- Quick skeleton
- Who I am and how I test tools
- What “best” means to me
- My hands-on takes: Tableau, Power BI, Looker Studio, Grafana, Flourish, Plotly/Dash, D3.js, Apache Superset, plus quick hits (Excel, Sheets, Qlik)
- Who should use what
- Final pick by use case
A quick hello from me
I’m Kayla. I make charts for a living. I also make them for fun. I’ve used these tools on real jobs, with real teams, and real deadlines. Late nights. Cold coffee. Big files. You know how it goes.
I won’t sell you fluff. I’ll tell you how each tool felt in my hands, what broke, and what worked.
If you’re hunting for a broader analytics stack—not just charts—I also keep deep-dives on the best business intelligence tools I actually use and the web-scraping tools that have really worked for me that feed those charts. If you’d like an independent, high-level comparison before diving into my hands-on notes, Bernard Marr has a clear breakdown of the seven leading options here.
What “best” means here
- It must help me tell a clear story.
- It must handle the size of data I have.
- It must let my team view it without pain.
- It must not eat a week to fix one small change.
I also care about cost, speed, and sharing. A pretty chart that no one sees is still a fail.
Tableau: The one I grab when the story is hard
I used Tableau to build a donation dashboard for a food bank. We needed to see gifts by zip code, by time, and by campaign. Drag, drop, map, done. It felt smooth. The “Show Me” feature gave me a head start, and I loved how fast I could test ideas.
What I liked:
- Mapping is easy. I made a zip map in minutes.
- Cross filters feel natural. Click a bar; the rest follows.
- Story points helped me guide the board through the tale.
What bugged me:
- It’s pricey for a small shop. That hurt.
- The desktop app felt heavy on my older laptop.
- LOD calcs are strong, but they took me time to learn.
A tiny win: I used Tableau Public to share a city bike trip story one summer. It got me quick feedback from folks who ride. That was sweet.
Best for: analysts, nonprofits with data depth, teams with budget, and folks who need rich maps.
Power BI: The workhorse that surprised me
I thought I’d hate it. I was wrong. Kind of. My team used Power BI to track monthly sales and churn at a SaaS. Power Query cleaned messy CSVs. DAX gave us rolling 90-day numbers. The model view kept joins sane.
What I liked:
- It connects to almost anything.
- Power Query felt like magic for cleanup.
- Sharing in our Microsoft setup was smooth.
What bugged me:
- DAX can twist your brain at first.
- Sharing outside the org needs extra licenses.
- Large models can get slow if you push it.
A new thing: Copilot hints started to help me build measures faster. Not perfect. But nice when I’m tired.
Best for: teams on Microsoft, finance dashboards, exec scorecards, and mixed data sources.
Looker Studio: The quick share king for marketing
I used it for weekly ad reports with GA4 and BigQuery. We made a clean view of spend, clicks, and ROAS. The share link went to the team chat. No fuss.
What I liked:
- It’s free for public stuff. That’s huge.
- Easy links. No installs. Just open and view.
- Good enough for most marketing needs.
What bugged me:
- Quotas can block data pulls. Then you wait.
- Some charts feel plain. Fine, but not wow.
- GA4 sampling made one report look off until I fixed it.
Best for: marketing, quick exec views, simple blends, and teams that live in Google land.
Grafana: When time series is the whole game
I used Grafana to watch server load and Postgres lag on a Sunday night release. It kept us calm. We set alerts with Prometheus, and my phone buzzed before users felt pain. Not cute, but very real.
What I liked:
- Time charts are crisp and fast.
- Alerts work. They saved us once at 2 a.m.
- Panels snap into a neat grid.
What bugged me:
- It’s not for storytelling to a wide audience.
- Design is plain. That’s fine for ops, less fine for execs.
- Setup needs help from your infra folks.
Best for: ops, data engineers, SREs, and any time-based metrics.
Flourish: Pretty, fast, and “good enough” for news-style work
I used Flourish to build a bar chart race for a local sports story. I also made a small election map. It felt like play. Pick a template, load data, tweak colors, embed.
What I liked:
- Templates look polished out of the box.
- The learning curve is tiny.
- Great for public-facing pieces.
What bugged me:
- Private projects need a paid plan.
- Custom logic is limited. When you hit the wall, you feel it.
- Large data can lag on some templates.
Best for: comms, newsrooms, social posts, and event recaps.
Plotly + Dash: When I need custom, but still want speed
I built a quality check app for a factory team. We used Dash to filter defects, show heat maps, and export PNGs. It ran on a small server. The engineers liked the hover info and the save button. I liked not having to build a front end from scratch.
What I liked:
- Charts look sharp in Python or R.
- Dash apps feel like real web tools.
- Interactions are smooth and clear.
What bugged me:
- Complex callbacks can get messy.
- Hosting and auth take time to set up.
- Big data can feel heavy without care.
Best for: data folks who code and need custom flows.
D3.js: Full control, full cost
I used D3 to build a chord chart for school transfer flows. I also made an animated line chart for a grant pitch. The control felt great. The time sink did not.
What I liked:
- You can make almost any chart you can dream up.
- Animations are yours to shape.
- Tons of examples to learn from.
What bugged me:
- The learning curve is steep. No way around it.
- One small change can break things.
- Not great for fast turn work.
Best for: custom interactive stories with a dev on hand.
Apache Superset: Open source BI that grew on me
We used Superset at a startup to avoid license fees. I set up SQL Lab, made slices, and built a KPI board for ops. It did the job. The team could explore without bothering me every hour.
What I liked:
- No per-seat cost. That helped a lot.
- SQL Lab is handy for quick checks.
- Dashboards are fine and shareable.
What bugged me:
- Setup and upgrades take care and time.
- Role and permissions need thought.
- Some visuals feel basic, but they work.
Best for: startups, data teams with an engineer, and cost-aware groups.
Quick hits I still use
- Excel and Google Sheets: Great for small data, quick charts, and one-off views. I use them for kickoff meetings, then move on.
- Qlik Sense: I used it in a warehouse to track pick rates. The green-white-gray filters made odd patterns pop fast. But the license and setup felt heavy for a small team.
So, which one should you pick?
- Need a rich story with maps and layered views? Tableau.
- Your org is on Microsoft and you want a hub for KPIs? Power BI.
- You need fast sharing for marketing and simple blends? Looker Studio.
- You watch systems and care about alerts? Grafana.
- You want pretty, public, and fast templates? Flourish.
- You code and need a custom app with real charts? Plotly + Dash.
- You need full control for a custom piece? D3.js.
- You want open source BI with real dashboards? Superset.
One more thing. If you’re not sure where to start, try this: mock up your idea in Sheets, get feedback, then move to Power BI or Tableau based on where your team lives. I do this all the time. It saves me from rework. I also keep a concise comparison checklist on ptools.org that helps me and my clients match needs to features at a glance. You can also check SelectHub’s regularly updated snapshot of popular visualization platforms here.
My honest wrap-up
No tool wins every race. I use three most: Power BI for business work, Tableau for data stories, and Looker Studio for quick shares.
I keep an always-updating version of this [best data visualization tools](https://www.ptools.org/the-best-data-visualization-tools-ive-actually-used-and-how-they-felt-in-real
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