Introduction to analytics
Analytics encompasses emerging industry practices, such as data mining, big data analytics, machine learning, AI, and predictive analytics. It is a term that is used to describe the technical aspects of analytics that have predictive capabilities and can be used to solve business problems.
Analytics can transform raw data into an extensive collection of information that categorizes data to identify and analyze behavioral data and patterns. Organizations can use this information to analyze the current state of their operations and to predict future behavior and trends by asking "what-if" questions. Additionally, analytics can help with fraud detection, image recognition, sentiment analysis, overall general employee productivity, and it also often replaces cumbersome manual processes.
Consider the scenario where you ask an employee to determine the cause of a recent spike in sales. The employee might have to painstakingly inspect each sale, interview customers, talk to sales people, and examine market trends. Instead, you can use the Microsoft Power BI key influencers visual to use advanced analytics and possibly get an answer much faster. The visual is only as good as the data that you give it, so you'll still have to collect the data and organize it. The actual analytics, however, can be done for you or at least give you an excellent start.
By reducing manual work, advanced analytics is ultimately able to help organizations make better business decisions and create actionable and meaningful results.
Traditionally, data analysis was a complex task that was carried out by engineers. Today, data analysis is more accessible to, and understood by, many people within organizations, across all teams. Power BI is an exceptional tool for quickly pulling actionable insights from data. It allows you to build visuals and metrics for your data in reports and dashboards so that you and your users can analyze data insights at a high level and drill down into those insights for more detailed information.
In this module's scenario, you work for Tailwind Traders as a data analyst. You've been tasked with building reports and dashboards that will be used across the organization to help with crucial business decisions. For example, the Product team is interested in learning if specific products are not selling as well as others, the Sales team is focused on sales forecasts for the coming year, and the Warehouse team is interested in a general breakdown of how the warehousing and shipping locations are performing worldwide. For each of these teams, you have to build and share unique reports and dashboards that display high-level insights and visuals that are developed by using advanced analytics.
The inherent functionality of Power BI will help you accomplish this task. You can develop quick insights and share them in reports and dashboards with different teams within the organization. The advanced analytics capabilities of Power BI will enable you to identify categories and trends, see how data changes over time, and much more. From this information, you can make predictive semantic models and, therefore, help your organization make more robust business decisions, plans, and forecasts.
This module outlines the advanced analytic capabilities of Power BI. By the end of this module, you will be able to:
Explore statistical summary.
Identify outliers with Power BI visuals.
Group and bin data for analysis.
Apply clustering techniques.
Conduct time series analysis.
Use the Analyze feature.
Use advanced analytics custom visuals.
Review Quick insights.
Apply AI Insights.