u/Brighter_rocks

Two PwC findings that belong in one sentence. Nobody puts them in one sentence. Watch me.

AI-exposed junior roles now require traditionally senior skills seven times more often. Seniorised entry-level postings: +35% since 2019. Meanwhile the tasks every current senior actually learned on - basic SQL, dumb reports, documentation, the same stakeholder request five Tuesdays running - are exactly what's going to AI first.

One sentence, as promised: come already having the skills that were supposed to be acquired here, we removed the part where you acquire them, welcome aboard.

Oh and the cherry. Indeed checked which industries offer employer training. Rarest in - drum roll, you already know - data analytics and software development. The industries deleting the learning tasks are the same ones not offering training to replace them. This is not a conspiracy. Nobody is steering. Somehow that's worse.

I call it the apprenticeship gap because it needed a name and "the thing where the ladder exists but the rungs don't" was taken. Title survives. Job survives. The part where a human becomes good at the job - that's the part evaporating.

If you broke in during the last two years: what did you actually learn on? Not rhetorical

Marketing keeps asking for "relatable content". Ok, here is the one.

Types of dashboards. You have all of them. Everyone does.

The Quick One. Started as "just revenue by region, super quick". Currently: 14 pages, three hidden pages, a bookmark navigation nobody ever found, and a page called "TEST do not delete" that two visuals in production secretly depend on

The Twin. Finance has one. Sales has one. Same metric, numbers off by 4%. Both certified. Everyone knows. It comes up at every QBR. Nothing has ever happened.

The One With The Filter. Works perfectly if you set seven slicers in the correct order. The order is documented in a Teams thread from 2023. The thread is wrong. The one person who knew the real order got promoted and now pretends not to remember.

Visibility™. Requested with the words "we need visibility into this". Visibility into what? For which decision? Great questions. Nobody asked them. Delivered by Tuesday.

The Meta. A dashboard about dashboard adoption. Its own adoption: low. It does not see the irony. It cannot see anything. It has four viewers.

The Beautiful One. Your masterpiece. Clean model, DAX you'd show your mother, a color palette you defended in a meeting. Four viewers, two are bots. The bots don't even like it.

The Survivor. Purpose unknown. Owner gone. Everyone's afraid to delete it. Once, someone brave did. That's how the company found out what it was for. It was for payroll.

Everything you memorized about an interface has a half-life. Ribbon moves, syntax updates, the vendor ships a redesign, a new tool wins the market and your muscle memory becomes trivia. Tool knowledge is a subscription you pay with study time.

Domain knowledge works the other way. Every year in an industry adds to it: how the money actually flows, why the numbers spike in week 3, which "impossible" values are data errors and which are that one client doing that one thing again, what operations can and can't actually change. It stacks. Nothing ships an update that invalidates it.

"Or similar" is doing a lot of work in that sentence.

When an employer lists BI tools in job posting as interchangeable, they're telling you - in writing - that the interface is not what the salary is for. The salary is for SQL, modeling, metric ownership, whatever the rest of the posting says. The tool is a delivery mechanism, like they don't care which brand of laptop you bring.

Which is uncomfortable if your entire professional identity is "Power BI person" or whatever BI person. Your subreddit karma, your certs, your muscle memory for the formatting pane - all real, all earned, all attached to one vendor's interface roadmap.

To be clear: deep Power BI knowledge still pays, in platform and enterprise roles it pays well. But those are specific roles, not the default analyst path. For the default path, "or similar" is the market saying the quiet part.

Quick self-audit: if your company switched to Looker tomorrow, what percentage of your value survives the migration? SQL survives. Modeling survives. Knowing why finance and sales disagree about revenue survives. The rest is interface.

I'd want my number above 70. Mine wasn't always.

There's a preview feature in Power BI called "Prep data for AI". AI data schemas, AI instructions, verified answers. Sounds boring. It is not boring.

Verified answers: you pick a visual, attach trigger phrases, and when a user asks something similar - Copilot returns your visual. Doesn't generate anything. Just retrieves the thing a human chose in advance. So the "AI answer" for your important questions is... not AI.

Approved for Copilot: a flag on a semantic model. Models without the flag get - I'm quoting the docs - "friction treatment" (!!) Microsoft built a feature that slows down their own AI when no human vouched for the model. Think about what that means for a second.

And my favorite sentence, straight from the docs: these features "can't ensure a specific output every time. AI behavior is nondeterministic." Same question, different answer. In your revenue reporting. They just say it.

I'm not dunking on Microsoft here. Opposite. This is the honest version of what everyone building AI on data finds out: plausible is not the same as acceptable. For numbers that matter you need a human-approved layer under the generation. We hit the same wall building Brighter, everybody hits it.

But someone has to build that layer. Choose trigger phrases. Pin the visuals. Describe the model so the schema makes sense. Decide what "approved" even means in your org. That someone is an analyst. Metric ownership, just with AI on the other end. Right now this is the most future-proof version of the job I can see.

