WEBVTT
00:00:04.879 --> 00:00:06.959
Welcome to the Dashboard Effect Podcast.
00:00:07.040 --> 00:00:07.839
I'm Brick Thompson.
00:00:08.080 --> 00:00:08.960
And I'm Landon Oaks.
00:00:09.279 --> 00:00:35.200
Landon, we wanted to talk in this episode about simplicity in designing dashboards and why often it might seem like a complex-looking dashboard with lots of colors and lots of visualizations and all sorts of things going on is a higher value dashboard than one that is simple, maybe has three or four or five visualizations and a couple of bright KPIs.
00:00:35.600 --> 00:00:43.759
Often that simple one is actually far more value, or brings far more value and is a lot more valuable for the company.
00:00:47.119 --> 00:00:58.240
When we think about designing dashboards here, our analytics engineers are trying to figure out how do they get the information to the viewer as intuitively as possible.
00:00:58.799 --> 00:01:02.320
Sometimes we say, could you show it to your elderly grandparent?
00:01:02.560 --> 00:01:08.319
And they would know maybe not exactly what's going on in the business, but they definitely would know how it's going.
00:01:08.560 --> 00:01:16.879
And so the business user is able to look at it and really quickly infer how it's going and it can drive decisions and actions quickly.
00:01:17.120 --> 00:01:24.879
Those are the dashboards that end up getting adopted well by businesses and actually giving ROI.
00:01:25.439 --> 00:01:34.319
But there's sort of a an unexpected thing, which is those simple dashboards are often the most complex to put together behind the scenes.
00:01:34.480 --> 00:01:37.120
And so I thought we'd just talk about a couple examples of that.
00:01:37.599 --> 00:01:38.879
Yeah, definitely.
00:01:39.120 --> 00:01:42.879
I think um to your point, the simpler the better.
00:01:43.040 --> 00:01:54.879
And I am uh I'm a bit of a I I have a bit of this problem where you know it's very easy for me to of to ignore all the dashboards and work on my day-to-day work.
00:01:55.120 --> 00:02:02.079
But I have this one specific one, really, really simple, um, that just simply shows me how much hours my team logged and what they should have.
00:02:02.239 --> 00:02:05.519
I reference it all the time because I look at it for about two minutes.
00:02:06.000 --> 00:02:11.039
And it should it, you know, very quickly whether they're meeting utilization targets and that type of thing.
00:02:11.439 --> 00:02:11.759
Yeah.
00:02:12.080 --> 00:02:13.840
So you can have a simple dashboard.
00:02:13.919 --> 00:02:19.520
Uh uh, we went and looked for a couple of examples of what might look simple but take a lot behind the scenes.
00:02:19.680 --> 00:02:24.319
One of the ones that we did for a client one time was a salesperson ROI dashboard.
00:02:24.479 --> 00:02:33.840
And so they wanted to be able to see how is a new salesperson performing against an expected baseline over their first 36 months in the business.
00:02:34.000 --> 00:02:47.120
Um, and one of the complicated things they had was that the new salesperson often inherited existing accounts from a departed salesperson or someone that moved to a different uh division or uh group.
00:02:47.520 --> 00:03:10.240
And the logic to make that work correctly so that you could give the new person credit for the old person's sales and show that correctly on the sort of the line chart against the baseline and give good numbers about what is the actual profitability of this salesperson, at least profitability contribution, took a lot of work.
00:03:10.479 --> 00:03:27.840
There was a lot of modeling, a lot of getting the data right, a bunch of sort of sorting through business rules, working with the business managers and the owners of these uh areas to figure out what's the right way to re represent this and then do that correctly in the data.
00:03:28.080 --> 00:03:33.199
It seemed obvious when they were laying out the requirements to us.
00:03:33.439 --> 00:03:38.560
Okay, that sounds simple enough, but when we got down to building it, we realized, oh, there's a lot more to it.
