that looked great on the surface but didn’t really anything?
After I first attempted to make sense of my dataset one Saturday afternoon, constructing a dashboard gave the look of the following reasonable step in my data science journey.
I’d binged enough YouTube tutorials to think I knew what a “good” one should appear like, probably something with a clean layout and perhaps just a few filters on the side.
With all that, I jumped right in.
I made a structure of how I wanted it to be and laid out components for my dashboard, but once I finally pieced all of it together, something felt off.
I stepped back to have a look at it, literally. I walked across the room and studied it from different angles. All of us do that, right?
After just a few long looks, I couldn’t explain what story the dashboard was actually telling.
And don’t get me improper, it was pretty decent for a primary attempt. But taking a look at it felt like watching a bunch of individuals all talk over one another.
I had squeezed in several chart types—bar charts next to pie charts next to line graphs—all fighting for attention on one screen. Each chart had something interesting to say, just not in a way that added as much as a transparent point.
Later that evening, my stomach sank as I sat there gazing my screen, the blue glow reflecting off my coffee mug. If my very own dashboard couldn’t connect with me, how could I expect it to attach with anyone else?
I began reading about why some dashboards fail to attach with people. I stumbled across a Harvard Business Review article that explained what number of dashboards fail to drive real decisions because most analysts focus an excessive amount of on looks relatively than clarity.
It mentioned something about “chart junk”, just decorative elements that don’t add meaning.
That hit home. Ouch.
Look, data storytelling isn’t nearly explaining insights. Slightly, it’s about helping people see what you saw in your evaluation and explaining it in a way that is sensible to them.
This text isn’t concerning the technical side of constructing dashboards; there are already countless tutorials that may teach you that.
Slightly, it’s concerning the parts we regularly overlook: how dashboards communicate meaning and intent. I’ll also share the mistakes and lessons that modified the best way I see data after constructing my first dashboard.
Why My Dashboard Looked Right but Felt Incorrect
It took me a little bit of humility to confess that the issue wasn’t the design.
It was me.
I used to be attempting to tell a story I hadn’t actually discovered yet.
I started to see that data really isn’t the story itself; as a substitute, it’s sort of just like the language we use to inform one. And like every language, meaning comes from how we elect to rearrange it.
That’s once I found out I needed to pause before constructing anything and ask myself just a few key questions first. I call them the three Ws:
- Why does this data matter?
- Who am I designing for?
- What query am I actually attempting to answer?
Those easy questions modified all the things. My dashboards stopped being just visuals and commenced feeling more like actual conversations.
It took me some time to understand the issue wasn’t the tool and even the dataset.
It was the best way I approached the story.
I had spent a lot time attempting to make the dashboard look right that I never stopped to ask what it was actually saying. It was like that moment in The Matrix when Neo finally sees the code. Once that thought crossed my mind, I knew I had to start out over.
(If you wish to dive deeper into dashboard design principles, this guide is solid.)
Constructing Again, but In a different way
After I got here back to the project, I made a decision to start out over. But this time, I didn’t rush to open my visualization tool just yet. Which felt weird, truthfully. My fingers were itching to click something.
I sat with the information for a bit, trying to grasp what it was really saying and the way I could guide that story toward interactive visuals.
Something about slowing down felt right. I began noticing stuff I didn’t see earlier, mainly small details that felt like they shouldn’t matter, but actually did.
As an alternative of trying to point out all the things, I made a decision to concentrate on one idea and construct around it. For instance, I had all these sales metrics sitting in front of me, but I picked one query that stood out:
That shifted all the things. Suddenly, my visuals weren’t fighting for attention. As an alternative, they were working together to inform the identical story.
As I went along, the less I added, the clearer all the things became. I removed just a few unnecessary charts and added transient notes to elucidate what certain numbers meant.
I added a straightforward annotation that said “Drop-off point” with an arrow pointing to where things began declining. No fancy design, just clarity. It wasn’t perfect, nevertheless it felt so much more intentional.
I spent three days constructing the primary version. The second? Six hours.
Six.
Not because I rushed, but because I finally knew what mattered.
After I shared it, people didn’t just nod politely. They leaned closer, asked thoughtful questions, and in addition tried to guess what is perhaps driving the trends. One person even pulled out their phone to take an image of it.
It felt different, in a superb way. Not gonna lie, I felt pretty proud.
Looking back, that moment modified how I approached projects afterward. I started to see dashboards less as something to display and more as a technique to translate what I used to be seeing, and help others understand it.
Sometimes I still catch myself wondering if I’m doing it right, but perhaps that’s the purpose. Possibly storytelling with data isn’t about getting it perfect.
Perhaps it’s about slowing down long enough to ask, what story am I actually attempting to tell here?
What I’d Tell My Past Self
If I could return to that first attempt, here’s what I’d tell myself:
Start with pen and paper before opening the tool. Sketch out the story first. What’s the start, middle, and end? You don’t need software for that.
Delete one chart for each two you add. If it doesn’t directly support your principal point, it’s only a distraction. Be ruthless with what you set in.
Read your dashboard out loud. In case you can’t explain it in a single breath, simplify. Your audience won’t have more patience than you do.
These easy rules have saved me countless hours and prevented me from creating more cluttered dashboards that look busy but say nothing.
I consider every dataset has a voice, nevertheless it takes patience to listen closely enough to listen to what it’s really saying. And trust me, when you do, all the things from the visuals to the insights starts to align with purpose.
Conclusion and Takeaways
After I first began, I desired to prove that I could construct something great. But by the tip? Seems the very best dashboards aren’t the flashiest ones. They’re those that make people pause and say, “Oh. I get it now.”
That project taught me something I didn’t expect: data storytelling is less concerning the data and more about empathy.
There was this satisfying click when all the things finally made sense—not only for me, but for everybody who checked out it. That feeling of connection, of being understood, made all of the rebuilding price it.
Now, every time I open a brand new dataset, I remind myself of that lesson: start slow, listen closely, and construct with intention. Sometimes I still mess it up. But at the least now I do know what I’m aiming for.
The goal isn’t to impress, it’s to attach.
