Online ISSN: 2515-8260

Data Visualization for Weather Forecasting Using Power BI

Main Article Content

Dr.M.Rajaiah,B.Sravani,Mr.P. Vivekananda Reddy,Ms.R.Rushika,Mr.S.Tharun,Mr.S.Naveen Kumar

Abstract

Power BI has taken the world of business intelligence, data visualization and analytic s by storm. Power BI is an online service that enables searching data, transforming it, visualizing it, and sharing the developed reports and dashboards with other users in the same or different departments/organizations or even with the general public. As of February 2017, more than 200,000 organizations across 205 countries are using Power BI. Power BI is having a free option that has adequate features and functionality, it has become a serious contender for use as a business intelligence platform in small and medium organizations. One of the innovative features of Power BI is its Quick Insights feature (Michael Hart, 2017) that is built on a growing set of advanced analytical algorithms. After uploading a data set to Power BI, a click of a button can be used to invoke this feature that automatically builds many reports based on its analysis of the data, without any human intervention being required. This also helps reduce human errors in calculations and statistical techniques, which lead to un-verifiable research. Accepting even Excel spreadsheets as input, Power BI is easy to use and ripe for adoption as a platform for Research Data Analysis. In this paper, an attempt has been made to show how easily a data set of research data can be transformed by Power BI into a set of analytical reports and dashboards, and whichcan be shared with ease. Turn your data into a competitive advantage by using Power BI and Azure together to connect, combine, and analyze your entire data estate. Enable business analysts, IT professionals, and data scientists to collaborate seamlessly, providing a single version of data truth that delivers insights across your organization. "Now we have a single consolidated source of truth that everyone uses, and we can increasingly automate our analysis for a deeper dive into the intricacies of the data."

Article Details