What is Augmented Intelligence?
Augmented intelligence is an approach that automates insight-finding by incorporating natural language processing and machine learning to create better data preparation methods and facilitate data-sharing.
This analytics paradigm also makes data easier to understand by presenting fully parsed and clear results, as well as giving users access to tools that let them make better everyday decisions for their business.
As part of the augmented intelligence philosophy, this analytics paradigm is designed to simplify how businesses absorb new insights from clients.
Thanks to the use of natural language processing, businesses will be able to incorporate data points gathered from conversational cues such as questions, prompts, and requests made in natural language from mobile devices and virtual assistants like Apple’s Siri and Amazon’s Alexa.
The use of natural language processing means businesses can also interact with their data in a more organic way, framing questions in regular language as opposed to computer code or complex queries.
Data analytics software is starting to integrate augmented intelligence and analytics because of its ability to quickly and effectively cut through large data sets and deliver timely insights that can improve companies’ decision-making abilities.
Augmented analytics tools can learn to spot different trends and return BI insights that are both actionable and more relevant to immediate needs. Importantly, they also handle the heavy lifting of scrubbing large data sets, parsing data, and returning clear insights that are ready for use.
What Can I Do With Augmented Analytics?
One of the biggest areas where augmented analytics can aid businesses is providing low-cost results that retain an elevated level of accuracy and quality. For small businesses, a data analyst may be out of the picture, but robust augmented intelligence tools cost a fraction of the price and deliver results quicker.
Moreover, augmented analytics systems will improve over time, meaning companies can use them more consistently to produce better and relevant insights all the time.
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A major use case for these intelligence tools is gathering data from non-traditional sources such as voice searches and mobile interactions from smartphones, as well as information gathered from virtual assistants.
On the business side, they can also make it easier for teams to ask questions in an uncomplicated way. Instead of having to program their questions into computer code, staff can simply ask questions in a natural language, and receive answers in kind.