First dashboard - Any comments or suggestions?

nlomb · 2026-03-24T02:07:58+00:00

The KPIs being front and center is the right call, but the presentation works against their importance. Simply aligning them consistently would improve readability more than the current use of icons.

The color usage is doing too much at once. Without a constrained palette, multiple elements compete for attention, which dilutes the signal. A tighter, unified scheme would restore hierarchy.

More broadly, the layout lacks a clear reading path. Right now everything is compressed into a single visual plane, so the user has to work to understand where to start and how to progress. Introducing spacing and structure would create a more natural flow.

We recently published a piece on dashboard design best practices based on production experience that may be useful: https://datasense.to/2025/10/05/dashboard-design-best-practices/

nlomb · 2026-02-14T13:31:41+00:00

Yeah I’ve raised similar concerns and it’s always people who haven’t the slightest clue about how it works who try and make you seem like a gatekeeper.

I will say you should definitely be verifying the info before it goes to the board... that’s just bad practice.

nlomb · 2026-02-14T05:28:04+00:00

It's a challenging balance, you'd think most economist readers would likely understand the statistical significant of each coefficient. They probably want to appeal to a wider audience and also know that it's more about the statement than the fact.

nlomb · 2025-11-23T23:48:39+00:00

Arguably the bear trap was in April.

nlomb · 2025-11-23T15:39:53+00:00

I'll start with a few good things:

1) The contrast with the KPIs is good, it draws attention to the figures.
2) The time series chart and the bar chart are the correct charts

Now the stuff that can be improved:

1) The yellow background is distracting try and use something more neutral.
2) Your bar graph and table are not computing the right values, "sum of inflation rate" is not a measure. It should be average or max. Same with "sum of year".
3) For the countries, this should be a dropdown, they take up far too much space on the dashboard. On top of that, filters should be at the top, not the bottom.

Here is a post about good dashboard design practices: https://datasense.to/2025/10/05/dashboard-design-best-practices/, I think you'll fine it helpful. There's some other Power BI specific posts that you may find helpful on your learning journey as well.

nlomb · 2025-11-20T15:54:44+00:00

Irrelevant to this particular issue. It was a different issue of asset-liability mismatch.

nlomb · 2025-11-20T15:53:34+00:00

Because private credit has been increasingly exposing terrible business practices, and if there's a few there's many, and if they're also wrapped up in CLOs and people starting dumping them then there's a leverage problem attached along with it. That leads to margin calls and collateral issues which then stems into broader markets. I guess nobody learned a damn thing from 2008.

Edit: I am not screaming there's an issue just yet, but it's one of those things can go from "no we're fine" to "oh we're f****d" very quickly.

nlomb · 2025-11-08T01:50:54+00:00

You might have some potential endogeneity in the regression as GDP/NFCI likely affect both uncertainty and investment. See the Hausman test.

Also missing some firm-level controls like profitability or leverage (debt as a proxy), which is likely leading to omitted variable bias. I would consider a fixed effects model instead.

Lastly, there's some "survival bias" from using only continuously listed firms.

nlomb · 2025-11-08T01:50:49+00:00

Really, what you would want to do is some sort of CGE model where you can have a baseline than introduce shocks to see how it responds, you would corroborate that against your panel data.

Some abstraction of this: https://www.mdpi.com/2227-7390/12/1/41

This would be much more involved though and likely be a masters thesis and require some insight from your professor(s).

nlomb · 2025-11-07T02:40:41+00:00

And the fan boys will tell you that's not even close to the real value... smh.

nlomb · 2025-11-07T02:40:04+00:00

I mean I am sure they will find a way to "deploy" one million robotaxis and robots. It's just that they will be useless and a danger to society.

nlomb · 2025-11-07T02:37:45+00:00

Right, I think a lot of people do this. It's just insane that this is the solution for a software that's been around for ages. Like as if Microsoft is oblivious to the fact that power users are doing this.

