Augmented Analytics

According to Gartner, Augmented Analytics is «the next wave of disruption in the data and analytics market that leaders should plan to adopt». In essence, Augmented Analytics, abbreviated as AR, is an advanced data analytics solution, which embraces Artificial Intelligence, or if to be precise, Machine Learning and Natural Language Processing for data processing and analysis, as well as insights generation. In its core, AR is the next step of Business Intelligence as a service, but with more emphasis on Artificial Intelligence rather than on human processing.

To get a better view of Augmented Analytics and its business applications, you can read the Forbes article, which is written by the Softengi CTO. It will give you a better understanding of Augmented Analytics utilization on the front, back, and middle office layers.

 

 

Augmented AnalyticsSoftengi

Advantages

The most substantial advantages of Augmented Analytics

Machine Learning

The use of advanced Machine Learning algorithms allows Augmented Intelligence software to automatically process large amounts of company internal and external data.

Artificial Intelligence

Artificial Intelligence-based technology enables Augmented Analytics software to analyze extracted data, providing insights and making predictions.

Natural Language Processing

Due to embedded Natural Language Processing technology, Augmented Analytics is able to provide instant answers in natural language to users’ queries, drastically reducing reporting time and accelerating operating processes.

Some perks of using Augmented Analytics:

1.      Being an offshoot of Business Intelligence but with more emphasis on automation, Augmented Analytics helps companies to get the most out of their data with ML and NLP. With its deep data analysis capabilities, Augmented Analytics software is able to perform data-driven tasks faster and more efficiently, requiring significantly less technical competency from companies’ employees, hence reducing expenses. The software processes all types of data, identifying patterns, correlations, expected results as well as analyzing location dependency networks and market trends.

2.     Predictive Modelling

Augmented Analytics allows companies to make more accurate predictions and drive improved performances. Augmented Analytics-powered systems can analyze huge amounts of data from various sources inside and outside the organization and make accurate forecasts based on the analyzed information. As a result, the technology improves customer experience by enhancing personalization rates, product suggestions, and search recommendations.

3.     Smart Data Visualization

Augmented Analytics provides companies with smart data visualization, which transforms complex data in easy-to-understand forms such as tables, numbers, descriptions, and other visual materials. Additionally, Augmented Analytics is able to translate the query and return the results in a natural language report with comprehensive visualisations. As a result, users can easily understand complex information presented in a report, thereby saving a significant amount of time. 

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