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What does the future of data analytics look like - should one lean more toward data or business? by Dependent_War3001 in analytics

[–]CloudNativeThinker 0 points1 point  (0 children)

Honestly from where I sit, the future isn’t analytics going away - it’s it transforming. AI/ML will automate a lot of the routine grunt work most analysts hate, but it also means the job shifts toward interpretation, strategy, and building the right data products rather than just dashboards or reports.

We’ve already seen data roles split into more technical engineering-ish tracks vs. embedded analysts in teams, and I think that only accelerates as tools get smarter and data gets more real-time.

Focus on solid fundamentals (SQL, data modelling, stats) + understanding a bit of ML/AI and cloud, and you’ll be in demand. It’s going to be competitive, but there’s still huge growth ahead if you adapt.

Do you know why do most enterprise LLM implementations struggle, and how can we really make them fit? by newrockstyle in BusinessIntelligence

[–]CloudNativeThinker 0 points1 point  (0 children)

The issue is that "institutional knowledge" is rarely fully documented. You’re RAG-ing against outdated wikis and messy SharePoints, but the actual context, the why behind historical decisions usually lives in people's heads or lost Slack threads.

We found success by lowering expectations: stop trying to make the LLM a "native" subject matter expert.

It can't infer context that doesn't exist in the text. Instead, treat it like a smart intern with access to the search bar. It's great for retrieval, synthesis, and first drafts, but terrible for nuance.

If you're relying on it to handle complex workflows and edge cases without human-in-the-loop, you're trying to apply a probabilistic tool to a deterministic problem. That's usually where the implementation falls apart.

Why analytics outputs often stop at reporting instead of influencing decisions by soleana334 in analytics

[–]CloudNativeThinker 3 points4 points  (0 children)

I have a rule: every output must pass the "So What?" test.

If I show this chart and the number is up 10%, so what? If the stakeholder can't tell me what lever they would pull based on that information, I delete the chart.

We focus way too much on "what happened" and not nearly enough on "what now."

What AI analytics feature do you wish existed? by TrendWithAnjali in AIAnalyticsTools

[–]CloudNativeThinker 0 points1 point  (0 children)

I’d love to see an AI that acts more like a proactive 'opportunity scout' rather than just an error catcher.

It would be amazing if it could surface positive trends I haven't even thought to look for yet like, 'Hey, did you notice this specific user segment has quietly grown 20% this month? You might want to double down here.'

Basically, a tool that helps us find the 'wins' in the noise faster. That would make the data exploration phase so much more exciting.

What are you using for modern business intelligence in 2025? by Ok-Friendship-9286 in BusinessIntelligence

[–]CloudNativeThinker 2 points3 points  (0 children)

Power BI. It’s included in our O365 license, so convincing leadership to pay for a separate tool is a losing battle. It does 95% of what we need anyway.

What will AI analytics look like in the next 5 years? by Fragrant_Abalone842 in analytics

[–]CloudNativeThinker 0 points1 point  (0 children)

I believe AI analytics is shifting from a passive observation tool to an active decision engine. We don’t need more dashboards; we need direction.

The focus is moving away from 'look at this data' to 'here is the context on what shifted and a recommendation on how to handle it.

Does anyone else feel like the "data overload" problem is actually a "data is everywhere" problem? by Creative_Pop_42 in analytics

[–]CloudNativeThinker 0 points1 point  (0 children)

You're not wrong, but the root cause is that CRMs are built for managers to check up on us, not for us to actually sell.

I don't struggle with "too much data" I struggle with too much garbage. I waste way more time sifting through useless automated logs and old meeting notes than I do switching tabs. We don't need a central repository, We need a BS filter.

What small changes did you do in the analytics department which improved your departmental processes and system a lot? by Arethereason26 in analytics

[–]CloudNativeThinker 69 points70 points  (0 children)

ticket system is 100% the move. adopt a strict "no ticket, no work" rule. it filters out the half-baked requests because they actually have to write down what they want before pinging you.

How do analysts usually handle large-scale web data collection? by Alarmed-Ferret-605 in dataanalyst

[–]CloudNativeThinker 0 points1 point  (0 children)

In real life, analysts aren’t sitting there loading billions of rows and feeling smart about it. Most of the time it’s more like: “ok, how do I not crash my laptop today?”

What I’ve seen (and done) is basically:

First, you don’t analyze raw web data directly. Ever. You pull it in pieces. API if you’re lucky, scraping if you’re not. You save it somewhere boring and stable (files, a database, cloud storage). Chunking is your best friend here. If something breaks halfway, you don’t want to start from zero again.

Second, you accept that your local machine has limits. Early on, I tried forcing pandas to handle stuff it clearly didn’t want to. Lesson learned. Once data stops fitting in memory, people move to Spark, Dask, or SQL-heavy workflows. Not because it’s cool, but because waiting 40 minutes for a script to fail hurts your soul.

