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Data Visualization Fundamentals

Nguyên tắc thiết kế visualization hiệu quả - Chart selection, Color theory, và Storytelling với data

Data Visualization Fundamentals

Trước khi học Tableau hay Power BI, bạn cần nắm vững nguyên tắc visualization. Một chart đẹp nhưng sai chart type có thể mislead hoàn toàn.

🎯 Mục tiêu

  • Hiểu tại sao visualization quan trọng
  • Nắm nguyên tắc chọn chart phù hợp
  • Áp dụng color theory hiệu quả
  • Storytelling với data

1. Tại Sao Visualization Quan Trọng?

1.1. Con số vs Hình ảnh

Text
1Raw data:
2Q1: 150, Q2: 180, Q3: 210, Q4: 195
3
4Observation từ số: "Q3 cao nhất"
5
6Observation từ chart:
7- Q3 cao nhất
8- Uptrend Q1→Q3
9- Q4 giảm nhẹ (potential concern)
10- Growth rate giảm dần

1.2. Anscombe's Quartet

4 datasets có cùng thống kê (mean, variance, correlation) nhưng hoàn toàn khác khi visualize.

Text
1Dataset 1: Linear relationship
2Dataset 2: Quadratic relationship
3Dataset 3: Perfect line + 1 outlier
4Dataset 4: Vertical line + 1 outlier
5
6Statistics: Identical
7Visualization: Completely different stories!

Lesson: Luôn visualize data, không chỉ dựa vào statistics.

1.3. Cognitive Load

Text
1Table với 1000 rows
2→ Brain: "Overload, cannot process"
3
4Chart summary
5→ Brain: "Got it in 3 seconds"

Visualization giúp:

  • ✅ Pattern recognition nhanh
  • ✅ Outlier detection dễ dàng
  • ✅ Comparison trực quan
  • ✅ Trend identification rõ ràng

2. Chọn Chart Phù Hợp

2.1. Decision Framework

Text
1Bạn muốn show gì?
2
3 ├── Comparison → Bar/Column Chart
4
5 ├── Trend over time → Line Chart
6
7 ├── Part-to-whole → Pie/Donut (≤5 parts)
8
9 ├── Distribution → Histogram/Box Plot
10
11 ├── Relationship → Scatter Plot
12
13 └── Composition → Stacked Bar/Area

2.2. Chart Selection Guide

PurposeBest ChartsAvoid
Compare categoriesBar, ColumnPie (nhiều categories)
Show trendLine, AreaPie, Bar
Part of wholePie (≤5), Treemap, StackedLine
DistributionHistogram, Box, ViolinBar
CorrelationScatter, BubbleLine, Bar
GeographicMap, ChoroplethBar

2.3. Common Mistakes

❌ Mistake 1: Pie chart cho nhiều categories

Text
1Bad: Pie với 15 slices → Không đọc được
2Good: Bar chart horizontal → So sánh dễ

❌ Mistake 2: 3D charts

Text
1Bad: 3D Pie → Distort proportions
2Good: 2D variants → Accurate reading

❌ Mistake 3: Dual-axis abuse

Text
1Bad: 2 Y-axes với scales khác nhau → Misleading
2Good: Separate charts hoặc normalize data

❌ Mistake 4: Truncated Y-axis

Text
1Bad: Y-axis bắt đầu từ 95 → Small change trông lớn
2Good: Y-axis từ 0 hoặc clearly labeled

3. Color Theory for Data

3.1. Color Purposes

PurposeColor TypeExample
CategoricalDistinct colorsProducts: Blue, Red, Green
SequentialLight to darkRevenue: Light blue → Dark blue
DivergingTwo-directionalProfit/Loss: Red ← Gray → Green
HighlightAccent colorOne bar highlighted in orange

3.2. Color Best Practices

DO:

Text
1✅ Limit to 5-7 colors max
2✅ Use colorblind-friendly palettes
3✅ Consistent colors across dashboard
4✅ Gray for context, color for focus

DON'T:

Text
1❌ Rainbow colors (hard to distinguish)
2❌ Red = Good, Green = Bad (reverse psychology)
3❌ Same color, different meanings
4❌ Bright colors for background

3.3. Accessible Color Palettes

Text
1Colorblind-safe palette:
2#1f77b4 (Blue)
3#ff7f0e (Orange)
4#2ca02c (Green)
5#d62728 (Red)
6#9467bd (Purple)
7#8c564b (Brown)
8
9Sequential (single hue):
10#deebf7 → #9ecae1 → #3182bd → #08519c
11
12Diverging:
13#d7191c → #fdae61 → #ffffbf → #a6d96a → #1a9641
14(Red) (Yellow) (Green)

3.4. Testing Accessibility

Tools để check:

  • Coblis Color Blindness Simulator
  • Viz Palette (Tableau)
  • Color Oracle (desktop app)

4. Design Principles

4.1. Data-Ink Ratio

Concept: Maximize data, minimize non-data ink.

