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Determine latest condition of each equipment and show a month wise count

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There are 100 machines in a factory.  Every machine has different test frequency. In a given month, not every machine is tested but we still have the last known rating (from some previous month) of that machine.  We have to show the latest rating of each machine for each month in a stacked column chart. This way, the total number will remain 100 every month in the chart, but the rating distribution (color based on legend) will change based on last available rating of that machine.

For example, in January, 35 machines were tested. So we have latest ratings of these 35 machines. But as the rest of the machines also have some previous rating, the graph needs to show all 100, with last available rating.

The expected result should look like this

You may download my PBI Desktop file from here.  The very same DAX formulas can be written in the DAX formula language of MS Excel as well.

Analyse membership changes from year to year

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Assume a simple 4 column dataset as shown below.  This data shows which ID had which type of subscription in which year.  So ID A, which started as a "Free" subscriber in 2018 switched to a "Premium" subscriber in 2019 and then churned out in 2020.  Likewise, ID D which started as a "Pro" subscriber in 2018, churned out in 2019 but returned as a "Free" subscriber in 2020.
The objective is to study how subscribers switched from one subscription type to another across year.  So the expected result should look like this


I have solved this question using the PowerPivot.  You may download my MS Excel workbook from here.

Count tasks by status

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Assume a simple 3 column dataset as shown below - the date of each task and the status of that task.
The objective is to get the status wise count of tasks by the last time stamp.  So for the Status "To-do", the count should be 2 - Task ABC and DEF.  Only these two tasks on their last time stamp have the status as "To-do".  Tasks CED and ADR should not be counted because their last time stamp had a status other than "To-do".  So the final expected result in MS Excel is:

Since the original data is being fetched from an external data source, no additional tables or columns can be created from/in the source data table.

The final result in PBI Desktop is this
You may download my PowerPivot solution workbook from here and PBI Desktop solution file from here.

Segment towns according to volume contribution and market share with a slicer

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This post is an extension to the one I posted here - Segment towns according to volume contribution and market share. Here's a simple dataset of Shampoo sales in the state of Rajasthan, India.

For a chosen segment, one may want to segment the 4 towns based on the following conditions:
Based on the two screenshots shared above, the desired result is shown in the screenshot below:
The difference between this solution at the previous one (the link of which I have shared above) is that in this one we want to drag the Classification (range E16:E17) to either the row/column/report filter section of the Pivot Table use it as a slicer.  The current limitation with measures that one writes in PowerPivot's is that measures cannot be used in either row/column/report filter section or as a slicer of/in a Pivot Table.  So in the previous solution, I had written a measure to return the result as Headroom, Stronghold, Emerging or small in only the value area section of the Pivot Table.  One could not drag that measure into the row labels of a Pivot Table.  In this solution, one can drag the Town classification to the row/column/report filter section or even to the slicer (see images below)
You may download my solution workbook from here.

Segment towns according to volume contribution and market share

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Here's a simple dataset of Shampoo sales in the state of Rajasthan, India.
For a chosen segment, one may want to segment the 4 towns based on the following conditions:
Based on the two screenshots shared above, the desired result is shown in the screenshot below:
The desired result is shown in range E16:E19 and the explanation of the classification is shown in range F16:F19.

The final result obtained by using the PowerPivot is shown in the screenshot below:
You may download my solution workbook from here.

Calculate rolling sum for the past week by ignoring blank cells

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Assume a simple dataset as shown in the image below (the input data is in columns A and B only.  The desired outcome is in columns C and D).

The objective is to calculate the 7 days rolling sum and average (as shown in columns C and D) ignoring blank cells.  So in cell C8, the rolling sum is the summation of values from range B2:B8.  In cell C9, it is from B3:B9.  However, in cell C10, it will be from range B3:B9 (not from range B4:B10).  Likewise, in cell C11, the rolling sum will be from range B4:B11.  So the range to be considered for calculating the rolling sum has to roll back automatically until it picks up 7 numeric cells - the blanks have to be ignored.  The rolling average is a simple division - Rolling sum/7.

I have solved this question with Excel formulas here.  This time however, I am sharing a solution by using the DAX formula language available in the PowerPivot and PowerBI Desktop.  You may download my PowerBI Desktop file from here.  The same solution can also be obtained in MS Excel using the PowerPivot as well.

