Intuitics

Intuitics empowers business users to run advanced analytics models themselves in order to generate insights from data. Today, data scientists in large companies frequently rerun predictive models for different parameters and data. This wastes time and slows down decision making.

With Intuitics, the data team quickly turns a model into a predictive web app which anybody in the organization can use with a few clicks. Their software speeds up decision making, makes it more data-driven, and saves the data scientists time. Intuitics applications are driven by the most popular data science languages: R, SAS, and Python.

The process of converting the existing code is quick and easy. Beyond that, all you need is a mouse to create the graphical interface with drag and drop. Rerunning the same models and reports over and over? By converting them to Intuitics applications for the users. This is especially valuable nowadays, when data scientists are scarce and your work is constantly increasing.

Intuitics automatically provisions computing resources for the applications, scaling them for big data and hundreds of users. It also provides with full control over dependencies. No longer need to set up servers, involve IT, or worry about memory and performance. Intuitics web apps are pretty, intuitive, interactive and work on tablets. Through code and the drag and drop interface builder one can easily create rich functionality and a flexible layout. users will love the desktop-like experience.

Build applications collaboratively with your colleagues. app's users can share insights with each other, save application states for later reference, export to a variety of formats and even create recurring reports.

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More Information on Predictive Analysis Process

Predictive Analytics Process Flow

For more information of predictive analytics process, please review the overview of each components in the predictive analytics process: data collection (data mining), data analysis, statistical analysis, predictive modeling and predictive model deployment.