00:03:38.800 --> 00:03:41.680
And that's a that's a pretty common thing we find.
00:03:41.919 --> 00:03:57.439
And in fact, in order to make the uh report or dashboard as simple as possible and as effective as possible, you often end up with those complexities behind the scenes to get them really good and make sure they're validated and that type of thing.
00:03:57.680 --> 00:03:59.680
You you were mentioning a different example.
00:03:59.840 --> 00:04:03.520
I think it was point-in-time accounts receivable or AR reporting.
00:04:03.680 --> 00:04:05.439
That's one that you've worked on a number of times.
00:04:05.599 --> 00:04:07.360
Maybe you could tell us a little bit about that.
00:04:07.919 --> 00:04:08.879
Yeah, definitely.
00:04:09.120 --> 00:04:19.680
So you know, point-in-time AR is uh a bit of a word that strikes some fear into some people where you know you just want to see at what point in time did this customer what did they owe?
00:04:19.759 --> 00:04:21.680
Um, what was the aging at?
00:04:22.160 --> 00:04:31.199
And so in general, you know, some ERPs have a way to sh to go back in time and say, you know, as of last month, what did my top customer owe?
00:04:31.439 --> 00:04:33.439
Um others don't have that ability at all.
00:04:33.600 --> 00:04:36.399
You can only see your current AR of zero analysis.
00:04:36.720 --> 00:04:44.399
Um so it's one of those things where the back-end work is more than you would think, right?
00:04:44.560 --> 00:04:46.079
So there's two ways to really accomplish it.
00:04:46.240 --> 00:04:50.959
One is the kind of easier way, which is to just simply take a snapshot of your data.
00:04:51.279 --> 00:04:54.079
So you just say, yeah, what's my AR right now?
00:04:54.160 --> 00:04:59.040
Okay, I'm gonna take a copy of that, I'm gonna put it over here, and then I'm gonna stack them all on top of each other.
00:04:59.279 --> 00:05:02.399
Um the one issue with that is people make mistakes, right?
00:05:02.560 --> 00:05:08.399
Somebody will enter the wrong, they'll put an extra zero on the end of a check or something, you know, that comes in.
00:05:08.560 --> 00:05:10.079
That's a huge difference.
00:05:10.160 --> 00:05:14.560
Aaron Powell And so they'll have to go back and correct that, but your snapshot maybe doesn't get it.
00:05:14.800 --> 00:05:16.079
It's solid, it's stuck in time.
00:05:16.319 --> 00:05:16.480
Yeah.
00:05:16.720 --> 00:05:21.920
So, you know, where where a lot of the complexity will come is you ideally want to avoid those mistakes.
00:05:22.160 --> 00:05:28.959
Um so what you can do is you know, you you actually go look at like, okay, here are my invoices, here are my payment history.
00:05:29.040 --> 00:05:30.720
I'm gonna merge these two up.
00:05:30.959 --> 00:05:34.480
I'm going to recreate history over time.
00:05:34.720 --> 00:05:38.079
And what that does is when somebody makes an update, it also updates your history.
00:05:39.279 --> 00:05:40.560
And that can be quite complicated.
00:05:40.639 --> 00:05:52.879
You know, you have invoices, credit memos, um, returns, all kinds of different transactions that can go into it that you need to figure out how do I handle these specific transactions, what do I need to ignore, et cetera?
00:05:53.040 --> 00:05:56.000
Um that yeah, it turns into a bit of a back and forth.
00:05:56.079 --> 00:06:02.000
And a lot of times we'll we'll kind of show data to the to the customer that they didn't even know was happening.
00:06:02.319 --> 00:06:03.120
They'll be shocked by it.
00:06:03.199 --> 00:06:03.920
Aaron Powell It is interesting.
00:06:04.079 --> 00:06:05.279
So you do all that work.
00:06:05.600 --> 00:06:10.160
The snapshot does seem pretty straightforward, but there's still some implementation issues.