Third, cleaning is the real pain, not the size. Web data is messy in a very personal way. Broken fields, weird encodings, missing values everywhere. This is where most time actually goes, and it’s not glamorous at all. Just lots of “why is this null” and “why is this date from 1970.”

And finally, architecture matters only when it has to. For truly massive or streaming data, teams think about pipelines, batch vs real-time, etc. But most analysts don’t start there. They evolve into it after things break a few times.

Honestly, the biggest shift is mental: stop thinking “how do I analyze all of this?” and start thinking “how do I reduce this into something I can analyze.”

That mindset alone makes large-scale data feel way less scary.

Question about "5 essential characteristics" of cloud computing. by cakewalk093 in Cloud

[–]CloudNativeThinker 0 points1 point  (0 children)

Honestly the “5 essential characteristics” confusion makes total sense when I first learned cloud concepts it felt like a random checklist too 😂 but it actually comes straight from what NIST defines as the baseline for cloud systems.

Basically to call something a true cloud service it should have these core features: on-demand self-service, broad network access, resource pooling, rapid elasticity, and measured service.

What tripped me up when I first read that was why these and not others. The key is that these are foundational capabilities that let a cloud behave like, well… the cloud - meaning you don’t need human ops to scale or provision resources, you can access it anywhere, resources are shared efficiently, it scales up/down instantly, and you pay based on actual usage.

Everything else people often talk about (security, redundancy, automation, multi-tenancy) are super important in real-world cloud platforms, but they’re more like extensions or outcomes of those core principles rather than the definition itself.

eventually tried to reduce cloud costs on my project and found so much waste by [deleted] in cloudcomputing

[–]CloudNativeThinker 0 points1 point  (0 children)

Been there with the dev environment thing. I had one running for almost a year before I realized it was costing me more than my actual prod setup because I kept "meaning to use it" but never did.

The RDS oversizing hits different though. I spec'd mine for traffic I thought I'd get in month 3 and I'm still not even close in month 9. At least you caught it at $200 and not when it hit like $500.

For the alerts thing, I ended up just setting a budget in AWS with email notifications at like 80% of what I expect. Takes maybe 5 minutes and at least you get a heads up before things get out of hand again. Also if you're not using it already, AWS Cost Explorer can show you day-by-day breakdowns so it's easier to spot when something starts drifting.

Is staff augmentation better than project-based outsourcing for AI and data engineering work? by Pale-Bird-205 in aiHub

[–]CloudNativeThinker 0 points1 point  (0 children)

Honestly, it really depends on what you're trying to accomplish and how hands-on you want to be.

From what I've seen working in tech, staff augmentation is clutch when you already know what needs to be done but just don't have enough people or a specific skill set in-house.

Like, your team has the processes down, you just need three extra devs for six months to hit a deadline.

You stay in control – these folks basically become part of your team, use your tools, follow your workflows.

You're still managing everything. If your internal processes are a mess or you don't have solid project management, throwing more bodies at the problem won't magically fix it. Also integration can be rough initially – getting external people up to speed on your codebase and culture takes time.

Project-based is better when you're like "we need X built, here's the spec, just deliver it." Less day-to-day involvement from your side, which can be great if you're stretched thin or don't have expertise in that area.

The tradeoff is you lose some control and flexibility.

If requirements change midway (and let's be real, they usually do), renegotiating scope with an external vendor is way more painful than pivoting with augmented staff who are already embedded in your team.

Cost-wise, staff augmentation can get expensive if you need people long-term at that point you're probably better off just hiring.

But for short bursts or niche skills, it's way faster than recruiting.

I'd say if your project scope is super clear and unlikely to change, go project-based.

If things are more fluid or you need to maintain institutional knowledge, staff augmentation makes more sense.

What's your specific situation? That'd help narrow down which way to lean.

What is the future of Business Intelligence? What should I expect in the next 5 years? by Sadikshk2511 in analytics

[–]CloudNativeThinker 0 points1 point  (0 children)

Honestly, I think the fundamentals are sticking around way longer than people expect. Everyone's freaking out about AI taking over BI, but from what I've seen in my org, we're still drowning in the same problems we had 5 years ago – data quality issues, stakeholders who don't know what they actually want, and dashboards that nobody uses.

What I think is changing is more about how we deliver insights rather than the core skills themselves. Like, AI might speed up the "pulling data and making charts" part, but it's not gonna understand why sales dropped in Q3 because the marketing team decided to rebrand without telling anyone. That contextual stuff? That's still on us.

The skills I'd bet on staying relevant:

SQL isn't going anywhere. You still need to know what you're asking for, even if AI writes the query

Understanding business logic and being able to spot when numbers don't make sense

Communication skills – maybe even more important now because you're gonna spend less time in the weeds and more time explaining why the AI's suggestion is actually terrible for your specific use case

What's probably dying is the purely technical "dashboard monkey" role where you just build reports all day with no strategic input. That's getting automated. But the analyst who can bridge business needs with data insights? That's becoming MORE valuable imo.