the data, there’s zero risk that queries could return sensitive data that is prohibited by an organization’s policy or regulatory compliance requirements. In cases where the row-level detail is not needed or cannot be stored, DNNs can completely replace Big Data repositories with a nearly accurate solution that will satisfy most analytic needs. For organizations with Big Data who want to work with their datasets quickly and easily, this is a game changer.
