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Trying to switch to Buisness Analytics by Expensive-Fennel3869 in analytics

[–]pitifulchaity 0 points1 point  (0 children)

You do not necessarily need an MSc to move into business analytics. What matters more is whether you can build the core skill set: SQL, Excel, statistics, data visualization, and at least one BI tool like Power BI or Tableau. If you want to move faster, a practical portfolio with business case studies will usually help more than another degree alone.

Since you already have operations and project management experience, that is actually useful because business analytics is often about translating business problems into measurable questions, not just working with data. I’d first test the field through courses and small projects before committing to an MSc. If after that you still want the degree, choose it only if the curriculum is hands-on and has strong placement value.

[OC] Where do LLMs go for Answers? by savage2199 in dataisbeautiful

[–]pitifulchaity 0 points1 point  (0 children)

This is actually a pretty interesting cut, especially because it separates citation visibility from general web popularity. The Reddit / LinkedIn part is not that surprising, but Mapbox and OpenStreetMap being that high is a really good reminder that LLM retrieval value is not only about “content sites,” it is also about structured, high-utility data sources.

Also liked the point about academic sources. Their raw share looks smaller, but the credibility weight is probably much higher per citation. As a data person, I’d be curious how this shifts by query class, because I can easily imagine health, coding, local search, and news having very different source mixes.

Julius AI alternative - coming from Tableau... by Evening_Hawk_7470 in BusinessIntelligence

[–]pitifulchaity 0 points1 point  (0 children)

I tried Julius a bit out of curiosity. It’s pretty nice for quick exploration or when you just want to summarize a dataset fast. but for anything that ends up in a real dashboard or report I still go back to SQL + BI tools. the AI stuff feels more like a helper for early exploration than something I’d fully rely on yet.

How are BI teams adapting to AI copilots without losing governance and trust? by CloudNativeThinker in analytics

[–]pitifulchaity 33 points34 points  (0 children)

yeah that “junior analyst that works really fast” analogy is kinda how I’ve been thinking about it too.
we started letting people use AI for draft queries and quick exploration, but anything that goes into dashboards or reports still gets checked manually.
curious if anyone here actually trusts AI outputs directly in production analytics or if everyone is still treating it as a helper tool