5 Things I Wish I Knew Before Building My First Production Dashboard

There’s a massive gap between building a dashboard for a course project and deploying one that executives will actually rely on for decisions. I learned this the hard way.

My first production dashboard? I thought I nailed it. Clean visuals, comprehensive metrics, slick interactions. I was proud of it.

Then I watched someone try to use it in a meeting. They couldn’t find the metric they needed, got confused by the filters, and gave up after 30 seconds. Ouch.

Here are the lessons I learned—often painfully—that transformed how I approach dashboard design.

1. Nobody Wants All The Data (They Just Think They Do)

What happened: A stakeholder told me they needed “everything” about sales performance. So I gave them everything—20+ visuals, every possible breakdown, filters for every dimension.

They hated it.

The lesson: People don’t actually want all the data. They want answers to specific questions. Your job is to figure out what those questions are, even if the stakeholder can’t articulate them.

What I do now:

Start every dashboard project with: “What decision are you trying to make?”
Build a simple prototype with 3-5 key metrics
Add complexity only when users ask for it (they rarely do)

Pro tip: If you can’t explain what a visual is for in one sentence, it probably shouldn’t be there.

2. Your Fancy DAX Won’t Save Bad Data

What happened: I spent hours writing complex DAX calculations for a revenue dashboard, only to discover the source data had duplicate entries and inconsistent date formats. My beautiful calculations were giving wrong answers.

The lesson: Garbage in, garbage out. Always. No amount of clever analytics can fix bad underlying data.

What I do now:

Spend 50% of my time on data quality before I even open Power BI
Build validation checks into my data model
Create a data quality dashboard before the actual dashboard
Document data issues and limitations clearly

Pro tip: Add a “Last Refreshed” timestamp and data quality indicators to every dashboard. If numbers are wrong, users need to know why.

3. The Best Filter Strategy is No Filters

What happened: I built a dashboard with 8 different slicers. Users could filter by date, region, product category, sales rep, customer type… you name it. I thought I was giving them flexibility.

Instead, they got analysis paralysis. Which filters should they use? In what order? What’s the default view supposed to show?

The lesson: Every filter you add multiplies the cognitive load. Most users won’t experiment—they’ll use the default view and nothing else.

What I do now:

Start with zero filters and add them only when absolutely necessary
Use smart defaults that show the most relevant view (usually “last 30 days” or “current month”)
If you need many filters, use drill-through pages instead of cluttering one view
Consider: can this be solved with multiple simple dashboards instead of one complex one?

Pro tip: Watch someone use your dashboard. If they ask “what should I filter by?”, you have too many options.

4. Stakeholders Care About “Why,” Not Just “What”

What happened: I showed a dashboard that clearly indicated sales dropped 15% in Q3. The first question? “Why?”

I had no answer. The dashboard showed what happened, not why.

The lesson: Descriptive analytics (what happened) only gets you halfway. Decision-makers need diagnostic analytics (why it happened) to take action.

What I do now:

Include comparison views (vs. last year, vs. target, vs. forecast)
Add drill-through pages that let users investigate anomalies
Use annotations to highlight known factors (“Product X discontinued”)
Build in segmentation analysis to identify patterns

Pro tip: The best dashboards answer the first three follow-up questions before they’re asked.

5. Performance Matters More Than You Think

What happened: My dashboard looked amazing… but took 45 seconds to load. People stopped using it.

The lesson: A slow dashboard is a useless dashboard. If it takes more than 5 seconds to load, users will lose patience.

What I do now:

Optimize my data model ruthlessly (remove unused columns, use proper data types)
Aggregate data at the source when possible
Use incremental refresh for large datasets
Test performance with real data volumes, not sample data
Monitor query performance and optimize slow-running DAX

Pro tip: Use Performance Analyzer in Power BI to identify bottlenecks. Usually it’s a few specific visuals or calculations causing the problem.

Bonus Lesson: Version Control and Documentation Are Not Optional

This one almost got me fired (okay, not really, but it was bad).

I made updates to a production dashboard and accidentally broke a critical metric calculation. I couldn’t remember what the old formula was. Panic ensued.

Now I:

Keep a changelog in every dashboard (text box on a hidden admin page)
Document calculation logic in measure descriptions
Take screenshots before major changes
Use Power BI’s deployment pipelines for dev/test/prod separation

What I Wish I’d Known

If I could go back and tell my past self one thing, it would be this:

Your job isn’t to build dashboards. Your job is to enable better decisions.

That means:

Understanding the business context, not just the data
Designing for users, not for your own portfolio
Focusing on insights that drive action, not just pretty visualizations
Making things simple, even when the data is complex

The technical skills—SQL, DAX, Power BI features—are important. But they’re just tools. The real skill is understanding what problem you’re solving and building the simplest solution that works.

Your Turn

What lessons did you learn the hard way? What would you tell someone building their first production dashboard?

Drop your thoughts in the comments—I’d love to hear what I’m still getting wrong!

Want to see these principles in action? Check out my portfolio projects where I apply these lessons to real-world data analysis challenges.