Picture this. The dashboards are finally live. There is an internal launch on Wednesday. The data team is on the call, the project sponsor is beaming, someone posts in Slack with the link. Someone else replies with a thumbs up. The data team takes the rest of the afternoon off because honestly, three months is a long time to be staring at DAX formulas.

Three months later, in a meeting that has nothing to do with reporting, someone says, "Do we still have that dashboard?"

Nobody can find the link.

If you are reading this and wincing, you are not alone. I have watched this exact film play out dozens of times. Different companies, different industries, different dashboards. Same ending.

The dashboard graveyard

The dashboards get built by people who love data, for people who do not love data. That is the first problem, and it is the one nobody wants to say out loud at the kick-off meeting.

Data teams build for themselves first. They optimise for what is technically interesting. Lots of measures, clever DAX, drill-throughs that branch into more drill-throughs. The kind of thing that makes another data person nod approvingly. The kind of thing that makes the ops manager close the tab.

The end users wanted a Tuesday morning answer to a Tuesday morning question. What they got was an analytics environment.

Fourteen tabs and one question

I sat in a training session once where the analyst was proudly walking the team through fourteen tabs of drill-down capability. Filters within filters. Cross-references. A custom visual someone had downloaded from the AppSource gallery that made everyone go "ooh."

At the end, the team lead raised her hand and asked, "Can it just tell me which jobs are late?"

Fourteen tabs. One question. The answer was buried somewhere in tab seven if you knew which filters to apply.

Nobody asked the team what questions they actually needed answered. They asked what data was available. Those are very different conversations.

If the people who will use the dashboard have not been part of the design, you have built something for an audience of one. Yourself.

A walkthrough is not training

Here is the bit that really gets me. The "training" for the new dashboard is a thirty-minute screen share at 4pm on a Friday. The data team walks through the report. Everyone nods. A few people ask polite questions. Someone says "this is great." The recording gets dropped into a SharePoint folder called "Training Resources" that no one will ever open.

By Monday, nobody remembers a thing.

This is not training. This is a demo. Training is what happens when people actually try to use the thing, hit a problem, and have someone walk them through it. Not once. Several times. Over weeks, not minutes.

I know this because I have been the one cleaning up afterwards. Six months on, someone asks me to come in and do a "refresher," and I open the dashboard and realise most of the team has gone back to exporting the data to Excel and doing it the old way. The dashboard still exists. It just is not used.

What if the people who built it also taught it

This is where Data Loop does things differently, and I am going to say it plainly because it matters. When we build a Power BI solution, we also train the team that will use it. Same people. Same context. The person who taught your team how the data model works on Tuesday is the same person who built the data model on Monday.

That sounds obvious. It is shockingly rare.

The other thing we do is follow up. Not just a one-and-done session. Coming back a few weeks later and saying, "How is it going? What are you struggling with? Let us look at what you have actually been doing and tweak the report to match." Because the way people actually use a dashboard is never quite the way the design doc predicted.

It is not glamorous work. It is just the part that makes everything else worth doing.

If your dashboards are gathering dust

If you have dashboards quietly sitting in a folder, getting opened by two people every quarter, I would genuinely love to hear about it. Not to sell you something. Just to figure out what went wrong and whether it is fixable. Half the time it is. The other half, we work out together what to do instead.

Either way, you do not have to keep paying for reports nobody reads.