See webinar recording of this presentation at https://resource.alibabacloud.com/webinar/detail.htm?webinarId=31 Need a better way of looking at your business data? Learn how to take your raw data and achieve powerful data visualization in just minutes. Join this webinar to get up and running with Alibaba Cloud QuickBI and gain an intuitive understanding of several commonly used data visualization charts and their application to business. No programming experience required. From this webinar, you will gain an informed understanding of: 1. The importance and why we need to visualize data 2. How to utilize QuickBI to achieve data visualization in just minutes 3. How to upload data and generate different charts through a live demonstration of QuickBI.

1. Create Powerful Reports
Using Data Visualization
With Quick BI
Alibaba Cloud University Special
Certification Course – Clouder
Presenter: Steve Xue

2. What You Will Learn
1
Gain an understanding of the characteristics
and applicable scenarios of visual charts
2
Choose a visual chart for a given scenario
3
Use Quick BI to design reports and dashboards
4 Independently process common chart
requirements
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3. Introduction to Graphical Reports
Introduction to Quick BI
Resolve and Prepare Common Charts
Task: Build a Business Report Portal
Content
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3
4

4. Introduction to Graphical Reports
Introduction to Quick BI
Resolve and Prepare Common Charts
Task: Build a Business Report Portal
Content
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2
3
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5. Reports
Report:
Displays common formats of reports including digital tables and charts. Reports are a fundamental
applications of Business Intelligence (BI).
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6. Data Visualization
Visualize complex data in an interactive manner, and intuitively expresses its contained
information and patterns.
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7. GUI Report
A report type most frequently used for data visualization. Graphical reports use charts to more
intuitively display data and convert key information in data to information that is quickly available
and easy to understand.
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Weight Height

8. Common Charts Use Cases
Display Data
Visualized charts can display known data
or data analysis results graphically to
clearly and effectively communicate
information
Data Analysis
Chart-based “secondary analysis” for
in-depth data mining. Further analyze
data insightfully by dissecting the
characteristics shown in charts.
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9. Steps to Designing Charts
Clarify the problem
What problem is this chart
supposed to solve?
What information is supposed to
be delivered to viewers?
Basic frame
Design a preliminary
frame, or an overview, for
the entire chart
Finalize the chart type
Choose a proper type based on
the content and information to
be displayed
2
Highlight key information
Design the chart to call attention
to key information
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1
Determine metrics
Convert the final
information that is
needed to specific
metrics
3
Design
charts
4
5

10. What Makes a Good Chart?
01
04 03
Intuitive, easily
understood, and
matches needs
Good visual effects
02
Key information
is properly
displayed
Suitable display
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11. Introduction to Graphical Reports
Introduction to Quick BI
Resolve and Prepare Common Charts
Task: Build a Business Report Portal
Content
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2
3
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12. Quick BI Introduction
Quick BI supports real-time online analysis of massive data, drag/drop
operations, and diverse visualization effects, helping you easily analyze and
deeply understand business data.
It also boosts data-driven operations and helps complete the “last mile” of big data
applications. Using Quick BI, everyone can be a data analyst!