Text
1Bad (low data-ink ratio):
2┌──────────────────────────────────────┐
3│ ████ Heavy gridlines │
4│ ████ 3D effects │
5│ ████ Decorative elements │
6│ ████ Redundant labels │
7└──────────────────────────────────────┘
8
9Good (high data-ink ratio):
10┌──────────────────────────────────────┐
11│ │
12│ ▬▬▬ Clean, minimal design │
13│ ▬▬▬▬▬ Focus on data │
14│ ▬▬▬▬▬▬▬ │
15└──────────────────────────────────────┘

4.2. Gestalt Principles

Proximity: Nhóm related items gần nhau

Text
1[Chart A] [Chart B] ← Related metrics
2
3[Chart C] ← Different category

Similarity: Same color/shape = Same category

Text
1● Sales Q1 ● Sales Q2 ● Sales Q3 ← Blue dots
2■ Costs Q1 ■ Costs Q2 ■ Costs Q3 ← Red squares

Enclosure: Border groups related info

Text
1┌─────────────────┐
2│ Revenue Section │
3│ [Chart] [KPI] │
4└─────────────────┘

4.3. Visual Hierarchy

Text
1Most Important
2
3████████████ Large, bold, top position
4
5████████ Medium, prominent
6
7████ Smaller, supporting
8
9Least Important

5. Data Storytelling

5.1. Story Structure

Text
11. SETUP (Context)
2 "Q3 revenue was $2.5M..."
3
42. CONFLICT (Problem/Opportunity)
5 "...but growth slowed to 5% vs 15% Q2"
6
73. RESOLUTION (Insight/Action)
8 "Analysis shows: Marketing spend dropped 20%
9 Recommendation: Increase budget by $50K"

5.2. Annotation Techniques

Text
1Chart với annotations:
2
3 Revenue Trend
4
5150 ┤ ★ Campaign launched
6 │ ╱
7100 ┤ ←─ Seasonal dip
8 │ ╱
9 50 ┤──────╱
10 └────────────────────────
11 J F M A M J J A

Annotation types:

  • Callouts: Highlight specific points
  • Reference lines: Targets, averages
  • Trend lines: Show direction
  • Notes: Context, caveats

5.3. Dashboard Flow

Text
1┌─────────────────────────────────────────────────────┐
2│ TITLE │
3│ Key insight in subtitle │
4├────────────────┬───────────────┬───────────────────┤
5│ │ │ │
6│ KPI 1 │ KPI 2 │ KPI 3 │ ← At-a-glance
7│ $2.5M ▲12% │ 150K ▼3% │ 95% ▬ │
8│ │ │ │
9├────────────────┴───────────────┴───────────────────┤
10│ │
11│ MAIN VISUALIZATION │ ← Core story
12│ │
13│ │
14├────────────────────────┬────────────────────────────┤
15│ │ │
16│ Supporting Chart 1 │ Supporting Chart 2 │ ← Details
17│ │ │
18└────────────────────────┴────────────────────────────┘
19│ Filters: Date | Region | Product │ ← Interactivity
20└─────────────────────────────────────────────────────┘

6. Pre-attentive Attributes

Attributes Brain Processes Instantly

AttributeBest For
PositionComparing values
LengthQuantities
Color hueCategories
Color intensityMagnitude
SizeRelative amounts
OrientationDirection

Using Pre-attentive Cues

Text
1Make important data POP:
2
3Before: ■ ■ ■ ■ ■ ■ ■ ■ (all same)
4
5After: ■ ■ ■ ■ █ ■ ■ ■ (target highlighted)
6
7 "This one!"

7. Common Visualization Mistakes

7.1. The Hall of Shame

1. Misleading Y-axis

Text
1Bad: Good:
2 100│ ▲ 100│
3 98│ ╱ 50│ slight
4 96│___╱ 0│___▬▬▬▬ increase
5 Looks like 50% Actually 4%
6 increase

2. Cherry-picked timeframe

Text
1Show only good months
2→ "Sales are up!"
3
4Show full year
5→ "Actually, we're down 10% YoY"

3. Wrong chart type

Text
1Pie chart for:
2- More than 5 categories
3- Values that don't sum to 100%
4- Showing trends

4. Overcrowded design

Text
110 charts on 1 screen
2→ Information overload
3→ No clear message

7.2. How to Avoid

Ask: "What's the ONE thing I want viewers to understand?" ✅ Simplify: Remove anything that doesn't support the message ✅ Test: Show to someone unfamiliar, ask what they see ✅ Iterate: First draft is rarely final


8. Hands-on Exercise

Exercise: Critique This Dashboard

Text
1┌────────────────────────────────────────┐
2│ 3D Pie Chart with 12 slices │
3│ Rainbow colors │
4│ Legend far from chart │
5│ No title │
6│ Y-axis starts at 1000 │
7│ Dual axis with different scales │
8└────────────────────────────────────────┘
9
10List 6 problems and suggest fixes.

Exercise: Choose the Right Chart

For each scenario, select best chart type:

  1. "Compare revenue across 10 regions"
  2. "Show how market share changed over 5 years"
  3. "Display correlation between ad spend and sales"
  4. "Break down expenses by category"
  5. "Show distribution of customer ages"

📝 Quiz

  1. Khi nào KHÔNG nên dùng Pie chart?

    • Show part-to-whole
    • So sánh hơn 5 categories
    • Có 3 segments
    • Show percentages
  2. Data-ink ratio cao nghĩa là?

    • Dùng nhiều colors
    • Chart phức tạp hơn
    • Tối đa data, tối thiểu decorations
    • Chart 3D
  3. Sequential color palette dùng khi?

    • Categories khác nhau
    • Values từ thấp đến cao
    • Positive vs Negative
    • Random selection
  4. Pre-attentive attribute nào brain xử lý nhanh nhất?

    • Text labels
    • Footnotes
    • Color và Position
    • 3D effects

🎯 Key Takeaways

  1. Chart selection matters - Wrong chart = Wrong message
  2. Less is more - High data-ink ratio
  3. Color with purpose - Categorical, sequential, diverging
  4. Tell a story - Setup, conflict, resolution
  5. Design for humans - Use pre-attentive attributes

🚀 Bài tiếp theo

Tableau Fundamentals - Bắt đầu với Tableau Desktop và tạo visualization đầu tiên!