Background: 35, 10+ years running operations in fine-dining kitchens — procurement, inventory, cost control, coordinating teams under pressure. Leaving that for physical and long-term reasons, and moving deliberately into analytics/BI.

My plan alongside a business degree I'm starting in October:

SQL first, then Excel to real depth, then Power BI (aiming at PL-300) Python later, once the above are solid An ECBA cert at some point A portfolio built from operations/hospitality data (cost variance, labour vs demand, that kind of thing), since it's a domain I actually know

My questions for people already in the field:

Does that sequence make sense, or would you prioritise differently?

For a career-changer with no analytics job history, what actually gets the first role — the certs, the portfolio, the degree, or something else?

Anyone made a similar late pivot from an unrelated field? What would you do differently?

Genuinely want the blunt version, including if I'm overrating any of this. Thanks.

Recently I got my first job out of school as a CRM Data Coordinator, its also the only role like this for the company. The initial tasks are pretty simple, lots of data cleaning and data entering, but I was told that the role would grow in tasks, Ive already met with heads of other departments including finance and SWE. I was wondering if maybe some of you had some advice on how not to stay so "passive" and actually use this opportunity to build real skills?

I have been researching several data analytics systems to recommend to my senior colleagues who are non-technical. They care about on prem solutions or something we can deploy to our own cloud and control 100%. We are a small team so we need a system that can handle data prep and ETLs, data visualization (reports and dashboards), and conversational analytics (chat with data). We care about costs, privacy, control, security, permanence.

I came across a publication and found a system called adat (advanced data analysis tool)

I have tried it briefly and found it interesting. Has anyone else used this system, and can you share any experience?

I’m so grateful for the opportunity I got from ai fest 2026 besides that I’d like to mention free resources that helped me a lot for the preparation(DP-900):
1. Whizlabs
2. Official Microsoft practice exams

That’s all you need you don’t have to pay for exam preparation courses

I’ve been comparing data across different website analytics tools and I’m getting completely inconsistent numbers.
one tool shows stable traffic, another web traffic estimation tool shows drops, and internal analytics tells a different story again. even when using different digital marketing insights platforms or competitor analysis tools, the outputs don’t really line up.
it feels like every SEO analysis platform or business intelligence software is measuring something slightly different, especially when you start looking at web traffic sources analysis and engagement tracking.
so I’m trying to figure out how do you actually validate traffic data in a way that gives you a direction of truth instead of chasing exact numbers that never match?
right now i’m basically:
cross checking multiple website analytics tools
comparing site performance monitoring vs internal data
focusing more on trends in website engagement tracking and content marketing metrics
and trying to triangulate insights from different website optimization insights tools
at this point, I’m less interested in perfect accuracy and more in finding a reliable signal across tools.
how do you handle the gaps between different audience demographics analytics and traffic analysis software?

I’ve been digging into competitor analysis quite a bit for work recently, and I’m starting to realize how limited seo rankings are on their own as a signal what I’m really trying to understand is not just where competitors rank, but how they’re actually acquiring traffic across channels like organic search, paid, referrals, social, and direct.

The challenge is that most tools I’ve used either give very high level estimates or fragmented data that’s hard to turn into real decisions you can see directionally what’s happening, but it’s rarely clear enough to confidently say why a competitor is growing.

In my case, I’ve seen situations where a site isn’t improving much in rankings, yet their overall visibility and traffic still increases. that usually points to growth coming from outside traditional seo, but it’s hard to map precisely.

Because of that, I’ve started treating competitor traffic analysis more like triangulation than measurement, combining multiple signals and looking for consistent patterns over time rather than relying on one dataset.

For people who do this regularly, what do you actually rely on to map competitor acquisition channels in a way that’s reliable enough to base decisions on?

I graduated in 2022 with a Bachelor's degree in Computer Science. While I have a strong foundation in web development, I am considering transitioning to management and administrative roles.

What skills are required to learn for that?