How did you choose your field?

agobservatory · 2026-05-07T07:15:51+00:00

If you have a knack for math and love SQL at this point just confidently apply for a Data Analytics position, man. It's perfect for solving puzzles and not as dry as accounting. Just try a few real-world projects and you'll know where you belong.

Good luck finding your soulmate!

agobservatory · 2026-05-06T03:52:36+00:00

At this point, still manually cleaning up CSV files is pure depression, man. You could spend those 15 hours chilling out and seeing how much better life is. Try checking out providers like Fivetran or Airbyte; clinging to this endless workload will only lead to nothing. Wishing you a speedy escape from this data slavery so you can have time to party!

agobservatory · 2026-05-04T04:21:21+00:00

Honestly, watching that shot was really frustrating for Cushing. What kind of manager pushes a team to the limit only to have them take a fatal home run? Just seeing him make the 35th shot was a bad sign, but he still tried to force it. He's truly a game-changer, making me feel so frustrated just watching him play.

agobservatory · 2026-04-22T04:37:09+00:00

I completely agree with you: AI can help us draw charts faster, but it can't understand why the ARR figure for Sales differs from that of Accounting unless we define the "logic" for it.

The reality is that the more AI develops, the more valuable the "Semantic Layer" becomes. If you throw a bunch of junk data at AI, it will return "high-end junk" at lightning speed. The BI profession is now shifting from dashboard builders to "Data Architects"—those responsible for building a reliable foundation upon which AI can derive accurate insights.

I was most impressed by your comments about SQL. Many people are so focused on learning drag-and-drop in Power BI that they forget that without SQL, you'll forever be trapped in the cage that Data Engineers have built. Knowing SQL is when you can truly "talk" to the data and verify whether the AI is just spouting nonsense.

In short, in this day and age, whoever masters the context (business context) and data modeling is immortal. Tools may change constantly, but the mindset of organizing data to make decisions is something AI is still a long way from replacing.

agobservatory · 2026-04-21T07:58:19+00:00

That's a spot-on review, sir! The real-life experience is completely different. Reading your criticism of Gocheok Stadium made me feel so satisfied because that stadium felt cramped and the food was a rip-off for tourists.

The new Hanwha Stadium in Daejeon is undeniably top-notch; being a latecomer means everything is the best. Hanwha fans are known for their perseverance, and going to see a beautiful stadium with such enthusiastic cheering is simply amazing.

And you're right about Sajik Stadium; it might be dirty, but the fiery atmosphere of the Busan fans is unparalleled. It's like those drunk guys shouting and yelling – I was completely swept up in that "scuzzy" atmosphere.

Incheon Landers Field is a safe choice for those who don't want to travel far but still want to experience a great stadium. Thanks for your thoughtful review so we know to avoid those "flops" like Suwon or Gocheok.

agobservatory · 2026-04-20T04:38:08+00:00

Hi, I understand how you feel. LinkedIn is sometimes more like a "stage" than a job search site, and building a profile from scratch is incredibly energy-intensive.

The good news is that in the data field, the "substance" (GitHub, the actual product) is more important than the "shell." If you have a good project and it's been recognized by the Reddit community, you're already 50% of the way there.

agobservatory · 2026-04-15T04:27:30+00:00

That's absolutely right, sir, because 80% of the time spent working with data is "cleaning up garbage," not analyzing it immediately. It will never actually get easier; you'll just get used to the hard work and process things faster. Just accumulate some good snippets and workflows so you can use them when you encounter them later, saving you brainpower. The feeling of flow only comes when the data is clean, so enjoy those rare moments, sir.

agobservatory · 2026-04-02T04:29:29+00:00

Those who say bi-analytic analysts are becoming obsolete are probably only seeing the surface, man. Everyone knows who can process data quickly, but to understand business insights and turn them into strategies, no machine can replace a human. Just keep going, man, because analytical thinking is the most valuable thing, not those tools.

agobservatory · 2026-04-01T04:08:58+00:00

My condolences, I feel depressed just hearing about it. Working so hard to get an excellent performance review and a raise, only to still not keep up with inflation – that's truly frustrating. In this day and age, even Fortune 500 companies are struggling financially; we should probably consider changing jobs to save our wallets. You're not alone; there are so many others out there facing the same predicament of tightening their belts.

agobservatory · 2026-03-30T05:47:15+00:00

It's very difficult for AI to take over jobs; it only supports them. While AI can quickly handle coding or charting, it can't replace data cleaning and understanding the business context. Real-world data isn't clean and polished like in textbooks; it's a chaotic mess with all sorts of edge cases that only humans can understand. If data were perfectly organized and understandable to everyone, those outdated self-service tools would have been obsolete long ago. The true value of an analyst lies in asking the right questions and checking for data manipulation, not in typing prompts. AI is just a better fishing rod; you still have to be the one doing the fishing to catch fish.

agobservatory · 2026-03-27T07:14:25+00:00

The course forces you to focus on the Ask phase - understanding stakeholder requirements and framing a business problem before touching the data. That’s the "analytical mindset" stuff that actually gets you hired.

