Why should we even bother visualizing data?

Sure, it’s tempting to just run a model and call it a day. But before we dive headfirst into hyperparameter drama, let’s remember:

A picture is worth a thousand rows of Excel.

Here’s why visualizing data isn’t just “nice to have” - it’s mission critical:

1. Humans are (mostly) visual creatures
   Your brain processes visuals 60,000x faster than text. That’s why charts hit harder than tables - our eyes see patterns, outliers, trends, and weird stuff almost instantly.
   Hence, visualization isn’t just the language of analysts while communicating with business stakeholders but also how they themselves get insights in the first place.

2. Good Decisions = Good Data + Good Understanding
   No matter how fancy your model is, it’s of no use if no one can understand what it’s saying or worse, they misunderstand it.
   Decision-makers don’t want your process. They want your point.
   Data visualization bridges that gap - turning “we observed a statistically significant drop in engagement” into “no one likes the new update - let’s fix it before they uninstall”. No jargon. Clear & concise.

3. Choosing the Right Visual: Less Art, More Alignment
   Once you've got a story to tell, the next question is - How do you show it so people get it instantly? That’s where visual alignment comes in. You don’t need the fanciest chart in the room. You need the chart that is right for the job.
   And yes, there are a hundred tools - Excel, Seaborn, Matplotlib, Tableau, etc. - but the technique matters way more than the tech.

4 Umbrella Principles of Effective Data Visualization

1. Know the Purpose
   Before you drag that first axis or pick a color palette, ask yourself - why is this chart here?
   Every visual / table / graph should not be there just to flex your tool skills; it should have a purpose statement - “I created this chart to help the audience see that ...”. Clear & Specific. Not “because it looked cool in Tableau”.

2. Ensure Integrity (a non-negotiable entity)
   A good chart tells the truth. A great chart makes sure it can't be misread.
   How you show the data is as important as its accuracy. Even a tiny visual distortion can completely misrepresent the story - and tank your credibility.
   For example, using a bar chart where the y-axis doesn’t start at zero? Congrats, you just made a small difference look like a giant one.

3. Maximize Data Ink, Minimize Non-Data Ink
   Anything that doesn’t directly support your data’s message = non-data ink. And we don’t have time for it.
   Gridlines you can’t read? Kill ‘em.
   Rainbow bars that mean nothing? Retire them.
   3D pie chart with 8 slices and shadows? Go sit in the corner.

4. Show your data & annotate it
   A chart is great. A chart with context is 10x better.
   Add labels, notes, markers - they guide the viewer’s attention.
   But be strategic: If your graph is dense with data points, don’t annotate everything like a 5th grade school diagram. Pick a few critical points and explain just those.

From Thought to Plot: Executing Your Information Display in 3 Steps🛠️

Okay, so you’ve got your clean data, a sense of purpose, and a deep respect for the truth (RIP 3D pie charts🪦). Now comes the execution phase - where you turn insight into impact. Here’s a 3-step system to make sure your visuals land.

1. Define the Message
   You can’t design an effective visual without a message. Even if it's subtle. Your visual must make this message obvious at a glance - not buried in labels or lost in color chaos.
   Ask yourself - What exactly am I trying to say? What do I want my audience to take away instantly?
   Maybe it’s - “Only 2% of users binge-watch long-format anime” or “Sales spiked the week we featured that show on the homepage.”

2. Choose the Form
   Not everything needs to be a graph. Sometimes plain text, a simple table, or even a well-drawn diagram does the job better.
   Don't reach for the bar chart just because it’s your default. Choose the form that makes your message land the fastest.

