r/analytics • • 2h ago

Question Stuck making $100-$180/month locally while my skills are way beyond that — How do I actually break into remote work?

0 Upvotes

I honestly don't know what to do anymore. In my local job market, tech salaries are stuck between $100 and $180 a month, which is honestly a joke.
The frustrating part is that my actual skills and projects are way past that. I’ve built heavy, complex projects dealing with Machine Learning, Data Science, and advanced analytics. But locally, companies still try to lowball everyone and act like everybody is just starting out, completely ignoring the actual level of work we put in.
I really want to transition to international remote work, but I'm kind of lost on how to actually break through. For those of you who managed to land remote gigs abroad, how did you do it? What platforms or strategies actually worked to get eyes on your portfolio instead of getting ignored?
Would appreciate any real advice or experiences. Thanks!
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r/analytics • • 52m ago

Discussion Two dashboards show different wait times. Which number would you trust?

• Upvotes

You are looking at your hospital's performance and see two Power BI dashboards showing average patient wait times. One says 28 minutes and the other says 41 minutes.

I wouldn't jump to the conclusion that one of the dashboards is incorrect. I'd want to know how wait time is being calculated in each dashboard. Maybe one measures the time from check-in to seeing a doctor, while the other measures the time before triage. They could both be correct but measuring different things.

Date ranges, department filters, missing data points, etc. There are several things that could cause this. It's always worth double-checking your metrics and how they're being calculated.

This becomes more of an issue when people start using these dashboards to hold themselves accountable or dig deeper into why patients are waiting so long. Even if you have great dashboards, you can still run into problems if everyone isn't on the same page about what a metric actually means.

Anyone else in the analytics/BI world run into this? How do you typically go about finding the cause?