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From the desktop, open the CC workbook.
Open the City Pareto worksheet.
You need to complete the Pareto chart to show the percentage of sales compared to the percentage of cities. The chart must show references lines to visualize how the data compares to the Pareto principle.
From the File menu in Tableau Desktop, click Save.
To complete the Pareto chart in the 'City Pareto' worksheet of your Tableau Desktop and add reference lines to illustrate how the data compares to the Pareto principle, follow these steps:
Open the CC Workbook and Access the Worksheet:
From the desktop, double-click on the CC workbook to open it in Tableau Desktop.
Navigate to the City Pareto worksheet by selecting its tab at the bottom of the window.
Construct the Pareto Chart:
Ensure that sales data is aggregated by city. If not, drag the 'City' dimension to the Columns shelf and the 'Sales' measure to the Rows shelf.
Sort the sales data in descending order to properly align the cities according to their sales contribution.
To create a running total of sales, right-click on the 'Sales' measure on the Rows shelf, select 'Quick Table Calculation', and choose 'Running Total'.
Drag the 'Number of Records' field to the Rows shelf next to the Sales running total. Right-click on it, select 'Quick Table Calculation', and choose 'Running Total'. Set its calculation to 'Percent of Total' from the 'Edit Table Calculation' option to represent the percentage of cities.
Add Reference Lines for the Pareto Principle:
Click on the Analytics tab in the sidebar.
Drag a 'Reference Line' element and drop it onto the chart area.
Set the Reference Line for the Sales axis at 80% to represent the typical Pareto cutoff where 80% of effects come from 20% of causes.
Add another Reference Line on the axis representing the percentage of cities, set at 20%, to visually assess the Pareto principle.
Adjust the Appearance of the Chart:
Format the reference lines by right-clicking on them, selecting 'Edit', and choosing a distinct style or color to make them stand out.
Ensure the chart is clear and labels are appropriately adjusted for easy understanding of the data visualization.
Save Your Changes:
From the File menu, click 'Save' to ensure all your changes are stored.
Tableau Help: Offers detailed guidance on creating Pareto charts and adding reference lines.
Tableau Visualization Best Practices: Provides tips on effectively displaying cumulative data and principles such as Pareto.
By following these steps, you will have successfully enhanced the City Pareto worksheet to include a complete Pareto chart with reference lines that illustrate how the sales data compares to the Pareto principle, making it easier to analyze and communicate the distribution of sales across cities.
An online sales company has a table data source that contains Order Date. Products ship on the first day of each month for all orders from the previous month.
The consultant needs to know the average number of days that a customer must wait before a product is shipped.
Which calculation should the consultant use?
The correct calculation to determine the average number of days a customer must wait before a product is shipped is to first find the shipping date, which is the first day of the following month after the order date. This is done using DATETRUNC('month', DATEADD('month', 1, [Order Date])). Then, the average difference in days between the order date and the shipping date is calculated using AVG(DATEDIFF('day', [Order Date], [Calc1])). This approach ensures that the average wait time is calculated in days, which is the most precise measure for this scenario.
To calculate the average waiting days from order placement to shipping, where shipping occurs on the first day of the following month:
Calculate Shipping Date (Calc1): Use the DATEADD function to add one month to the order date, then apply DATETRUNC to truncate this date to the first day of that month. This represents the shipping date for each order.
Calculate Average Wait Time (Calc2): Use DATEDIFF to calculate the difference in days between the original order date and the calculated shipping date (Calc1). Then, use AVG to average these differences across all orders, giving the average number of days customers wait before their products are shipped.
Date Functions in Tableau: Functions like DATEADD, DATETRUNC, and DATEDIFF are used to manipulate and calculate differences between dates, crucial for creating metrics that depend on time intervals, such as customer wait times in this scenario.
A client's dashboard has two sections dedicated to their shops and warehouses shown when a viewer chooses either shops or warehouses with a parameter.
There are a few quick filters that apply to both, while others apply to only shops or only warehouses.
Currently, the quick filters are all shown at the left side of the dashboard. The client wants to hide all filters, but when shown, make it easy for the viewer to
find the quick filters that work for only shops or only warehouses.
Which solution should the consultant recommend that meets the client's needs and is most user-friendly?
A stakeholder has multiple files saved (CSV/Tables) in a single location. A few files from the location are required for analysis. Data transformation (calculations)
is required for the files before designing the visuals. The files have the following attributes:
. All files have the same schema.
. Multiple files have something in common among their file names.
. Each file has a unique key column.
Which data transformation strategy should the consultant use to deliver the best optimized result?
Given that all files share the same schema and have a common element in their file names, the wildcard union is an optimal approach to combine these files before performing any transformations. This strategy offers the following advantages:
Efficient Data Combination: Wildcard union allows multiple files with a common naming scheme to be combined into a single dataset in Tableau, streamlining the data preparation process.
Uniform Schema Handling: Since all files share the same schema, wildcard union ensures that the combined dataset maintains consistency in data structure, making further data manipulation more straightforward.
Pre-Transformation Combination: Combining the files before applying transformations is generally more efficient as it reduces redundancy in transformation logic across multiple files. This means transformations are written and processed once on the unified dataset, rather than repeatedly for each individual file.
Wildcard Union in Tableau: This feature simplifies the process of combining multiple similar files into a single Tableau data source, ensuring a seamless and efficient approach to data integration and preparation.
A client wants to see the average number of orders per customer per month, broken down by region. The client has created the following calculated field:
Orders per Customer: {FIXED [Customer ID]: COUNTD([Order ID])}
The client then creates a line chart that plots AVG(Orders per Customer) over MONTH(Order Date) by Region. The numbers shown by this chart are far higher
than the customer expects.
The client asks a consultant to rewrite the calculation so the result meets their expectation.
Which calculation should the consultant use?
The calculation {FIXED [Customer ID], [Region]: COUNTD([Order ID])} is the correct one to use for this scenario. This Level of Detail (LOD) expression will calculate the distinct count of orders for each customer within each region, which is then averaged per month. This approach ensures that the average number of orders per customer is accurately calculated for each region and then broken down by month, aligning with the client's expectations.
The initial calculation provided by the client likely overestimates the average number of orders per customer per month by region due to improper granularity control. The revised calculation must take into account both the customer and the region to correctly aggregate the data:
FIXED Level of Detail Expression: This calculation uses a FIXED expression to count distinct order IDs for each customer within each region. This ensures that the count of orders is correctly grouped by both customer ID and region, addressing potential duplication or misaggregation issues.
Accurate Aggregation: By specifying both [Customer ID] and [Region] in the FIXED expression, the calculation prevents the overcounting of orders that may appear if only customer ID was considered, especially when a customer could be ordering from multiple regions.
Level of Detail Expressions in Tableau: These expressions allow you to specify the level of granularity you need for your calculations, independent of the visualization's level of detail, thus offering precise control over data aggregation.
