Power BI: Analyzing Seasonality in Data, and Predicting Future Data using Seasonality


The video is a tutorial for creating a dashboard that explains seasonality and predicts future sales for a business based on their monthly sales data from 2010 to 2022. The video starts by introducing the data and showing charts of individual years that depict the trend of sales each year. The ups and downs observed in the sales are referred to as seasonality. The video then goes on to explain how the data is imported into Power BI Desktop application using the 'Get Data' feature. The tutorial explains how to use data analysis expression to analyze monthly sales data. Firstly, month numbers are extracted from dates and shown in a column added to the sales table. Then a new table with distinct month numbers from the sales table is created. Average sales are calculated in a new column against each month using DAX (Data Analysis Expressions), where the 'Calculate' function is used to calculate the average sales. The tutorial demonstrates how to add a filter to calculate average sales against each month from the sales table where the month number in the sales table is equal to the month number in the sales analysis table. The same formula is used to calculate the maximum sales against each month. The tutorial proceeds to demonstrate how to create a dashboard. Firstly, a title is added to the dashboard using a text box visual. Next, a line and column chart is added to display last year's sales and customer data. The sales field is added to the line y-axis, and the customers' field is added to the column y-axis. The month from date hierarchy is added to the x-axis, and this chart shows the total sales per month during all the years. The chart is then filtered to show only the sales data for the year 2022. After that, a copy of the existing chart is created, and the 2022 filter is removed. This chart is then changed to show the month-wise average sales. A comparison of both charts already suggests a strong correlation between average monthly sales and monthly sales during 2022, which is evidence of strong seasonality. https://youtu.be/xlhYjSmbjJ4 https://virtual-school.org/tutorial?v=xlhYjSmbjJ4 https://docs.google.com/spreadsheets/d/1dNV34doTkq_hM55BL1RJ6jvSKb-Am_B0/edit?usp=sharing&ouid=110287700425339553017&rtpof=true&sd=true Chapters: 01:22 - Comparing Average Monthly Sales with Last Years Sales 03:18 - Using DAX to Analyse Sales Data 07:43 - Attempting to Predict Next Year's Sales Using Seasonality 10:18 - Creating a Line Chart with Years in Small Multiples #tutorial #datavisualization #datanalysis #PowerBI #salesdata #seasonality


tutorial, data visualization, data analysis, Power BI, sales data, seasonality, DAX, data analysis expression
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