00:06:10.319 --> 00:06:15.680
Doing it the way to reconstruct it so that you catch those changes uh is a lot more complicated.
00:06:15.920 --> 00:06:17.839
The end result, though, often looks pretty simple.
00:06:18.079 --> 00:06:22.399
You might see um AR balance over time on a line chart.
00:06:22.560 --> 00:06:29.680
Um you might see, you might want to be able to see how does that balance change by um the the buckets, the aging buckets.
00:06:30.240 --> 00:06:33.279
So my 30 to 60 days, how is that how's that bucket changing?
00:06:33.439 --> 00:06:37.040
How is my 60 to 90 day bucket changing, that type of thing?
00:06:37.279 --> 00:06:42.879
It looks simple on the page, but to make that happen really well can be really challenging.
00:06:43.120 --> 00:06:46.639
You can get your DSO out of it, and how is my DSO changing over time?
00:06:46.879 --> 00:06:50.160
Again, usually a pretty simple line chart.
00:06:50.399 --> 00:06:58.560
But the engineering that goes into the back end to make that happen and to make it actually correct and useful takes a lot of work.
00:06:59.040 --> 00:07:00.240
Yeah, absolutely.
00:07:00.879 --> 00:07:03.839
So another example we thought of was uh price-volume mix.
00:07:04.000 --> 00:07:06.800
This is a pretty common thing that people want to see.
00:07:06.879 --> 00:07:15.120
How are changes in price and changes in mix, changes in volume, how are all those things affecting our top line, our sales.
00:07:15.920 --> 00:07:22.399
And so very often that uh chart or report looks like a waterfall chart.
00:07:22.639 --> 00:07:34.240
Um pretty simple, simple chart to look at, simple to interpret, but to get that right on the back end so it's actually reporting the right data takes a lot of work.
00:07:34.319 --> 00:07:42.480
And it usually comes in the form of the fact that every company thinks about those calculations a little bit differently.
00:07:42.720 --> 00:07:45.920
And so you can't just apply a simple template to that.
00:07:46.160 --> 00:07:59.519
You really need to go deep with the people in the business that understand the business rules around that, and then code that into both the data modeling and into the measures and the KPIs so that it comes out correctly.
00:07:59.759 --> 00:08:04.480
This is one where I've definitely had the experience with a client where they're saying, how hard could it be?
00:08:04.720 --> 00:08:08.720
I'll just drag a waterfall chart under the table, connect it to the data, and it's there.
00:08:08.959 --> 00:08:10.160
It's not that simple.
00:08:10.319 --> 00:08:13.040
There are so many things that can make it go wrong.
00:08:13.279 --> 00:08:18.800
And uh and in fact, that example I was thinking about, um, you know, the client showed the client an example of that.
00:08:18.959 --> 00:08:20.560
He said, well, that's not how I want it at all.
00:08:20.639 --> 00:08:21.920
And then it all sort of clicked.
00:08:22.079 --> 00:08:25.680
Okay, we need to do all the groundwork to make this come out right.
00:08:26.000 --> 00:08:30.879
So I guess that's just another another example of uh of how that can happen.
00:08:31.040 --> 00:08:33.279
And this happens all over the place.
00:08:33.519 --> 00:08:35.440
I mean, maybe not every report.
00:08:35.600 --> 00:08:38.559
Some truly are simple, but pretty much.
00:08:39.120 --> 00:08:39.279
Yeah.
00:08:39.600 --> 00:08:39.759
Yeah.
00:08:39.840 --> 00:08:45.360
And I mean, even to add on to it too, is um at the at behind all these reports, you have what's called a model, right?
00:08:45.600 --> 00:08:47.519
It's the backbone of everything.
00:08:47.759 --> 00:08:53.679
Um so you're not just building a model specifically for salesperson ROI, specifically for AR.
00:08:53.840 --> 00:08:58.240
Ideally, you want that thing to be able to be used for other areas of the business too.
