tanzeemagra/global_store_data_analysis_using_tableau

Global_Store_Data_Analysis_Using_Tableau

Business intelligence (BI) combines business analytics, data mining, data visualization, data tools and infrastructure, and best practices to help organizations to make more data-driven decisions. In practice, you know you’ve got modern business intelligence when you have a comprehensive view of your organization’s data and use that data to drive change, eliminate inefficiencies, and quickly adapt to market or supply changes. It’s important to note that this is a very modern definition of BI—and BI has had a strangled history as a buzzword. Traditional Business Intelligence, capital letters and all, originally emerged in the 1960s as a system of sharing information across organizations. It further developed in the 1980s alongside computer models for decision-making and turning data into insights before becoming specific offering from BI teams with IT-reliant service solutions. Modern BI solutions prioritize flexible self-service analysis, governed data on trusted platforms, empowered business users, and speed to insight.

Much more than a specific “thing,” business intelligence is rather an umbrella term that covers the processes and methods of collecting, storing, and analyzing data from business operations or activities to optimize performance. All of these things come together to create a comprehensive view of a business to help people make better, actionable decisions. Over the past few years, business intelligence has evolved to include more processes and activities to help improve performance. These processes include: • Data mining: Using databases, statistics and machine learning to uncover trends in large datasets. • Reporting: Sharing data analysis to stakeholders so they can draw conclusions and make decisions. • Performance metrics and benchmarking: Comparing current performance data to historical data to track performance against goals, typically using customized dashboards. • Descriptive analytics: Using preliminary data analysis to find out what happened.

• Querying: Asking the data specific questions, BI pulling the answers from the datasets. • Statistical analysis: Taking the results from descriptive analytics and further exploring the data using statistics such as how this trend happened and why. • Data visualization: Turning data analysis into visual representations such as charts, graphs, and histograms to more easily consume data. • Visual analysis: Exploring data through visual storytelling to communicate insights on the fly and stay in the flow of analysis. • Data preparation: Compiling multiple data sources, identifying the dimensions and measurements, preparing it for data analysis.

In this Project we have created following models using Tableau Desktop In our Project, we have considered following business model, such as

• Category and Department
• Sales and Profit
• Sales by Region
• Profit Forecast
• Trend Lines of the Business.