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The Important Components of Augmented Analytics

When it comes to the new world of analytics, the augmented analytics approach allows business users with no data science background to readily access and use analytics in an intuitive way. There are some important aspects of this approach, including auto machine learning, natural language processing (NLP) and intuitive search analytics.

 

Machine Learning via AutoML allows users to leverage systems and solutions that are designed with Machine Learning capabilities to predict outcomes and analyze data. Auto ML is the automated process of features and algorithm selection that supports planning, and allows users to fine tune, perform iterative modeling, and allows for the application and evolution of machine learning models. Machine Learning Algorithms allows the system to understand data and applies correlation, classification, regression, or forecasting, or whichever technique is relevant, based upon the data the user wishes to analyze. Results are displayed using visualization types that provide the best fit for the data, and the interpretation is presented in simple natural language. This seamless, intuitive process enables business users to quickly and easily select and analyze data without guesswork or advanced skills.

 

With natural language-processing-based search capability, users do not need to scroll through menus and navigation. The business can address complex questions using this simple search capability with a contextual flexible search mechanism that provides one of the most flexible, in-depth search capability and results offered in the market today.

 

Clickless Analysis and contextual search capabilities go beyond column level filters and queries to provide more intelligence support and translate the contextual query and returns results in an appropriate format, e.g., visualization, tables, numbers, or descriptors. It takes natural language processing search analytics (NLP) and predictive modeling for business users to the next level and frees business users to produce accurate, clear results, quickly and dependably, using machine learning that frees the business user to collect and analyze data with the guided assistance of a ‘smart’ solution.

 

This foundation and these techniques come together to enable the enterprise and its business users to perform complex data analytics and share analysis across the organization in a self-serve, mobile environment. It brings the power of sophisticated, advanced analytics and smart data visualization to the next level with tools for automated data insights.

 

If you want to make encourage your business users to adopt and leverage the Clickless Analytics approach to NLP search analytics, and capitalize on intuitive Search Analytics and Auto Insights features that improve results and user adoption, Contact Us to get started. Read our Blog to find out more about Clickless Analytics and Natural Language Processing.

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Tags: dsc_analytics, dsc_ml

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