12 Reasons Why You Should Do Data Analytics With Power BI

12 Reasons Why You Should Do Data Analytics With Power BI
Launched in 2011, Microsoft Power BI is a Business intelligence software used by top-level organizations to visualize and analyze data. Microsoft Power BI extracts data, performs operations, and represents it in the form of visuals. From the visualization, data can be easily customized for analysis

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Reasons to Choose Microsoft Power BI

  1. Customized Predictive Analysis

    Real-time analytics with respect to API integration and data visualization in Microsoft Power BI presents the organization with results of a set of finite past values. But gives little insightful information about further business planning.

    Microsoft Power BI performs predictive analysis for businesses to make smart and logical decisions. Based on the existing values and past states, besides the data extraction, representation, and visualization, Microsoft Power BI can provide predictive analysis.

    Microsoft Power BI performs Predictive analytics by statistical methods, data mining, and predictive modeling. It uses Azure Machine Learning and SQL Server-based Analysis Services to realize and analyze current and past state values.

    Moreover, in power bi predictive analytics and Azure analytics, Microsoft Power BI offers customized options. This happens as the software accounts for an increased scope by merging cloud accessibility and on-premises capabilities.

    Businesses can add their own indicators and functionalities to generate the required results by using customized reports. The predictive analysis results may be sales forecasting, revenue prediction, and many other significant reports.

  2. Interactive Dashboards

    The customized accessibility of data provides a room of high-level flexibility that utilizes machine learning to build interactive dashboards.
    With a high scope of Machine learning and predictive analysis in the upcoming years, Microsoft Power BI lets users work on interactive dashboards that provide them with customized data access.
    This data is sharable in visual outputs among stakeholders, business intelligence, and data analyst teams.

  3. Third-Party Integration

    Microsoft Power BI can integrate with products of Microsoft and third-party apps like Exchange, Office 365, Dynamics 365, SharePoint, Excel, Cortana, Salesforce, and Teams. This helps Microsoft Power BI to offer better and more customized functionality for data analysis.

    The addition of tools and functionalities streamlines data analysis processes with the elimination of errors, enhancement of real-time reporting, and creates end-to-end visibility.
    This helps integrate the software’s data sets for the organization to extract the models that help in customized predictive analysis.

  4. R Programming Language Integration

    R programming language is the most used coding language for statistics. It reads the data into Power BI and uploads files into the data model.

    Microsoft Power BI offers an indirect integration to R programming language with the assistance of Microsoft Revolution Analytics to generate better power BI data analytics.

    The R language extracts unscored data from Azure SQL and reads it into Power BI. Firstly, R extracts the unscored data and sends it to Azure machine learning services to generate an output. The scored data output by Azure machine learning service is embedded into Azure SQL. R reads the data to Microsoft Power BI and publishes the read file into the Power BI service. Then, R uses some gateways to refresh data and provides predictive models.

    The usage of R in Power Query of Power BI allows powerful data visualization, error detection, and deep analysis.

  5. Quick Insights

    Microsoft Power BI’s popular feature of Quick Insights presents you with the most meaningful data visualization. Some advanced analytical algorithms let you search trends in data subsets to extract unique insights from the existing values in a set time frame. The extracted insightful data by this feature is presented in form visualization.

  6. Data Segmentation

    Microsoft Power BI performs segmentation on data to produce meaningful pieces of information. Data Segmentation is the process of breaking data into groups that provides insightful information that the organization can use to make logical business decisions.
    Breaking data into smaller subsets lets you understand information on a deeper level and perform operations to predict future values. To perform data segmentation, Microsoft Power BI uses three different methodologies- Clustering, Grouping, and Binning.

    Clustering is a process of combining similar subsets of data together, while Grouping combines two different categories of data.

    Binning is the process of continuously grouping data into smaller bins with customizable sizes.

  7. Data Connection

    Monitoring real-time data and extracting information from such data is possible in Microsoft business analytics to power bi through Data Streaming. The Real-Time Dashboards allow you to extract information from time-sensitive data at a faster speed.

    Microsoft Azure’s Stream Analytics and Machine learning allow integration with real-time data through Azure Event Hub supported by IOT to help you to perform customization and analysis on such data.

    Furthermore, you can record real-time data and store it in the cloud or on-premises services.

    Some examples of real-time data include live social media campaigns, the intent of social media posts regarding the organization, etc.

  8. Data Analysis Expressions (DAX)

    Data Analysis Expressions abbreviated as DAX commonly, are a key feature of Microsoft Power BI that distinguish it from other software in the same operation.

    Microsoft Power BI is deployed using a set of predefined Data Analysis Functions (DAX) codes. There is a library of 200 DAX functions for calculating and measuring columns.
    You can create DAX formulas to extract valuable insights from your data for analysis, comparison, and prediction.

  9. Data visualization

    Powerful Data visualization is a top-notch feature and the reason to use Microsoft Power BI.

    A list of visualizations is available in the visuals gallery of Microsoft Power BI that you can use to present data and gain insightful information.
    The visual gallery is curated by Microsoft itself and the community. New visualizations are added continuously into the library for you to use. This gallery lets you extract appropriate visualization into your report for downloading.
    You can customize visualizations in the Power BI development environment to use in your own reporting systems. There is an option to export the custom visuals for uploading into the Visuals Gallery for other analysts to use.

    Furthermore, if you want more effective visualizations, the R programming language uses complex algorithms to produce visualizations that the community can use.

  10. UX & UI

    Microsoft Power BI offers one of the best usability and user interfaces among all the Business Intelligence software, as you can easily use it. Structured and unstructured data can be easily incorporated into power bi data analytics by the team that may or may not have any prior technical experience or educational background.

    Teams who have been using Microsoft products like MS Office, MS Excel, and MS PowerPoint may find it easier to use power BI. The software is flexible and requires no complex coding to perform data analysis and generate predictive reports.

  11. Data Security

    Microsoft Power BI provides Encryption to safeguard your data by using Microsoft Managed Keys. All your data is backed by security procedures of Azure SQL databases with two primary repositories. However, extra security optimization is needed when extracting real-time data for analysis.

  12. Data Streaming

    Monitoring real-time data and extracting information from such data is possible in Microsoft business analytics to power bi through Data Streaming. The Real-Time Dashboards allow you to extract information from time-sensitive data at a faster speed.

    Microsoft Azure’s Stream Analytics and Machine learning allow integration with real-time data through Azure Event Hub supported by IOT to help you to perform customization and analysis on such data.
    Furthermore, you can record real-time data and store it in the cloud or on-premises services.
    Some examples of real-time data include live social media campaigns, the intent of social media posts regarding the organization, etc.

Conclusion

There are many reasons to use Microsoft Power BI for effective data analytics in your organization, like customized predictive analysis, real-time data sharing, interactive dashboards, etc. Power BI offers great functionality, room for third-party collaboration, a visual library, flexible UX/UI, machine learning support, and R programming language integration for data analysis.

With top-notch features of effective data visualization and segmentation, business analytics power bi has become a legacy software for data analysis. Whether your organization is small or big, you should choose Power BI to perform effective data analysis and generate customized predictive reports.

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