Technology is here to make our lives easier. NLP, for example, is a tool that allows companies to have thousands of reviews analyzed in a single day. NLG enables machines and humans to communicate seamlessly, simulating human to human conversations. And these are just 2 examples of many different technologies that are starting to be in the day-to-day of marketers, product managers, customer care, and other professionals.
In this video , our CEO Riccardo Osti explain the concept of Augmented Analytics and the usage managers can apply to it in a couple of minutes.
After watching this video, you will understand how Augmented Analytics can help you perform your job, by automatic processes allowing you to focus on the decision making. If you have questions about the content, feel free to contact us at hello@wonderflow.co.
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.