   | CHOOSE A TABLE WHEN | CHOOSE A CHART WHEN | | --- | --- | | You need to display the full dataset — numbers, labels, everything. | You’re comparing a slice of information. | | You want to highlight one specific item in the context of the bigger picture. | You want to show cause vs. effect (y-axis vs. x-axis). | | You’re presenting a wide range of values that don't scale well graphically. | You’re showing trends over time (line charts, anyone?). | | You need to show the math - like how values were calculated or derived. | You want to display patterns or distributions (bell curves, clusters, etc.). |

   | If your message is about... | Use this chart type | | --- | --- | | Components of one item | Pie Chart (⚠️Use only when slices are few and one clearly stands out -otherwise, choose a bar chart as it’s hard to compare similar-sized slices) | | Components of multiple items | 100% Stacked Column / Bar Chart | | Item comparison | Bar Chart | | Change over time | Line Chart / Column Chart | | Frequency / Distribution | Histogram | | Correlation / Relationship | Scatter Plot / Paired Bar | | Outliers and spread across categories | Boxplot |

3. Create the Design
   This is where your inner minimalist takes the wheel. Use layout, color, annotations, and emphasis with intention, not decoration.
   A good design -

   • Guides the viewer’s eye

   • Reduces friction in understanding

   • Highlights what matters most

   • Makes your chart look effortless

First, here’s what NOT TO DO:

• No 3D effects – They make everything harder to read and distort values.

• No chunky borders – Avoid contrasting borders around bars, pies, or text boxes. They shout when your data should speak.

• No legends (if possible) – Use direct labels on bars, lines, and slices instead.
  Less eye-hopping = faster understanding.

Now, here’s what TO DO to design clean and readable graphs:

• Use annotations smartly to highlight key data changes or to focus on specific data points. They help direct focus exactly where you want it.

• Design the graph to support your message. Consider using talking head here, too.
  If you’re saying “this value is dropping,” make sure the visual screams it too.

• Minimize grid lines – Or drop them altogether. Less visual noise = better comprehension.

• Keep everything thin – Lines, axes, arrows, even bars. Thinner = cleaner.

• Data points? Show just enough – You don’t need to mark every value. Just enough to suggest a trend, not distract from it.

• Use minimal tick marks – Usually, just the min and max on your Y-axis do the job. Add more only if it adds clarity.

• Label axes and values – Tell your reader what they’re looking at. But don’t go overboard. If labeling every bar clutters the view, label the important ones and skip the rest.

Visualization on Dashboards

Let’s talk about dashboards - not the kind in your car (though, same vibe), but the ones you build to keep businesses running.

So... What Is a Dashboard?

A visual display of the most important information needed to achieve one or more objectives that has been consolidated on a single screen so it can be monitored and understood at a glance.
No multiple pages. No click throughs. No scroll bars.

Does It Have to Be Interactive?

Not necessarily.
In fact, a great dashboard should be instantly understandable without clicking anything. The interaction should be optional - not a scavenger hunt.
That said, it’s good to include additional links for more exploration. But the main screen? It should be so clear it could hang on a wall and still make sense.

What Should Go on a Dashboard?

Only what’s important.
So ask:

• What is the decision this person needs to make?

• What data do they need at a glance to make that decision?

• What details can be kept elsewhere (like a link, second screen, or deep-dive dashboard)?

Basic Dashboard Design Principles

1. Zoom out before you zoom in.
   Start with the big picture: KPIs, status indicators, totals. Then lead the viewer to details if needed.

2. Use color with intention.
   Usually, Red = alert. Green = good. Yellow = warning.
   But remember that accessibility isn’t a bonus - it’s a baseline. So use color-blind-friendly palettes & don’t rely on color alone to show meaning - combine it with labels, icons, or textures.

3. Cut the clutter.
   If it’s not answering a core question, it doesn’t belong here. Add extra info via buttons or links or providing pathways to deeper dashboards or reports instead of overcrowding the main stage.

4. Highlight key movements or exceptions.

While dashboards usually focus on descriptive analytics (What happened?), they can also include:

• Diagnostic info (Why did it happen?)

• Predictive metrics (What might happen next?)

• Prescriptive nudges (What should we do?)

Data visualization isn’t the cherry on top - it’s the plate the whole dessert is served on. So be bold with your message, kind with your colors, and ruthless with clutter.