Quick BI
- Supports many data
source types
- Supports many
visualization components
Flexible Fast
- Real-time analysis of
massive data
- Provides smart acceleration
with a single click
Secure
- Flexible report
integration plans
- Granular security and
permission control
Economic
- Low threshold, easy to get
started, saves time
- Cloud computing ensures
low cost
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13. Quick BI Product Architecture
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14. Quick BI's Role in Big Data Planning
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15. Get Started With Quick BI in 5 Steps
Import data Table analysis Design charts
1 2 3 4
 Create/import
data source
 Upload local
data
 Edit dataset
Publish
applications
5
 Use a verified real-
name account
 Log on to the
console
 Sign up for Quick BI
 Set dimensions
and metrics
 Analyze multi-
dimensional data
 Design table
reports
 Design graphs
and tables
 Analyze report
data
 Create data portal
 Publish data report
 Data distribution
mechanism
 Data security control
Sign up for
Quick BI
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16. Step 1: Sign up for Quick BI
1 2 3 4
 Create/import
data source
 Upload local
data
 Edit dataset
5
 Use a verified real-
name account
 Log on to the
console
 Sign up for Quick BI
 Set dimensions
and metrics
 Analyze multi-
dimensional data
 Design table
reports
 Design graphs
and tables
 Analyze report
data
 Create data portal
 Publish data report
 Data distribution
mechanism
 Data security control
Import data Table analysis Design charts Publish
applications
Sign up for
Quick BI
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17. Step 2: Sign up for Quick BI
1 2 3 4
 Create/import
data source
 Upload local
data
 Edit dataset
5
 Use a verified real-
name account
 Log on to the
console
 Sign up for Quick BI
 Set dimensions
and metrics
 Analyze multi-
dimensional data
 Design table
reports
 Design graphs
and tables
 Analyze report
data
 Create data portal
 Publish data report
 Data distribution
mechanism
Import data Table analysis Design charts
Publish
applications
Sign up for
Quick BI
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18. Step 3: Table Analysis
1 2 3 4
 Create/import
data source
 Upload local
data
 Edit dataset
5
 Use a verified real-
name account
 Log on to the
console
 Sign up for Quick BI
 Set dimensions
and metrics
 Analyze multi-
dimensional data
 Design table
reports
 Design graphs
and tables
 Analyze report
data
 Create data portal
 Publish data report
 Data distribution
mechanism
 Data security control
Import data Table analysis Design charts
Publish
applications
Sign up for
Quick BI
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19. Step 4: Design Charts
1 2 3 4
 Create/import
data source
 Upload local
data
 Edit dataset
5
 Use a verified real-
name account
 Log on to the
console
 Sign up for Quick BI
 Set dimensions
and metrics
 Analyze multi-
dimensional data
 Design table
reports
 Design graphs
and tables
 Analyze report
data
 Create data portal
 Publish data report
 Data distribution
mechanism
 Data security control
Import data Table analysis Publish
applications
Sign up for
Quick BI
Design charts
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20. Step 5: Publish Applications
1 2 3 4
 Create/import
data source
 Upload local
data
 Edit dataset
5
 Use a verified real-
name account
 Log on to the
console
 Sign up for Quick BI
 Set dimensions
and metrics
 Analyze multi-
dimensional data
 Design table
reports
 Design graphs
and tables
 Analyze report
data
 Create data portal
 Publish data report
 Data distribution
mechanism
 Data security control
Import data Table analysis Design charts
Publish
applications
Sign up for
Quick BI
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21. Introduction to Graphical Reports
Introduction to Quick BI
Resolve and Prepare Common Charts
Task: Build a Business Report Portal
Content
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2
3
4

22. Common Chart Types
Line chart Column chart Pie chart Scatter plot
Card
Radar chart
Funnel chart Tornado chart Tree chart Conversion chartTree map
Dashboard Geo chart Polar chart Word cloud
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23. Column Chart
Column chart:
Uses the lengths of
rectangles to show values.
A number of vertical
columns with different
heights are used to show
data distribution.
Scenarios:
Bar graphs are suitable for displaying two-dimension datasets, with one axis
representing the classified dimension for comparison, and the other
representing values of the dimension, such as (month, sales amount of
goods). Bar graphs can also be used to compare comparable metrics of the
same dimension, such as (month, apple output, peach output)
Advantages:
 Simple, intuitive, easy to show value differences by column lengths
 Easy to compare differences between groups of data
Disadvantage:
- Inefficient at displaying big datasets
Similar charts:
Bar chart, histogram, stacked chart, percentage stacked chart, double Y-axis
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24. Column Chart Examples
Column chart Grouped column chart Stacked chart
Percentage stacked chart Double Y-axis column chart Bar chart
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25. Line Chart
Line chart (or broken
line graph): Formed by
marking values as points,
and links these points
with straight lines in a
certain sequence.