All preview, details will change. The direction won't. Not Microsoft's. Not ours

A dashboard is not a deliverable. It's a subscription - refresh, maintenance, questions, "can we add one more page", eventual archaeology when it breaks and its author is gone. You're not building it. You're adopting it.

Most requests don't need one. The checklist I run before agreeing:

Does this question repeat, or does it just feel important today? One-off question, one-off analysis - answer it, close it, done.

Does anyone need to WATCH this number, or do they need to know when it crosses a line? That's an alert, not a report. Is there an owner who'll return to it monthly, or will it join the workspace graveyard by Q2?

Can the underlying process just be fixed instead of monitored forever? Monitoring a broken process is a weird form of acceptance.

The honest ratio I've seen: maybe a third of existing dashboards pass this checklist. The rest are one-off analyses that got promoted to permanent residency because building felt more productive than answering.

And here's the timely part: AI is collapsing the cost of building, which removes the last natural brake. When a dashboard cost two weeks, someone occasionally asked "is this worth two weeks?" At two hours, nobody asks anything. The graveyard is about to get very well-designed headstones.

Under the title Power BI Analyst there are two completely different professions, and nobody tells you to choose.

Career one: business and product analytics. SQL, metrics, experimentation, cohorts, stakeholder wrangling, domain knowledge. Power BI is your delivery van. Nice van. Still a van.

Career two: BI platform and engineering. Semantic models, Fabric, deployment pipelines, performance, security, governance, CI/CD. Power BI is the platform itself, and you're the one keeping it trustworthy at scale.

Both are real. Both pay. They grow in different directions, get promoted for different things, and interview completely differently.

The trap is the default path: learning a little of both. Some DAX patterns, some Fabric, a bit of experimentation theory, half of governance. Feels like breadth. Reads as junior in both interviews - the platform people see you don't know deployment, the product people see you've never owned a metric.

I'm not saying the split is absolute, seniors eventually reach across it. But you reach across FROM somewhere. Depth first, then breadth. The reverse doesn't work, it just feels like it's working.

So before the next course: which of the two are you actually building? If you can't answer, that's the thing to figure out this month. Not time intelligence.

The product post. I said it was coming.

Everything in this series comes down to one gap: chat AI answers your description of the problem. In Power BI the problem lives in the model - relationships, grain, filter direction, dependencies. If the tool can't see the model, it's guessing politely.

So Brighter opens the PBIX. You bring a real broken file, it looks at the actual model, and walks the diagnostic path with you.

The annoying design choice, on purpose: it doesn't just hand you the fix. It shows how it narrowed things down and asks you to verify. When it sees you tripping on the same thing over and over - filter context, grain, whatever - it gives you practice on that thing and remembers between cases.

Because honestly: a tool that just fixes your file makes you faster this week and more replaceable next year. The learning loop from junior work is dying. If AI killed it, AI tools should be rebuilding it. Not selling you the speed of losing it.

That's the bet. Ask me anything, including "why not just ChatGPT" - the previous post is the answer, but ask anyway.

Or just try Brighter

https://app.brighter.rocks/

DAX is the only popular language where the same code is correct in one file and wrong in another.

SQL query works or doesn't - the logic is in the query. Python function works or doesn't - the logic is in the function. A DAX measure has half its logic outside the text. Filter direction, relationship cardinality, grain of the fact table, what's on the visual. SUM(Sales[Amount]) is a different calculation on every page of your report. That's the whole design of the language.

We usually write long serious posts at Brighter. But... our marketing person said nobody shares long serious posts and asked for "something viral".

Fine.

Here it is: every popular belief about data analyst careers, ranked by how much damage it does.

"One more certificate and I'm safe." No. Certificates got you interviews in 2019. Now every posting says "or equivalent experience" and the screener skips straight to "tell me about a metric you owned". A cert without production experience is a bookmark. It marks where you stopped.

"AI is garbage, I can relax." Also no. Copilot writes DAX that compiles and answers the wrong question, agreed. But management already budgets for AI-assisted output. The expectations move faster than the tools. You're not competing with AI, you're competing with the manager's belief in AI.

"I need to learn Python. Or is it Fabric? Or dbt?" The tool roulette is a coping mechanism. Postings don't converge on a tool. They converge on: can you take a vague business question and turn it into a number people trust. The tool is whatever's lying around.

"Senior means knowing more DAX." Senior means being responsible for what the number means. Definition, exceptions, source of truth, what happens when finance disagrees with marketing about it. I know people who can write CALCULATE blindfolded and still wait for a ticket to tell them what to do.

"Good work speaks for itself." It speaks very quietly, in a locked room, to nobody. The postings say "influence", "partner", "proactively surface" - that's not HR poetry, that's the job now. The analyst who explains uncertainty out loud beats the one with the better model.

"I'll learn on the job like everyone before me did." This one hurts. The boring junior tasks everyone learned on - simple reports, small fixes, repetitive requests - are going to AI first. The ladder still exists. The bottom rungs don't.

