r/Powerbihelp

we rolled it out across a large org, dozens of markets, tens of thousands of users, and the pitch was exactly what you'd expect — data for everyone, empower the business, reduce dependency on the data team, all of that

and for maybe the first six months it felt like it was working

then we started noticing shadow dashboards appearing everywhere, teams building their own logic, the same metric calculated five different ways by five different people who were all completely convinced they were right, and suddenly instead of one version of truth we were maintaining dozens of versions of chaos and nobody could agree on anything because everyone had their own numbers and their own definitions and their own reasons why their version was the correct one

the hardest part wasn't technical, we had governance, we had documentation, we had training, none of it really mattered because most people don't think in terms of data models or grain or definitions, they just want an answer fast and they'll build whatever gets them there

and once you give teams full control over their data you can't really take it back, everyone optimizes for their own use case and alignment becomes almost impossible, you've essentially traded one bottleneck for a hundred small fires

i'm not saying self-service is useless but i think we massively underestimate how much structure it actually needs to not fall apart, because without that you don't get data democratization, you just get distributed chaos with nicer dashboards

curious if anyone actually made this work at scale or if it always ends up like this

my director (in a very very big corpo) once rejected a dashboard because it wasn't "beautiful enough" = unprofessional. nobody knew what that meant. all team spent more time on colors & arrowws than on data analysis

had one of those moments this week

sales pulled a number for “active customers”
finance had a different one
and of course… the dashboard showed a third version

all coming from the same warehouse btw.

wrong data, you will think? nope!

turns out:

- sales defines “active” as anyone who purchased in the last 90 days

- finance uses 12 months

- product counts anyone who logged in

and this is the part that really gets me - we spend so much time fixing pipelines, optimizing queries, building cleaner dashboards

but almost no time aligning on what the numbers actually mean

feels like tools aren’t the bottleneck anymore, and with AI coming to data analysis this just will end up in huge fights for data

how others deal with this - do you formalize metric definitions somewhere?

when i started as an analyst everyone expected me to give insights and drive business decisions

sounds completely normal until you're actually sitting there

the data was messy, like genuinely messy, and there was a lot of it, so probably 90% of my time just went into cleaning and fixing things and trying to make them usable enough to look at, and then someone would call a meeting and ask "so what do the numbers tell us?" and i'd be staring at a dashboard i'd seen for the first time twenty minutes ago trying to say something that didn't sound completely stupid

the thing is i didn't understand what "good" even looked like for this business, i didn't know which decisions actually depended on these numbers or why anyone cared about these specific metrics, i was just a person who knew SQL sitting in a room with people who'd been working in this industry for fifteen years waiting for me to tell them something smart

it wasn't a skill issue, it was just an unrealistic expectation

what actually helped wasn't learning more tools, it was sitting in meetings and shutting up and listening to how people talked about the business, noticing which questions came up every single time, presenting badly and getting corrected, basically just being around the decisions long enough to start understanding why they mattered

but that takes time, more time than anyone tells you, because understanding a business is just genuinely harder than learning Power BI or SQL or anything else, and there's no course for it

did anyone actually feel ready when they started or was it always just figuring it out as you go

Has anyone had any trouble creating the ontology for a Power BI Semantic model? I've gone through all my tenant settings and enabled them for my BI admin group/user. But I'm still getting the error code: PowerBIFeatureDisabled. I have an F128 premium capacity. I'm pretty sure I've checked all the boxes but I still can't generate an ontology. Does anyone have any suggestions?