# Tags: CALCULATE

Assume a three column dataset which has Year, Company ID and Cash flows.  For each Company, there are cash flows for multiple years.  So for Company ID A001, there are 7 rows, one each for 2010 to 2004 and cash flows appearing in a third column.  Let's assume the number of rows are 750,000.

The task is to compute the year on year growth rate in a fourth column.  While this problem can easily be solved by writing a formula in a fourth column, copying that formula all the way down to 750,000 rows will be time consuming and processor intensive.

I have been able to solve this problem using PowerPivot.  You may download the workbook from here.

You may refer to related questions at this link

Assume a three column dataset showing Audit ID, Date of receipt of audit mandate and Date of audit completion.  There are other columns as well but they are not important for our Analysis.  One may want to compute the following month wise:

1. Which (Audit ID) are the audits pending at the end of every month; and
2. When (Date of receipt of audit mandate) was the mandate for these pending audits received; and
3. Ageing of these pending audits i.e. this would be computed as the last date of the month less Date of receipt of audit mandate

Here's an example:

In January 2014, there are a total of 10 audits reports which were received (Filter "Date of receipt of audit mandate" column on January 2014).  Of these 10 audits, 4 were completed in January 2014 (Filter "Date of audit completion" column on January 2014) itself and therefore there are 6 pending audits.  To this figure of 6, we need to add the audits pending from previous months.  If one filters column "Date of receipt of audit mandate" column on Oct-Dec 2013 and "Date of audit completion" column on dates after January 2014, 8 rows will appear.  This means that there are 8 audits which were received before 1 January 2014 but were completed only after 31 January 2014.  So the total number of pending audits as at 31 January 2014 are 8+6=14.  This task needs to be carried out for all months.

You may refer to my solution in this workbook.

Assume two databases:

1. One showing employee headcount (one row per employee) which has all employee details such as Name, ID, Date of Joining, Supervisor name, Department etc. (Range A1:R781 of Source worksheet)

2. The other showing data for employees who resigned. (Range U1:Z36 of Source worksheet)

The task is to compute the attrition rate for selected Group and selected months.  Groups and months will be selected from slicers.

In the attached workbook, one can see the aborted Pivot Table attempt and the successful PowerPivot solution.  Refer to cell I25 of Abortive Pivot Table attempt worksheet to see how attrition rate should be computed.

Assume a simple three column dataset showing hours worked by different machine on different dates.  So column A is Date, column B is Machine Name and column C is hours worked.  There are duplicates appearing in column A and B .  Blanks in column C depict machine idle time.

The task is to create a simple three column dataset showing all unique Machine names in the first column, Last day on which the machine worked in the second column and hours worked on the last day in the third column.

This problem can be solved by using formulas (Refer first worksheet of the workbook) but if one has to use a Pivot Table, then there would be a few problems.

1. The Grand Total for the Date Field should be blank because on cannot determine the Last day on which the machine worked across different machine types.  A conventional Pivot Table shows the Maximum of all dates appearing in the Date Field.

2. The Grand Total for the Hours worked Field should be a summation of the total hours worked on last day across all machine types.  A conventional Pivot Table shows the Maximum of all hours worked appearing in the Hours worked Field.

3. The biggest problem of them all is that there is no way to give a criteria as the Last day for that machine for computing another Field in the Pivot Table.  Please refer the file for a better understanding.

This problem can be solved using the PowerPivot.  You may refer to my solution in this workbook.

Visualise Sales Data of a Non-Alcoholic Beverage Company with basic columnar information such as Date of Sale, Time of Sale, Brand, Stock Keeping Unit (SKU), State, City, Quantity sold, Unit Price and Salesman Code.  In this sales dataset, each line item represents one visit for one SKU.  If nothing is sold in a certain visit, then the SKU column displays No Sale.  So effectively there is a line item for each visit whether or not something is sold in that visit.