What do you think? Is it viral enough? Add your ideas.

How did I learn this job? Dumb reports. Dumb requests. Basic DAX. Fixing small stupid errors at 6pm. The same stakeholder request for the fifth time.

I hated all of it.

But - that grind is where diagnostic instinct comes from. There is no other source.

Those exact tasks are going to AI first.

Meanwhile the postings I read monthly (see previous post) already want ownership from entry-level. Ambiguity handling, validation, recommendations. Senior expectations, junior title, junior pay. The bottom rungs of the ladder are being sawed off while the top ones move up.

Moreover, AI gives a junior fast answers, which feels like learning. It isn't. Microsoft's own research shows the more people trust AI output, the less they check it. So you get illusion of seniority - output looks strong, understanding doesn't grow, and the person can't explain their own solution or work outside a familiar pattern.

Entry-level work is disappearing faster than entry-level expectations. That sentence is the whole post.

Another CALCULATE course won't fix this. Topic courses assume you get reps at work afterward. The reps are gone. What's needed is what the job used to provide for free: messy problem, incomplete context, several possible causes, pick a path, defend it, get feedback on reasoning and not just the answer.

I don't have a neat ending. If you got in recently - I honestly don't know how you're supposed to build judgment now

Every week someone posts "learn X and you're safe". This is very relatable & very seductive - it takes away all anxiety, right?

We track US analyst postings monthly at Brighter (it feeds the product, long story) and the pattern is not one skill. It's three layers, and safety only exists where they overlap

Foundations. SQL, DAX context, modeling, grain, filter propagation. No, these are not useless now. Producing SQL is less distinctive - checking it is not. AI writes the query, you decide if the join duplicates rows, if the grain is right, if the calc matches what the business actually means. You cannot check work you couldn't do yourself. Sorry.

Judgment. Root cause, reconciliation, telling correlation from mechanism. Verifying the AI answer that looks right. Postings literally say it: "flag discrepancies", "validate accuracy". AI gives you a plausible solution. Judgment tells you if it solved the real problem or a nearby one.

Ownership. Metric definitions, source of truth, making stakeholders agree, tying numbers to money. There's a Mercer posting, $127-150k, where Power BI is listed as nice to have. Nice to have (!!) They pay for the thinking.

AI compresses layer one. Barely touches two and three. But two and three don't exist without one!

Every few months I test Copilot in Power BI hoping it got good. It didn't. It writes DAX that compiles and answers the wrong question. Fine. Good for me.

But was is not good for me: the job market is moving anyway.

I've been reading US analyst postings for the last couple months and the shift is obvious. The listings want validation, ownership, "partner with stakeholders", "proactively surface insights". Basic execution - the thing most of us got hired for originally - is being treated as automatable, even though the automation is still mediocre (!!)

So we're in this weird window where the tools don't work yet but management already expects AI-assisted output. The role is shifting from producing reports to owning what the reports say. Whether Copilot ever gets good is almost beside the point.

The dangerous mistake is looking at Copilot's garbage output and concluding you have five more years to chill. You don't. We dont.

My honest impression is: the market is flooded with people who learned tools, but there’s still a shortage of people who can operate independently

Every time I see this take, I want to grab the author by the shoulders and shake some sense into them.

A profession is more than the tool you open. It's the object of your work, the goal, the actual actions, the conditions you work under, the knowledge you need, the mistakes that trip people up. Ask which of those changes in 6 months.

For an analyst, the object of work was never DAX syntax. It's the gap between what a number says and what it actually means. The goal was never "write a query fast." It's giving someone a number they can trust enough to act on. The conditions are the same too - a deadline, undocumented logic somewhere upstream, three teams with three definitions of the same metric.

What gets faster is the tool. That's real. Writing SQL faster, drafting a query faster, none of that is nothing. But it's not the same claim as "the job is different now." Those are two different sentences and people keep swapping one for the other.

I'd like this specific kind of prediction to become embarrassing to make in public

15 years ago, my first job, and there was this fight that went on for months over sales numbers.

Finance counted by payment date. Sales counted by shipment date. Sounds like a couple days' difference, who cares. Except it's not a couple days when bonuses get calculated off those same numbers at quarter end.

Got to the point where in the same table, on the same slide, there were two columns both labeled "March sales" - almost a million apart. Nobody was lying. Nobody was cooking anything. One person counted the deal the moment the client signed, the other counted it when the money actually hit the account. Both numbers had been living side by side for months, until sales, finance and ops ended up in the same planning meeting and realized the totals didn't match. That was a "fun" one.

After that I started asking it first thing at every new job. Not "how do you calculate sales" - specifically: which date, returns included or not, with or without tax. Because if you don't ask upfront, eventually you're the one sitting there at midnight before a report is due, staring at Excel not matching the SAP export, absolutely certain your formula is right.

The formula was right. This is exactly the kind of mess we build training cases around at Brighter - not the definition, the moment you realize two 'correct' numbers were never the same number