For identifiers listed in column A, there are time intervals in column B. Note that for a certain identifier, a time interval can appear multiple times. The objective is two-fold:

For each identifier, show the time interval which appears most frequently; and

For each identifier, compute the count of the time interval which appears most frequently

The expected result is:

As you can observe, the result for S is two time periods - 3pm-6pm and 9pm-12pm. This is because each of them appears twice.

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.

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).

Assume a dataset with a Key Performance Indicator (KPI) [appearing in one column] data for years ranging from 1985 to 2010 for 114 countries. This dataset has 170,000 rows of data and one row below the last row for every country, there is a total of the KPI column. So, if there are 25 rows for India, then in the 26th row, there will be the total appearing for numbers in the KPI column. The same is occurring for other countries as well.

There is another dataset (in another worksheet of the same workbook) which has an index value for the same countries and same date range as the first dataset. The second dataset is relatively smaller (with only 1315 rows) because the index value is not available for all years of each country.

The objective is to determine the year wise (for all years from 1985 to 2010) weighted average of KPI. An illustration of the weighted average computation has been shown in range F5:H10 of the "Index" worksheet of the workbook link shared below.

Solving this problem using Pivot Table, filters, formulas will slow down processing speed due to sheer size of data. I have solved this problem using the Power Pivot tool (for Excel 2010 and higher versions).