From this simple Sales dataset, here are a few questions which one may need to find answers to:

1. How did the Company perform (in both years 2013 and 2014) on two of the most critical Key Performance Indicators (KPI's) - Quantity sold and Number of Visits.  Also, what is the month wise break up of these two KPI's.

2. Study and slice the two KPI's from various perspectives such as "Type of Outlet visited", "Type of Visit" - Scheduled or Unscheduled, "Day of week", "Brand", "Sub brand".

3. Over a period of time, how did various SKU's fair on the twin planks of "Effort" i.e. Number of visits YTD and "Business Generated" i.e. Quantity sold YTD.

4. Analyse the performance of the Company on both KPI's:
a. During Festive season/Promotional periods/Events; and
b. During different months of the same year; and
c. During same month of different years; and
d. Quarter to Date

5. "Complimentary Product sold Analysis" - Analysis displayed on online retailers such as Amazon.com - "Customers who bought this also bought this".  So in the Sales dataset referred to above, one may want to know "In this month, outlets which bought this SKU, also bought this much quantity of these other SKU's."

6. "Outlet Rank slippage" - Which are the Top 10 Outlets in 2013 and what rank did they maintain in 2014.  What is the proportion of quantity sold by each of the Top 10 outlets of 2013 to:
a. Total quantity sold by all Top 10 outlets in 2013; and
b. Total quantity sold by all outlets in 2013

7. In any selected month, which new outlets did the Company forge partnerships with

8. Which employees visited their assigned outlets once in two or three weeks instead of visiting them once every week (as required by Management).

9. Which outlets were not visited at all in a particular month

10. Business generated from loyal Customers - Loyal Customers are those who transacted with the Company in a chosen month and in the previous 2 months.

These are only a few of my favourite questions which I needed answers to when I first reviewed this Sales Data.  Using Microsoft Excel's Business Intelligence Tools (Power Query, PowerPivot and Power View), I could answer all questions stated above and a lot more.

You may watch a short video of my solution here

Visualise an MS Excel file with two worksheets:

1. Employee headcount – a multi column dataset with information such as Employee code, Date of Joining, Age, Division, Department and Location.  Each row represents data for one employee.  The number of rows on this worksheet is approximately 700.
2. Training Data - a multi column dataset with information such as Employee code, Training Date from, Training Date to, Training Program Name, Training Program Category (Internal and External), Training Location and Training Service Provider.  Each row represents one training attended by one employee.  The number of rows on this worksheet is approximately 2,600.

Let’s suppose that the training calendar of this company runs from July to June.  Some questions (only few mentioned for illustration purposes) which a Training Manager may need answers to are:

1)   How may unique employees were trained each year; and
a)   Of the unique employees trained, how many were first time trainees and how many were repeat trainees
i)   Of the first time trainees:
(1)    How many joined this year
(2)    How many joined in past years
ii)  Of the first time trainees:
(1)    How many were trained within the first year of joining
(2)    How many were trained in the second year of joining
(3)    How many were trained in the third year of joining
(4)    How many were trained after three years of joining
iii)  Of the repeat trainees:
(1)    What is the average gap (in days) between trainings
(2)    What is the minimum gap (in days) between trainings
(3)    What is the maximum gap (in days) between trainings

Getting answers to the questions mentioned above would entail writing a lot of lookup related formulas, applying filters, copying and pasting and then creating Pivot Tables.  While the example taken above is that of a training database, you may envision “drilling down to and slicing” any dataset – Marketing, Sales, Purchase etc.

You may watch a short video of my solution here

In these two workbooks, you will be able to see the level to which one can drill down and analyse data using the Power Pivot add-in.  When you open this workbook, please go the first worksheet and make the relevant choice of MS Excel version first so that you start looking at the Analysis from the correct worksheet.

You will be able to see the analysis in these workbooks only if you are using one of the following versions of MS Office:

1. Excel 2013 Professional Plus; or
2. Excel 2010 with the Power Pivot add-in installed.  Power Pivot is a free add-in from Microsoft which can be downloaded from here.

