Tags: LASTDATE

Summarise data by most recent status

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Here's a simple 3 column dataset showing Date, ID and Status - the status of each ID by Date.

So, the narrative for ID A is:

  1. It was "New" on Jan 1
  2. It remained "New" until Jan 14
  3. On Jan 15, the status changed to "Open"
  4. It remained "Open" till Jan 31 and the status changed to "Closed" on Feb 1
  5. It remained "Closed" till March 31 and the status changed to "Stop" on April 1
  6. It has remained in 'Stop" status till Today

Note that for the month of March, there is no record for ID A but the status of it has to be treated as Closed (refer point 5 above).

The objective is to count the number of ID's by status and month.  The expected result is:

Please note that the trick part here is to get the result as 2 in cell D6 (Status closed for March). I have solved this problem using Power Query and PowerPivot.  Since these two Business Intelligence (BI) tools are available in PowerBI desktop (PBI) as well, you may download a folder with both files (the MS Excel workbook and PBI file) from here.

Identify buy and sell break points

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Assume a two column dataset with Date in the first column and Price in the second one.  The purpose is to identify times to buy and sell - buying would be just after the lowest low is confirmed and sell before or just after the highest high is in place. Confirmation is achieved through crossover of moving averages. This data is being used in back testing buy and sell criteria.

Snapshot of base data

Snapshot of expected result

The Lowest Low is the lowest price that occurs before the next Highest High.  The Highest High is the highest price that occurs before the next Lowest Low..  2.77 is the lowest low after the highest high of 3.69 and 3.23 is highest high after the lowest low of 2.77.

You may refer to my solution in this workbook.

Sales data modelling and interactive visualisations

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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

Compute “running total in” across years in a Pivot Table

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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.

Story telling with Excel Power BI

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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:

1. An overview of the BRIC Economies
2. Sales data analysis

You may watch a video of my work at this link