Analyse free flowing text data or user entered remarks from multiple perspectives

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Here is a 2 column dataset - UserID in column A and Remarks in Column B.  This dataset basically tabulates the remarks/comments shared by different users.  Entries in the Remarks column are basically free flowing text entries which have the following inconsistencies/nuances:

  1. Users reported multiple errors which are separated by comma, Alt+Enter (same line within the cell) and numbered bullets
  2. Users committed spelling mistakes (see arrows in Table1)
  3. A user ID may be repeated in column A

Given this dataset, one may want to "hunt" for specific "keyword Groups" (column E above) in each user remark cell and get meaningful insights.  Some questions which one would like to have answers to are:

  1. How may users reported each type of keyword Group - "How may users used the Unresponsive keyword?".  See Pivot Table1 below.
  2. Which are the keyword Groups that each user reported - "Which are the different keyword groups reported by UserID A004?".  See Pivot Table2 below.
  3. How many users reported each of the different keyword Groups - "How many users reported all 3 problems of Slow, unresponsiveness and crash".  See Pivot Table 3 below.
  4. How may users who used this keyword group also used this keyword group - "How many users who reported Crash also reported Unresponsive?".  See Pivot Table 4 below.

This was quite a formidable challenge to solve because of spelling mistakes and multiple keywords reported in each cell.  I have solved this problem with the help of Power Query and PowerPivot.  You may download my workbook from here.

Show Project wise status in a Pivot Table

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Visualise a simple 6 column Table as shown below - Project Name and the finish date for each of the 5 stages that the projects go through.  Each project goes through 5 stages - Requirement (Req), Development (Dev), UAT, Implement and Warranty.

The objective is to report on the status of each project at the end of each month based on which stage is/was completed in that month.  So, if a given project's requirements are completed in January and development completes some time in March, the one would expect the output of the report to show the project's status in January and February as "Req" and in March as "Dev" respectively.  February should also show "Req" because the next stage was completed only in March (although it may have started in January).  If multiple stages complete in one month, then the report should display only the most recently completed stage.  So, if Project A completed both Requirements and Development stages in January, the report should show only "Dev" as the stage completed in January.

For the data shared above, the expected result is:

You may download my solution workbook from here.

Identify Customers that Organisations can upsell or cross sell their products to

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Here's a simple Sales data of a retail Store which sells Apple Products.  Since a customer can transact multiple times, there will be repetitions in the Cust ID column.  While Cust ID 123 and 782 purchased multiple products from the same Store in one transaction, Cust ID 53 purchased multiple products from different stores (Store ID 165 and 45) [and therefore the Order ID's are also different (Order ID 2 and 6)].

On this small sample, one may want to identify "Cross and up selling opportunities" i.e. one may want to know which are the Customers that can be approached for selling more products to.  So for e.g. one may want to know which Customers have bought only one product so far.  A case in point being the Apple Watch - Customer ID 2442 and 428 bought only this product.  The other Customers who bought the Apple Watch also bought atleast one more product.  Therefore, Cust ID 2442 and 428 could be approached for buying other products as well.

Solving this via conventional Excel formulas and Pivot Tables would prove to be a formidable challenge.  I have solved this problem using a Data visualisation software from Microsoft called PowerBI desktop (it can be downloaded free from the Microsoft website).  This problem can also be solved in MS Excel using Power Query and Power Pivot.

There are 3 sections in the image below - Table at the top (First Table), slicers at the right and another Table at the bottom (Second Table).

Interpretation of First Table

  1. 1 appearing at the intersection of APPLE TV (row labels) and APPLE TV (column labels) represents that there is 1 customer who bought the APPLE TV
    1. 1 appeaing at the intersection of APPLE TV (row labels) and MACBOOK AIR (column labels) represents that the 1 customer who bought the APPLE TV also bought the MACBOOK AIR
  2. 4 appearing at the intersection of APPLE WATCH (row labels) and APPLE WATCH (column labels) represents that there are 4 customers who bought the APPLE WATCH
    1. 1 appearing in other columns of the same row represents other products which those customers bought
    2. When one right click's on APPLE WATCH and selects "Drill down", one will be able to see the Customers who bought the other products as well.
      1. Customer 53 bought the APPLE WATCH, AIRPORT and IPHONE 8S.  Customer 123 bought APPLE WATCH, IPHONE X and IPOD
      2. Customers 2442 and 428 did not buy any other product

Interpretation of Second Table

This table shows a list of Customers (and their transaction details) who bought only and only that one product selected by the user in the filter section (see the red oval selection in the image).  So these two customers could be approached for selling more products to.

You may download my PBI desktop file from here.

Filtering on 2 date fields within one Table

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This table contains a list of all the inspections created and completed within different time periods.

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The objective is to create two Tables from this single table - one showing the Accounts created within the chosen time period and another showing the those that were closed within the same time period.  Here are screenshots of the expected results.

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You may download my PowerBI desktop solution workbook from here.  The same solution can be obtained in Excel as well (using Power Query and PowerPivot).