Tags: FILTER

Customer analysis by Country and time period

{ 2 Comments }

Here is a Sales dataset of 8 columns and 29 rows.  It basically details the revenue earned and cash collected by service type, Customer, Country and Period.  For a selected Country and time period, there could be customers availing of both services or of any 1 service. There are 2 broad questions that one may […]

Read More →

Compute Relative Size Factor per vendor

{ 2 Comments }

Relative size factor (RSF) is a test to identify anomalies where the largest amount for subsets in a given key is outside the norm for those subsets. This test compares the top two amounts for each subset and calculates the RSF for each. In order to identify potential fraudulent activities in invoice payment data, one […]

Read More →

Determine the lowest bidding vendor(s) for each product in a Pivot Table

{ 0 Comments }

Imagine a dataset like this.  This dataset shows vendors that submitted proposals for supplying various parts to a Company.  There is one column for each of the twelve months. Via a simple Pivot Table, one can determine the lowest bidding vendor per product (part) for any chosen month.  However, one may also want to know […]

Read More →

Filtering on 2 date fields within one Table

{ 0 Comments }

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

Read More →

Compute transaction fee based on a tiered pricing model

{ 0 Comments }

Consider a simple dataset as shown below: For each tier, the tier rate is incrementally applied to the volume within the tier volume range.  Given the following transaction volumes, one may want to compute the transaction fee The expected result is shown below As one can observe, for a transaction value of 400,000, the fee […]

Read More →

Perform an aggregation on Top x items after satisfying certain conditions

{ 0 Comments }

Visualise a 5 column dataset as show below.  This is a very small sample of the actual dataset.  It shows the date on which supplies were received for each item from Vendors and whether those supplies had errors in them.  Finally those identified errors have been bucketed into relevant categories.  The Item ID# is a […]

Read More →

Compute the average of values against the 5 most recent dates of each Category

{ 0 Comments }

Here is a simple 3 column dataset showing Categories, Date and Value Catagorie Date Value Fish 08-12-2015 6 Crab 05-12-2015 7 Crab 04-12-2015 6 Bird 27-11-2015 4 Snow 25-11-2015 10 Cat 21-11-2015 7 Dog 12-11-2015 5 Dog 28-10-2015 5 Fish 12-10-2015 3 Bird 11-10-2015 9 Dog 22-09-2015 9 Crab 17-08-2015 8 Cat 11-08-2015 1 Fish […]

Read More →

Determine the total number of projects by Status

{ 0 Comments }

Here’s a simple 3 column table showing Date, Project name (Cat.) and Status of the project.  Each project can have multiple status entries on different dates.  So as you can observe, project “alpha_9383993” was In Progress on Oct 2, 2017, remained so on October 5, 2017 but was completed on October 6, 2017. Date Cat. […]

Read More →

In a Pivot Table, compute highest revenue earned on any day from each customer and the date thereof

{ 0 Comments }

Here’s a simple dataset showing the Date of sale, Customer Name and Sales amount. Date Customer Name Sales amount 12-03-2017 A 1 12-03-2017 A 2 12-03-2017 A 3 12-03-2017 B 4 12-03-2017 B 5 12-03-2017 B 6 12-03-2017 B 7 12-03-2017 B 8 13-03-2017 A 1 13-03-2017 A 1 The objective is to determine Customer […]

Read More →

In a Pivot Table, show the most frequently appearing text entry by a certain parameter

{ 0 Comments }

Here’s a simple two column dataset Comment Identifier Intervals A 3pm-6pm A 9pm-12pm S 3pm-6pm S 3pm-6pm S 9pm-12pm A 9pm-12pm S 9pm-12pm D 3pm-6pm A 9pm-12pm A 9pm-12pm A 9pm-12pm A 3pm-6pm A 3pm-6pm For identifiers listed in column A, there are time intervals in column B. Note that for a certain identifier, a […]

Read More →