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Today’s topic is more like an academic exercise than a production use case, but there are a lot of learnings along the way and I think it’s worth the effort.

The other day I saw a tweet by Igor Cotruta that talked about «text fingerprinting». The idea is embedding the user id in invisible characters so that if the exported data get’s eventually leaked it can be traced back  to whoever exported it. Looks like stuff from a spy movie, so I was quickly on-board in the attempt to reproduce that with Power BI with a Calc Group.

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Ok, I have maybe gone a bit overboard with the title, but I think it has some advantages over other hacks out there to establish the column widths of matrix, so here I am to explain how I came up with it and how to use it.

The first hack I saw was from Ben Ferris (aka The Power BI Guy) which added a dummy measure with a number of 0 to make the width (having automatic width enabled) and then it would disable automatic widths and remove the measure. Nice. But of course, if new columns appear you’ll  need to set the thing again. Something similar happens with the approach of Bas, who skips the dummy measure thing and instead just plays with the format string to show the evenly wide number. His approach is cooler because you skip the measure thing, but you have the same weak points.

However, Bas’s video got me thinking on the topic and the role of format strings…

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Ok, it’s a long title, but it’s the best I came up with.

So here’s the story. At work there was a sales report with a nice matrix. On the rows, we had several product attributes like category, subcategory and so on. On the columns it was a bit weird because they wanted to show the day of sale, but also compare with the day of sale from the previous year, and with some particular logic for  comparison: if current year had equal or more days of sale than the previous one, it should match day one with day one and so on. However, if previous year had more days of sale, it should match starting from the back, so day one of sale would be matched with day 2 or 3 of previous year. Anyway, this is just to say that I put a measure saying what was the actual day of sale for current year, and previous year, as well as the actual date, because sales are different depending on the day of the week. Then we had cumulative sales, daily sales, for current and previous year. So far so good. Oh I forgot. On top of that there was like % over forecast measure that was the source of all ills.

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