00:08:58.480 --> 00:08:58.639
Right.
00:08:58.879 --> 00:09:09.519
Because part of the issue is is, you know, if you have like in this case, we have three different completely separate models, that's a lot of work to keep those updated and maintained over time.
00:09:09.759 --> 00:09:14.399
Um so, you know, it's almost like complexity for simplicity, right?
00:09:14.480 --> 00:09:21.120
You're you're gonna make it a little more complicated to be able to make this thing reusable, however, your your future self is gonna thank you.
00:09:21.440 --> 00:09:21.759
Yeah.
00:09:21.919 --> 00:09:33.039
It's almost like if you see a complex report, even though they can look pretty and sort of fancy with lots of dials and things going on, it's probably an unfinished report, an untested report.
00:09:33.200 --> 00:09:37.200
It hasn't been tested in you know, in an actual live fire.
00:09:37.519 --> 00:09:43.600
Because if people were using it that way, they would immediately request changes to make it simpler to read.
00:09:43.840 --> 00:09:51.120
So yeah, complicated in terms of the final view is not necessarily better, almost always worse.
00:09:51.440 --> 00:09:52.159
Yeah, absolutely.
00:09:52.480 --> 00:09:53.039
So all right.
00:09:53.120 --> 00:09:53.679
Well, great.
00:09:53.840 --> 00:09:54.799
Just wanted to cover that.
00:09:54.960 --> 00:09:55.840
Thanks for the discussion.
00:09:55.919 --> 00:09:56.399
It was interesting.
00:09:57.039 --> 00:09:57.519
Yeah, thank you.
00:09:57.759 --> 00:09:58.000
All right.
00:09:58.240 --> 00:10:04.480
Uh one final note if you uh like our content, what we're discussing here, don't forget to like and subscribe for for more to come.
00:10:04.799 --> 00:10:11.039
Or uh navigate on over to blue margin.com where we have uh a history and other useful articles you might like.
00:00:04.879 --> 00:00:06.959
Welcome to the Dashboard Effect Podcast.
00:00:07.040 --> 00:00:07.839
I'm Brick Thompson.
00:00:08.080 --> 00:00:08.960
And I'm Landon Oaks.
00:00:09.279 --> 00:00:35.200
Landon, we wanted to talk in this episode about simplicity in designing dashboards and why often it might seem like a complex-looking dashboard with lots of colors and lots of visualizations and all sorts of things going on is a higher value dashboard than one that is simple, maybe has three or four or five visualizations and a couple of bright KPIs.
00:00:35.600 --> 00:00:43.759
Often that simple one is actually far more value, or brings far more value and is a lot more valuable for the company.
00:00:47.119 --> 00:00:58.240
When we think about designing dashboards here, our analytics engineers are trying to figure out how do they get the information to the viewer as intuitively as possible.
00:00:58.799 --> 00:01:02.320
Sometimes we say, could you show it to your elderly grandparent?
00:01:02.560 --> 00:01:08.319
And they would know maybe not exactly what's going on in the business, but they definitely would know how it's going.
00:01:08.560 --> 00:01:16.879
And so the business user is able to look at it and really quickly infer how it's going and it can drive decisions and actions quickly.
00:01:17.120 --> 00:01:24.879
Those are the dashboards that end up getting adopted well by businesses and actually giving ROI.
00:01:25.439 --> 00:01:34.319
But there's sort of a an unexpected thing, which is those simple dashboards are often the most complex to put together behind the scenes.
00:01:34.480 --> 00:01:37.120
And so I thought we'd just talk about a couple examples of that.
00:01:37.599 --> 00:01:38.879
Yeah, definitely.
00:01:39.120 --> 00:01:42.879
I think um to your point, the simpler the better.
00:01:43.040 --> 00:01:54.879
And I am uh I'm a bit of a I I have a bit of this problem where you know it's very easy for me to of to ignore all the dashboards and work on my day-to-day work.