Scenarios:
Used to reflect changes of data on an ordinal dependent variable. In a
more general sense, they reflect the trend of things changing along with
ordinal categories. Line charts can clearly reflect the trend, speed, and
pattern of data increasing/decreasing, peak values of data, and so on
Advantages:
 Accurately displays the changing trend along a dimension
 Can compare the trend of multiple groups of data on the same
dimension
 Can display big datasets
Disadvantages:
- Cannot display large number of broken lines in each chart
Similar charts:
Stacked chart, curve graph, double Y-axis broken line graph, area graph
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26. Line Chart Examples
Stacked chartDouble Y-axis chart Area graph
Multi-metric broken line chartCurve graphLine chart
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27. Pie Chart
Pie chart:
Uses a pie graph to
display the magnitude
and ratio of each item in
a data series.
Scenarios:
Suitable for processing two-dimensional data, one dimension being a
category field, and the other being a continuous data field. Users can choose
pie charts if they care more about percentages
Advantage:
 Simple, intuitive, clearly shows the percentages of components
Disadvantages:
- Inefficient at displaying big datasets
- Negative values not allowed to serve as its data items
- Difficult to visually differentiate close percentages
Similar charts:
Donut chart, 3D pie chart
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28. Pie Chart Examples
Pie chart Donut chart 3D pie chart
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29. Scatter Plot
Scatter plot:
Displays data as points
to show the relationship
between variables or the
extent to which they are
mutually affected. The
positions of the points
are decided by the
values of the variables.
Scenarios :
Used to display the relationship between values in several data series.
Similar to X-Y axes charts, scatter plots can be used to judge whether two
variables are in some way correlated, or spot how data is distributed and
aggregated
Advantages:
 Shows how data is distributed and aggregated
 Suitable for displaying big data sets
Disadvantages:
- Scatter plots look messy. Also, they essentially only show correlation,
distribution, and aggregation. Other information is not displayed well
Similar charts:
Bubble chart
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30. Scatter Plot
Distribution Aggregation
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31. Radar Chart
Radar chart
(or spider chart):
Maps the data values of
multiple dimensions to
the coordinate axes
starting from the center
of the same circle and
ending at the
circumferential edge,
and connects the points
of identical groups with
lines.
Scenarios:
Suitable for multi-dimensional data sets
Advantages:
 Suitable for displaying multiple key characteristics of a data set
 Suitable for displaying how multiple key characteristics of a data set
are compared with standard values
 Suitable for comparing values of multiple data entries in multiple
dimensions
Disadvantages:
- The number of dimensions is limited, usually four to eight
- Inefficient at comparing large numbers of data entries
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32. Radar Chart Example
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33. Funnel Chart
Funnel Chart:
Formed by multiple
trapezoids stacked from
top to bottom. The items
from top to bottom follow
a logical sequence. The
area of a trapezoid
represents the difference
between the business
volume of a link and that
of the previous link.
Scenarios:
Used in one-way analysis of single-path businesses featuring standardized
procedures, long cycles, and multiple links. Funnel charts can intuitively
spot links by comparing the business data of each link, thereby facilitating
further decision-making
Note:
- A funnel chart always starts with a 100% value and ends with a smaller
percentage
- N links exist between the beginning and the end, each represented by a
trapezoid
- The traffic of all links in a funnel chart must use the same metrics
Similar charts:
Pyramid chart, symmetric funnel chart (tornado chart), comparison funnel
chart
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34. Funnel Chart Examples
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35. Tornado Chart
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36. Tree Chart
Tree chart:
Uses a tree-like shape to
display the
organizational
relationship between
hierarchical data, and
organizes objects using
a parent-child hierarchy.
This is an example of
enumeration.
Applicable scenarios:
Suitable for analysis related to organizational structures or data that follows a
clear hierarchical structure
Advantages:
 Intuitively displays hierarchical relationships
 Displays relationships between the metrics of each hierarchy, and
supports simple operations such as roll-up and drill-down
Disadvantages:
- Inefficient at handling large numbers of data hierarchies
- Inefficient at handling large numbers of members at each hierarchy
- Cannot display the percentage of each part
Similar charts:
Tree map
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37. Tree Chart Example
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38. Tree Map
Tree map:
Uses rectangles to
represent the nodes of
hierarchical structures,
and expresses the
parent-child hierarchy
with the mutual nesting
of rectangles.
