Creating KPI Dashboards in Microsoft Excel is a series of 6 posts by Robert from Munich, Germany.
This 6 Part Tutorial on KPI Dashboards Teaches YOU:
Creating a Scrollable List View in Dashboard
Add Ability to Sort on Any KPI to the Dashboard
Highlight KPIs Based on Percentile
Add Microcharts to KPI Dashboards
Compare 2 KPIs in the Dashboards Using Form Controls
Show the Distribution of a KPI using Box Plots
The challenge – Adding Percentile Information
Let’s get back to our last week’s KPI dashboard example: Adding sort options to excel dashboards. In today’s post we want our dashboard to take a step forward by adding another data analysis feature. Up to now the user is able to view a window of 10 rows out of a much larger list and to sort by any given decision parameter. But the KPI dashboard falls short if we want to evaluate the performance of the displayed items regarding the other 4 KPIs.
Imagine we are at the top of the list and the table is sorted by KPI 1 (see left). We see that “Product Name 36” is the TOP performer regarding KPI 1. But how does it perform regarding KPI 3? The value of 2% is probably rather poor, but how poor? Sure, we can change the sort order to KPI 3 and scroll down until we find product 36 and look at the ranking in the first column. But changing the sort order back and forth is in-convenient, time-consuming and not user-friendly.
The solution
One statistical method to examine a list of data is the percentile. A percentile is the value of a variable below which a certain percent of observations fall (more). The 10% percentile of our list of values for KPI 3 returns the threshold at which 10% of all values are smaller than this threshold. We will use this method to classify the values of the KPIs that are not selected as the sort criteria by highlighting the values above the 90% percentile in green (10% best performers) and by highlighting the values below the 10% percentile in red (10% poorest performers).
After the highlighting we are now able to see immediately that Product 36 is best in class regarding KPI 1 but it belongs to the poorest 10% of all products regarding KPI 3.
Download the Excel file with KPI Dashboards and read on below how it is done.
The implementation
Implementation needs a simple conditional formatting and the excel spreadsheet function PERCENTILE. The syntax of this function is PERCENTILE (array, k), where ‘array’ is the range with the data and ‘k’ is the percentile value in a range between 0 and 1. PERCENTILE (A1:A100, 0.10) returns the threshold at which 10% of all values in the range are smaller than this value and the remaining 90% are larger than this value.
Here is the description how to change the workbook:
- Add two additional rows to the data worksheet to define the upper and lower percentile value.
- Insert five new columns on the dashboard each of them right to the existing column with the data.
- To simplify the formula, insert the number of each KPI in the cells below the header (F6 = 1; H6 = 2, and so on).
Fill the new columns with the following formula (example for cell G8):=IF (mySortCriteria=F$6,"",
IF (F8>PERCENTILE (Calculation!$K$10:$K$109,Data!$E$5),"<+",
IF (F8<-","")))If the actual column is the one the table is sorted by, a blank would be returned. Otherwise: if the value
in the cell left is larger than the e.g. 90% percentile, “<+”, if the value is smaller than the 10% percentile “<-” will be returned. For all other values the result of the formula is a blank.
Format the new columns with a red font color and add additional formatting that changes the font color to green if the cell value is “<+”.- Finally add a caption under the table to help the user understand what the triangles are representing.
Final remarks
If you don’t like the triangles, you could easily replace them by a dot or a diamond or whatever you choose. Or you might want to change the colors or put the triangles to the left of the columns instead of the right. If you don’t like the extra columns next to the data, you could also use the described formula to conditionally format the cells with the data (e.g. with red and green fill color).
What’s next?
Make sure you have downloaded the KPI Dashboards XLS files – Click here
Up to now we have limited our dashboard to texts and numbers. Of course graphical visualization can always add much value for analysis. See next post: Part 4: Add Microcharts to KPI Dashboards
Also, Checkout our Excel Dashboards Page for more examples and resources.
Chandoo’s note: Robert is a regular reader of this blog, please leave your comments, questions, appreciations here and he will respond.
One Response to “SQL vs. Power Query – The Ultimate Comparison”
Enjoyed your SQL / Power Query podcast (A LOT). I've used SQL a little longer than Chandoo. Power Query not so much.
Today I still use SQL & VBA for my "go to" applications. While I don't pull billions of rows, I do pull millions. I agree with Chandoo about Power Query (PQ) lack of performance. I've tried to benchmark PQ to SQL and I find that a well written SQL will work much faster. Like mentioned in the podcast, my similar conclusion is that SQL is doing the filtering on the server while PQ is pulling data into the local computer and then filtering the data. I've heard about PQ query folding but I still prefer SQL.
My typical excel application will use SQL to pull data from an Enterprise DB. I load data into Structured Tables and/or Excel Power Pivot (especially if there's lot of data).
I like to have a Control Worksheet to enter parameters, display error messages and have user buttons to execute VBA. I use VBA to build/edit parameters used in the SQL. Sometimes I use parameter-based SQL. Sometimes I create a custom SQL String in a hidden worksheet that I then pull into VBA code (these may build a string of comma separated values that's used with a SQL include). Another SQL trick I like to do is tag my data with a YY-MM, YY-QTR, or YY-Week field constructed form a Transaction Date.
In an application, I like to create a dashboard(s) that may contain hyperlinks that allow the end-user to drill into data. Sometimes the hyperlink will point to worksheet and sometimes to a supporting workbook. In some cases, I use a double click VBA Macro that will pull additional data and direct the user to a supplemental worksheet or pivot table.
In recent years I like Dynamic Formulas & Lambda Functions. I find this preferable to pivot tales and slicers. I like to use a Lambda in conjunction with a cube formula to pull data from a power pivot data model. I.E. a Lambda using a cube formula to aggregate Accounting Data by a general ledger account and financial period. Rather than present info in a power pivot table, you can use this combination to easily build financial reports in a format that's familiar to Accounting Professionals.
One thing that PQ does very well is consolidating data from separate files. In the old days this was always a pain.
I've found that using SQL can be very trying (even for someone with experience). It's largely an iterative process. Start simple then use Xlookup (old days Match/Index). Once you get the relationships correct you can then use SQL joins to construct a well behaved SQL statement.
Most professional enterprise systems offer a schema that's very valuable for constructing SQL statements. For any given enterprise system there's often a community of users that will share SQL. I.E. MS Great Plains was a great source (but I haven't used them in years).
Hope this long reply has value - keep up the good work.