00:01:55.120 --> 00:02:02.079
But I have this one specific one, really, really simple, um, that just simply shows me how much hours my team logged and what they should have.
00:02:02.239 --> 00:02:05.519
I reference it all the time because I look at it for about two minutes.
00:02:06.000 --> 00:02:11.039
And it should it, you know, very quickly whether they're meeting utilization targets and that type of thing.
00:02:11.439 --> 00:02:11.759
Yeah.
00:02:12.080 --> 00:02:13.840
So you can have a simple dashboard.
00:02:13.919 --> 00:02:19.520
Uh uh, we went and looked for a couple of examples of what might look simple but take a lot behind the scenes.
00:02:19.680 --> 00:02:24.319
One of the ones that we did for a client one time was a salesperson ROI dashboard.
00:02:24.479 --> 00:02:33.840
And so they wanted to be able to see how is a new salesperson performing against an expected baseline over their first 36 months in the business.
00:02:34.000 --> 00:02:47.120
Um, and one of the complicated things they had was that the new salesperson often inherited existing accounts from a departed salesperson or someone that moved to a different uh division or uh group.
00:02:47.520 --> 00:03:10.240
And the logic to make that work correctly so that you could give the new person credit for the old person's sales and show that correctly on the sort of the line chart against the baseline and give good numbers about what is the actual profitability of this salesperson, at least profitability contribution, took a lot of work.
00:03:10.479 --> 00:03:27.840
There was a lot of modeling, a lot of getting the data right, a bunch of sort of sorting through business rules, working with the business managers and the owners of these uh areas to figure out what's the right way to re represent this and then do that correctly in the data.
00:03:28.080 --> 00:03:33.199
It seemed obvious when they were laying out the requirements to us.
00:03:33.439 --> 00:03:38.560
Okay, that sounds simple enough, but when we got down to building it, we realized, oh, there's a lot more to it.
00:03:38.800 --> 00:03:41.680
And that's a that's a pretty common thing we find.
00:03:41.919 --> 00:03:57.439
And in fact, in order to make the uh report or dashboard as simple as possible and as effective as possible, you often end up with those complexities behind the scenes to get them really good and make sure they're validated and that type of thing.
00:03:57.680 --> 00:03:59.680
You you were mentioning a different example.
00:03:59.840 --> 00:04:03.520
I think it was point-in-time accounts receivable or AR reporting.
00:04:03.680 --> 00:04:05.439
That's one that you've worked on a number of times.
00:04:05.599 --> 00:04:07.360
Maybe you could tell us a little bit about that.
00:04:07.919 --> 00:04:08.879
Yeah, definitely.
00:04:09.120 --> 00:04:19.680
So you know, point-in-time AR is uh a bit of a word that strikes some fear into some people where you know you just want to see at what point in time did this customer what did they owe?
00:04:19.759 --> 00:04:21.680
Um, what was the aging at?
00:04:22.160 --> 00:04:31.199
And so in general, you know, some ERPs have a way to sh to go back in time and say, you know, as of last month, what did my top customer owe?
00:04:31.439 --> 00:04:33.439
Um others don't have that ability at all.
00:04:33.600 --> 00:04:36.399
You can only see your current AR of zero analysis.
00:04:36.720 --> 00:04:44.399
Um so it's one of those things where the back-end work is more than you would think, right?
00:04:44.560 --> 00:04:46.079
So there's two ways to really accomplish it.
00:04:46.240 --> 00:04:50.959
One is the kind of easier way, which is to just simply take a snapshot of your data.
00:04:51.279 --> 00:04:54.079
So you just say, yeah, what's my AR right now?
00:04:54.160 --> 00:04:59.040
Okay, I'm gonna take a copy of that, I'm gonna put it over here, and then I'm gonna stack them all on top of each other.
00:04:59.279 --> 00:05:02.399
Um the one issue with that is people make mistakes, right?
