Business Analytics with R | Capabilities

Posted by radha on November 19th, 2020

Business Analitics with R :The business world is facing fastinnovation and change. The traditional the business management function is gone through a wave of transformation with in terms of its application and functions. Today, analytics is considered key resource in the operational and strategic of a companyactivities. All leading organizations are gaining power quickly taking advantage informationto gain insight and drive deal. Business analytics provides business performance evaluation also as futuristic predictions of company operations based on existing data and statistical methods. Today, business analysts providecritical insights on how to analyze data and come up with an important policy recommendations.
Easy-to-use software packages, ranging from best known as the MS Excel to the most advanced as R are being widely used by academics and business professionals to facilitate business
data analysis. R is a language and an environment for statistical computing and graphics that gained wide acceptance in the industry and academy as leading analytical software.

R analytics (or R programming language) is free open source software used for all kinds of data science, statistics and visualization projects. The R programming language is powerful, versatile AND can be integrated into BI platforms like Sisense, to help you get the most out of business-critical data.

These integrations include everything from statistical functions to predictive models, such as linear regression. R also allows you to create and run statistical models using Sisense data, automatically updating it as new information flows into the model.
Analytics is a journey that involves a combination of potential skills, advanced technologies, applications and processes used by a company to obtain business insights from data and statistics. This is done for business planning.

In the analysis process, data is collected from various external sources and stored in a data warehouse. After this data collection, the analysis process begins. Here, a deeper learning of the data is done to derive possible insights from the data. In addition to this, all advanced data-driven capabilities are developed, which in turn provides additional value to the business. 

Capabilities:

• It is a practical use of statistical analysis that focuses on making meaningful proposals.
• It should allow the user to perform analytical operations through an intuitive interface without the use of coding or programming.
• It provides a competitive advantage to companies by combining available data with various well thought out models so that business decisions can be improved.
• It converts the available data into valuable information that can be presented in any required format, comfortable for the decision maker.
• It helps improve business profitability, increase market share and revenue, and provide better returns to shareholders.
• It also fosters a superior understanding of the primary and secondary data available, which again influences the operational efficiency of some departments.
• It also has a flexible option for business users from commercial providers like Revolution Analytics (which support 64-bit Windows and now Linux), as well as big data processing through its RevoScaleR package.

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