$45.00 USD
The simple answer to solving this problem is to run pass-fail logic to test each column (which houses the different data points about the thing you are filtering). Then, you run a pass-fail on all the pass/fail columns to see if the overall result is that the row passes. If it passes, you then bring it over to a summary report with the standard filter formula, but now all you have to test for is if the final pass/fail column has a '1' in it rather than trying to embed a bunch of if statements in it.
The best way to describe why this is useful is with an example. Let's say you are an organization that has 1,000 rows of data about ticket buyers of your events and you want to show a list of only certain ticket buyers based on them meeting various criteria.
So, let's say you want to only show ticket buyers that spent at least $x, lived further than 100 miles from the event, spent at least $x on merchandise, were male, and were over the age of 35.
It would be hard to do such a filter with the standard column filters and it would be hard to use a single filter formula to test for all of those things (pretty much near impossible). So, the answer is to use the logic described in this Google Sheet template and apply it to each of the data points you have.
There are a million use cases for such logic and there are probably 1,000's or 10's of thousands of organizations that look into their data for various reasons and would love such functionality. So, here you go.
By isolating each of the values you want to test for, it gives you a lot more flexibility in how you test for it and what kinds of specific logic you want to add in your overall database filtering algorithm. The end result is a magical database that allows you to chop it any way you want to see it quickly and painlessly.
The best way to describe why this is useful is with an example. Let's say you are an organization that has 1,000 rows of data about ticket buyers of your events and you want to show a list of only certain ticket buyers based on them meeting various criteria.
So, let's say you want to only show ticket buyers that spent at least $x, lived further than 100 miles from the event, spent at least $x on merchandise, were male, and were over the age of 35.
It would be hard to do such a filter with the standard column filters and it would be hard to use a single filter formula to test for all of those things (pretty much near impossible). So, the answer is to use the logic described in this Google Sheet template and apply it to each of the data points you have.
There are a million use cases for such logic and there are probably 1,000's or 10's of thousands of organizations that look into their data for various reasons and would love such functionality. So, here you go.
By isolating each of the values you want to test for, it gives you a lot more flexibility in how you test for it and what kinds of specific logic you want to add in your overall database filtering algorithm. The end result is a magical database that allows you to chop it any way you want to see it quickly and painlessly.
Here is another fun Google Sheets database template.