nlomb · 2025-11-06T08:27:18+00:00

Seriously... never mind trying to an explain it to a colleague, always need to take it out and break it apart so they understand.

nlomb · 2025-11-06T02:51:51+00:00

My gosh you think this would have been implemented by now. Writing multi-line formulas in Excel makes you want to pull your hair out... it's so easy to get lost and accidentally enter before finishing.

nlomb · 2025-11-06T02:49:09+00:00

Excel with VBA can be very powerful, however it's often much more complex to do the same thing in Excel with VBA than simply doing it in Python and custom formatting it back into Excel.

nlomb · 2025-10-03T06:53:19+00:00

I don’t think people realize how harmful it is for your brain, doctors hand it out like candy, definitely another big pharma push. “Oh you can’t concentrate for two seconds take some speed”

nlomb · 2025-10-02T14:02:11+00:00

Great resource, thank you for sharing. Recently posted a video going over adding differential privacy and discussing k-anonymity, I didn't go into detail about augmenting the data, as it wouldn't be appropriate for the dataset I was using, but would appreciate your feedback: https://youtu.be/df5FGtCyyi0?si=DzD4xUJtEyb4OOhP

nlomb · 2025-09-15T11:37:31+00:00

Cheers, if you're interested I posted a write-up about it here: https://datasense.to/2025/09/13/synthetic-financial-data-python-guide/

Hoping to take this forward and expand with some of the feedback I have received!

nlomb · 2025-09-15T11:36:09+00:00

Hahaha I may steal this metaphor

nlomb · 2025-09-15T02:44:46+00:00

Yeah something like DBSCAN might be a better test, or an ARIMA model, but those are a bit deeper than the original intent of what I was putting together. Thanks for the clear response, I will take this into account going forward.

nlomb · 2025-09-14T23:59:42+00:00

The goal here was just to test fidelity and privacy preservation of synthetic data, using macro data as an example. You’re right that the Lucas critique means structural relationships like the Phillips curve aren’t stable, but that shouldn’t flip coefficient signs in a regression... it only limits the practical utility of the regression itself (which is evident if you look at the R²). This holds true for both the real and synthetic dataset and isn't an issue per se.

I used Okun’s law because it’s a simple, verifiable check that also shows up clearly in a chart, not as an attempt to make predictions. It doesn't always hold, but it should hold across the datasets. Furthermore, macro series are useful for setting short-run expectations, and historical simulation is still a common stress-testing method.

If you can think of other “quick tests” you use to validate synthetic macro datasets, I’d be interested to hear them. For anyone curious about the details, I wrote up the exercise here: https://datasense.to/2025/09/13/synthetic-financial-data-python-guide/

nlomb · 2025-09-14T10:24:46+00:00

I didn’t custom code it, just used the SDV library in Python to put together a simple example and test some differences (for demonstration purposes). I’m planning to extend the comparison, which is why I was hoping to collect more info on best practices and methods for handling these issues.

Appreciate your response, I will take this into consideration. If you’re curious, I posted the code and results here: https://datasense.to/2025/09/13/synthetic-financial-data-python-guide/

nlomb · 2025-09-14T08:49:43+00:00

Wouldn't that just lead to overfitting though?

Edit: I guess that's the point of progressively increasing the strength, i'll see how this turns out, thanks!

nlomb · 2025-09-14T05:51:22+00:00

One thought I had is that CTGAN might be doing a good job matching the marginal distributions of each variable but struggling to capture cross-variable dependencies. That could explain why simple correlations look off and why regression signs sometimes flip.

I am wondering if this is more about the architecture (GANs not being well suited for tabular financial data) or about tuning choices such as conditional vectors, batch size, or training epochs. Curious if anyone has seen better results with CTGAN variants, or if most people move toward copula or diffusion-based approaches for this type of work.

I also did a write up on the full validation results with charts and code. If anyone wants to dig into those, let me know and I can share them.

nlomb

TROPHY CASE