00:05:02.560 --> 00:05:08.399
Somebody will enter the wrong, they'll put an extra zero on the end of a check or something, you know, that comes in.
00:05:08.560 --> 00:05:10.079
That's a huge difference.
00:05:10.160 --> 00:05:14.560
Aaron Powell And so they'll have to go back and correct that, but your snapshot maybe doesn't get it.
00:05:14.800 --> 00:05:16.079
It's solid, it's stuck in time.
00:05:16.319 --> 00:05:16.480
Yeah.
00:05:16.720 --> 00:05:21.920
So, you know, where where a lot of the complexity will come is you ideally want to avoid those mistakes.
00:05:22.160 --> 00:05:28.959
Um so what you can do is you know, you you actually go look at like, okay, here are my invoices, here are my payment history.
00:05:29.040 --> 00:05:30.720
I'm gonna merge these two up.
00:05:30.959 --> 00:05:34.480
I'm going to recreate history over time.
00:05:34.720 --> 00:05:38.079
And what that does is when somebody makes an update, it also updates your history.
00:05:39.279 --> 00:05:40.560
And that can be quite complicated.
00:05:40.639 --> 00:05:52.879
You know, you have invoices, credit memos, um, returns, all kinds of different transactions that can go into it that you need to figure out how do I handle these specific transactions, what do I need to ignore, et cetera?
00:05:53.040 --> 00:05:56.000
Um that yeah, it turns into a bit of a back and forth.
00:05:56.079 --> 00:06:02.000
And a lot of times we'll we'll kind of show data to the to the customer that they didn't even know was happening.
00:06:02.319 --> 00:06:03.120
They'll be shocked by it.
00:06:03.199 --> 00:06:03.920
Aaron Powell It is interesting.
00:06:04.079 --> 00:06:05.279
So you do all that work.
00:06:05.600 --> 00:06:10.160
The snapshot does seem pretty straightforward, but there's still some implementation issues.
00:06:10.319 --> 00:06:15.680
Doing it the way to reconstruct it so that you catch those changes uh is a lot more complicated.
00:06:15.920 --> 00:06:17.839
The end result, though, often looks pretty simple.
00:06:18.079 --> 00:06:22.399
You might see um AR balance over time on a line chart.
00:06:22.560 --> 00:06:29.680
Um you might see, you might want to be able to see how does that balance change by um the the buckets, the aging buckets.
00:06:30.240 --> 00:06:33.279
So my 30 to 60 days, how is that how's that bucket changing?
00:06:33.439 --> 00:06:37.040
How is my 60 to 90 day bucket changing, that type of thing?
00:06:37.279 --> 00:06:42.879
It looks simple on the page, but to make that happen really well can be really challenging.
00:06:43.120 --> 00:06:46.639
You can get your DSO out of it, and how is my DSO changing over time?
00:06:46.879 --> 00:06:50.160
Again, usually a pretty simple line chart.
00:06:50.399 --> 00:06:58.560
But the engineering that goes into the back end to make that happen and to make it actually correct and useful takes a lot of work.
00:06:59.040 --> 00:07:00.240
Yeah, absolutely.
00:07:00.879 --> 00:07:03.839
So another example we thought of was uh price-volume mix.
00:07:04.000 --> 00:07:06.800
This is a pretty common thing that people want to see.
00:07:06.879 --> 00:07:15.120
How are changes in price and changes in mix, changes in volume, how are all those things affecting our top line, our sales.
00:07:15.920 --> 00:07:22.399
And so very often that uh chart or report looks like a waterfall chart.
00:07:22.639 --> 00:07:34.240
Um pretty simple, simple chart to look at, simple to interpret, but to get that right on the back end so it's actually reporting the right data takes a lot of work.
00:07:34.319 --> 00:07:42.480
And it usually comes in the form of the fact that every company thinks about those calculations a little bit differently.