Lastly, if you are using the Power Pivot add-in in Excel 2010, you will not be able to see the underlying Data Model or the calculated Field formulas because this workbook has been created in Excel 2013 Professional Plus and unfortunately the Power Pivot model is not backward compatible.  However, all the analysis performed in this workbook can be performed in Excel 2010 as well (with the Power Pivot add-in installed).

Visualise a Pivot Table with a few Fields dragged in the Report filter, Row labels and Value Area section.  In the Column labels are two fields, Month and then Year - so in the column labels, for every month, there is data for three years 2005, 2006 and 2007.  For some months, there is data for two years only 2005 and 2006.  In the Value area section are fields such as Net Amount, Quantity, Bonus etc. and the summarization function applied to them is SUM.  There is no complication in creating the Pivot Table described above.

The actual requirement is to customise the Subtotal column of the Pivot Table as follows:

1. For the monthly subtotals, the Net Amount and Bonus figure are to computed as a difference of 2005 and 2006 i.e. SUM of quantity of 2005 - SUM of quantity of 2006.  The Grand total column should be a a summation of individual subtotals.
2. Average Selling price for every year is to be computed as as Net Amount/(Ttl Bonus + Quantity).  For the monthly subtotals, the figure is to be computed as

=(Net Amount of 2005/((Bonus of 2005+Quantity of 2005)) - (Net Amount of 2006/((Bonus of 2006+Quantity of 2006))

The Grand Total column is to be left blank for Average Selling Price,

As you can observe, the subtotal column (for the months) will have different formulas running for different Fields.

A conventional Pivot Table does not allow one to have custom formulas in the Subtotal columns.  I have been able to resolve this problem by using the free Power Pivot add-in from Microsoft for Excel 2010 and higher versions.

You may refer to my solution in this workbook.

Here's another example.  Assume a dataset with three columns - Date, Manager and Amount.  There are repetitions in the Data and Manager column.  One may want to know the maximum amount per month per Manager.  While this is easy to accomplish with a Conventional Pivot Table as well, the problem occurs in the Subtotal/Grand Total cells of a Pivot Table.  The Subtotal/Grand Total cells assume the same function as has been used in the "Summarise Values field by".  So, while in the "Summarise values fields by" section, one may want to use the Maximum function, in the subtotal cell, one may want to use the sum function.

You may refer to my solution in this workbook.

Assume quantity sold date by date and City in a three column database.  The objective is to determine year wise, month wise and City wise running total of quantity sold in a Pivot Table.

The issue which will arise with generating this result in a Pivot Table will be that the Show Values As > Running Total in, resets the quantity sold to 0 when the year changes.

This issue can be overcome by writing DAX formulas in a Power Pivot.  You may refer to my solution in this workbook.

With Power Business Intelligence (BI) tools of Excel 2013, one can metamorphose raw data and/or results of complex calculations into stunning and interactive visualizations.  Power View (one of the four components of Power BI) allows one to create a PPT like flow in Excel thus allowing one to weave a story.  To be able to interact with/create visualizations, you will need to install Microsoft Office Professional Plus 2013 (this version will already have two of the four components of Power BI - PowerPivot and Power View).  Additionally, you will have to install the following add-ins from Microsoft (the other two components of Power BI)

1. Power Query; and
2. Power Map

I have tried to showcase the prowess of Power BI tools of Excel 2013 in these two workbooks:

You may watch a video of my work at this link

Assume a table which lists attendees for a Company's Annual day function.  In this Table, data for every attendee is shown on a separate row so if an employee attends the function with his/her spouse and three children, then there will be 5 rows for that employee.

The question is to determine the count of the following family configuration:

1. Employees only (those who attended without spouse and children); and
2. Employees, spouse and children (Family); and
3. Employees and spouse (no children); and
4. Employees and children (no spouse)

You may refer to my solution in this workbook.  I have solved this problem using:

1. MS Excel Formulas based on Set Theory and Venn Diagram; and
2. PowerPivot