What is Augmented Analytics?
Augmented analytics is a technology that uses statistical and linguistic technologies to improve data management performance. It mainly takes care of data analysis to data sharing and business intelligence.
When do we use Augmented Analytics?
Augmented analytics comes into picture when the dependence on data scientists needs to be relieved by a company, to automate insight generation in a company through the use of advanced Machine Learning and Artificial Intelligence algorithms.
Where is Augmented Analytics being used?
Some of the use-cases of Augmented analytics:
- Augmented Analytics are used in agriculture, to help farmers analyze the amount of water being used, soil temperature and to monitor the status of their crops.
- Smart cities use advanced augmented analytics to collect huge city data to manage resources and predict natural calamities.
- They are also used by defense pilots to communicate critical flight information in the "Heads-Up-Display" (HUD).
Why do we use Augmented Analytics?
We should use augmented analytics to allow data scientists and the IT community to focus on strategic and more meaningful work and projects. They provide better decisions and business predictions and measurable analysis of the product, pricing, promotions and other aspects of the business.
Who uses Augmented Analytics?
The tasks performed by data scientists, who spend 80% of their time on data collection and preparation of data and the 20% remaining on finding insights. The main goal of Augmented analytics is to automate the data collection and preparation processes to save 80% of the data scientists time.
How Augmented Analytics works?
Data analytics software when combined with augmented analytics, uses Machine Learning and NLP to understand and interact with data as humans would on a large scale. This process often starts with data collection from various sources (public or private), then prepares itself to analyze in order to extract insights. These insights are then shared with the organization to bring in action plans after learning of data.
Benefits of using Augmented Analytics
- Increased accuracy and speed as compared to data scientists’ effort and time
- Reduced bias that leads to overlooked insights in case of a manual process