00:07:42.720 --> 00:07:45.920
And so you can't just apply a simple template to that.
00:07:46.160 --> 00:07:59.519
You really need to go deep with the people in the business that understand the business rules around that, and then code that into both the data modeling and into the measures and the KPIs so that it comes out correctly.
00:07:59.759 --> 00:08:04.480
This is one where I've definitely had the experience with a client where they're saying, how hard could it be?
00:08:04.720 --> 00:08:08.720
I'll just drag a waterfall chart under the table, connect it to the data, and it's there.
00:08:08.959 --> 00:08:10.160
It's not that simple.
00:08:10.319 --> 00:08:13.040
There are so many things that can make it go wrong.
00:08:13.279 --> 00:08:18.800
And uh and in fact, that example I was thinking about, um, you know, the client showed the client an example of that.
00:08:18.959 --> 00:08:20.560
He said, well, that's not how I want it at all.
00:08:20.639 --> 00:08:21.920
And then it all sort of clicked.
00:08:22.079 --> 00:08:25.680
Okay, we need to do all the groundwork to make this come out right.
00:08:26.000 --> 00:08:30.879
So I guess that's just another another example of uh of how that can happen.
00:08:31.040 --> 00:08:33.279
And this happens all over the place.
00:08:33.519 --> 00:08:35.440
I mean, maybe not every report.
00:08:35.600 --> 00:08:38.559
Some truly are simple, but pretty much.
00:08:39.120 --> 00:08:39.279
Yeah.
00:08:39.600 --> 00:08:39.759
Yeah.
00:08:39.840 --> 00:08:45.360
And I mean, even to add on to it too, is um at the at behind all these reports, you have what's called a model, right?
00:08:45.600 --> 00:08:47.519
It's the backbone of everything.
00:08:47.759 --> 00:08:53.679
Um so you're not just building a model specifically for salesperson ROI, specifically for AR.
00:08:53.840 --> 00:08:58.240
Ideally, you want that thing to be able to be used for other areas of the business too.
00:08:58.480 --> 00:08:58.639
Right.
00:08:58.879 --> 00:09:09.519
Because part of the issue is is, you know, if you have like in this case, we have three different completely separate models, that's a lot of work to keep those updated and maintained over time.
00:09:09.759 --> 00:09:14.399
Um so, you know, it's almost like complexity for simplicity, right?
00:09:14.480 --> 00:09:21.120
You're you're gonna make it a little more complicated to be able to make this thing reusable, however, your your future self is gonna thank you.
00:09:21.440 --> 00:09:21.759
Yeah.
00:09:21.919 --> 00:09:33.039
It's almost like if you see a complex report, even though they can look pretty and sort of fancy with lots of dials and things going on, it's probably an unfinished report, an untested report.
00:09:33.200 --> 00:09:37.200
It hasn't been tested in you know, in an actual live fire.
00:09:37.519 --> 00:09:43.600
Because if people were using it that way, they would immediately request changes to make it simpler to read.
00:09:43.840 --> 00:09:51.120
So yeah, complicated in terms of the final view is not necessarily better, almost always worse.
00:09:51.440 --> 00:09:52.159
Yeah, absolutely.
00:09:52.480 --> 00:09:53.039
So all right.
00:09:53.120 --> 00:09:53.679
Well, great.
00:09:53.840 --> 00:09:54.799
Just wanted to cover that.
00:09:54.960 --> 00:09:55.840
Thanks for the discussion.
00:09:55.919 --> 00:09:56.399
It was interesting.
00:09:57.039 --> 00:09:57.519
Yeah, thank you.
00:09:57.759 --> 00:09:58.000
All right.
00:09:58.240 --> 00:10:04.480
Uh one final note if you uh like our content, what we're discussing here, don't forget to like and subscribe for for more to come.
00:10:04.799 --> 00:10:11.039
Or uh navigate on over to blue margin.com where we have uh a history and other useful articles you might like.