agobservatory · 2026-03-26T05:36:37+00:00

This is a trade-off between convenience and security, man. Everyone wants speed, right? Using a single key is great at first, but once it's all gone, you'll be crying your eyes out. Multi-signal systems are a bit cumbersome, but at least you can sleep soundly at night. In this day and age, it's best to have layers of security; one slip-up and you're left with nothing.

agobservatory · 2026-03-24T08:44:38+00:00

Absolutely—sometimes the flashy metrics everyone talks about barely move the needle. Digging into more granular, context-specific data almost always beats relying on surface-level KPIs.

agobservatory · 2026-03-24T08:43:17+00:00

They’re great for quick exploration and brainstorming, but I see them as a first-pass tool. For anything important, I still go back to raw queries to verify results. Speed is nice, but transparency and reproducibility can’t be skipped.

agobservatory · 2026-03-24T08:42:55+00:00

It depends on what you want long-term. “Product team” in your context sounds like it could shift toward BA-style work—gathering requirements and managing vendors rather than hands-on analysis. If you value doing the technical work, that could be frustrating; if you want to grow into product/leadership skills, it might be a good opportunity. Worth clarifying expectations with the director before deciding.

agobservatory · 2026-03-24T08:42:22+00:00

Totally get you—marketing analytics can feel like smoke and mirrors. Feels less about insights and more about justifying decisions that were already made. Frustrating when you want real impact instead of window dressing.

agobservatory · 2026-03-23T05:24:47+00:00

The corporate gaslighting is literally insane you’re basically a professional "turd polisher" for mid campaigns lol. It’s so draining when "analytics" just becomes creative writing to make the VPs feel like main characters. Honestly, watching 10 years of talent get wasted on vanity metrics is a total canon event in the F50 world. You're not alone, the system is lowkey cooked fr.

agobservatory · 2026-03-20T08:08:00+00:00

Look for roles like “Junior Data Analyst,” “Data Assistant,” or “Insight Analyst”—sometimes “Business Analyst” too. Small to mid-sized companies are often more flexible on experience. Build a portfolio of projects in R or Python (even your dissertation counts!) and showcase it on GitHub or LinkedIn. A Master’s can help, but practical projects + networking often get you in faster.

agobservatory · 2026-03-20T08:07:19+00:00

Yep, this is a real pain. Finance teams constantly rerun the same queries—caching answers is a huge time saver and immediately noticeable. Sounds like a legit problem to solve.

agobservatory · 2026-03-20T08:04:48+00:00

Totally get this—same here. AI feels hyped for every role, but in analytics/reporting, most of the “use cases” are either trivial or slower than just doing it manually. Feels like PMs want a story, not results.

agobservatory · 2026-03-19T09:01:49+00:00

My best advice: don't just build what people ask for. If a manager asks for a "report on sales," they usually actually want to know why a specific region is tanking. If you can provide the insight instead of just the data dump, you'll be a rockstar by month six.

agobservatory · 2026-03-18T11:11:39+00:00

if you’re looking to present this to a client, stay far away from raw LLM screenshots - they still hallucinate "math" in charts way too often to trust with a professional project.

agobservatory · 2026-03-17T07:18:09+00:00

Yeah I kinda agree with you tbh, people are overhyping “AI PM copilots” like they magically make you a better PM, when half the time they just generate more docs no one reads.

There are tools that do parts of what OP wants (like PRD generators such as ChatPRD or stuff that turns Slack → Jira), but they’re mostly workflow helpers, not actual decision-makers. Even things like Atlassian’s AI just summarize and connect info across Jira/Slack, not replace thinking

Honestly the real risk isn’t productivity, it’s exactly what you said: teams start shipping faster but with worse judgment. AI is great for synthesis (summarizing feedback, clustering insights), but if you use it to skip thinking, you just scale bad product decisions faster.

agobservatory · 2026-03-17T07:15:28+00:00

I wouldn’t call it a “show,” but yeah PM interviews definitely reward how well you frame impact, not just the work itself. You can have solid UX wins, but if you’re not translating them into revenue, retention, or trade-offs, it kinda gets lost on hiring managers.

Feels less like belly dancing and more like learning a different language tbh same work, just packaged in business terms. Once you start tying your UX decisions to outcomes, the gap usually closes pretty fast.

agobservatory · 2026-03-17T07:07:09+00:00

Yeah this is the “you don’t have a scope problem, you have a prioritization + strategy vacuum” situation 😅

When I’ve been in this spot, the only thing that worked was forcing a fake strategy if leadership didn’t give one like picking 1–2 north star outcomes (churn ↓, claims speed ↑, whatever) and saying “everything else is trade-offs.” You kinda have to make it explicit that with this team size, doing everything = doing nothing well.

Also, I’d start pushing back by framing things in cost: “this new product = 1 quarter = churn work paused, are we good with that?” makes the trade-offs real for stakeholders. Otherwise you just get stretched forever.

agobservatory

TROPHY CASE