Scenarios:
Suitable for displaying data with hierarchical relationships, and can
intuitively compare data of the same level
Advantages:
 More compact, so more information can be displayed
 Can display the weights of members
Disadvantages:
- Not as intuitive and specific as tree charts
- Displaying a category with a very low percentage can be challenging
Similar charts:
Tree chart, mosaic chart, heatmap
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39. Tree Map Example
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40. Conversion Chart
Conversion chart:
The conversion rate of a
page is calculated using
the number of page
views (PVs) and unique
visitors (UVs). This
allows users to evaluate
the overall operation
effectiveness of a site
and the final volume of a
particular product
Scenarios :
Suitable for analysis related to e-commerce and marketing, such as
analyzing a shopping website for the best-selling items and the times
with the most visits
Advantages:
 Particularly suitable for analyzing and displaying the
operational data of website traffic
 Intuitively displays results, including how the metrics of each
dimension change
 Supports querying the status of processes by node
Disadvantages:
- Narrow application because it only displays the process data of
three dimensions
- Strict metric requirements for displaying
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41. Conversion Chart Example
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42. Card
Card
displays texts, numbers,
and symbols in a way
that is easily
understood. Comprised
of kanban tags and
kanban metrics. Tags
are decided by
dimensions, and metrics
by data measures.
Scenarios:
Suitable for displaying one or several measures in the same
dimension, especially when some metrics must be precisely read
Advantages:
 Displays detailed data and gives users precise information
 Simple and intuitive; highlights key numbers; easy to show key
information to users
Disadvantages:
- Only displays one dimension
- Inefficient at displaying large numbers of metrics
- Only a digital panel without advantages of charts
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43. Card Example
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44. Dashboard
A dashboard
is like a clock or a dial
scale, and has scales
and pointers. Scales
represent metrics;
pointers represent
dimensions; pointer
angles represent values;
pointers point to current
values.
Scenarios:
Used to manage statements or reports, and can intuitively display the
progress or status of a metric
Advantages:
 Displays professional data by a common scale, intuitive and easy to
understand
 Skeuomorphism display is more user-friendly
Disadvantages:
- Suitable for a limited number of scenarios that require displaying progress
or percentages
- Can handle one dimension and display limited information only, inefficient
at processing too many metrics
Similar charts:
Stacked chart
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45. Dashboard Example
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46. Geo Chart
Geo chart:
Data distribution across
different geographic
locations is mapped to
the map in the form of
color or bubble.
Scenarios:
Suitable for displaying datasets that contain information of geographic
locations, and typically displays summarized continuous values by region
Advantages:
 Intuitively displays geographic distribution of data in conjunction with
maps
 Easy to show the magnitude of measures through light/dark colors and
bubble size
Disadvantages:
- Geographic information is required; only displays summarized data; bubbles
can easily overlap
- Displays less accurate values; difficult to differentiate close bubble sizes and
colors
- No correlation between the size of a geographic location and the measure
can cause misreading
Similar charts:
Geo bubble chart, color geo chart, point plotting geo chart
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47. Geo Chart Examples
Color geo chartGeo bubble chart
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48. Polar Chart
A polar chart:
Comprises multiple
sectors. Data dimension
determines the tag of
each sector; data
measure determines the
length of each sector. All
sectors have the same
angle. Different radii are
used to indicate
differences.
Scenarios:
Suitable for comparing enumerated data. Examples include displaying data
variation within a period of time or the comparison between items
Advantages:
 Better visual effect than other charts in some scenarios
 Displays more data than other charts in the same canvas
Disadvantages:
- Inefficient at handling datasets of a low number of classifications
- Inefficient at handling datasets of too small metric values
Similar charts:
Pie chart, donut chart, column chart, and rose diagram
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49. Polar Chart Example
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50. Word Cloud
Word cloud:
Visual display of text
data, a cloud-like color
graph used to display
massive text data.
Important words are
displayed with different
fonts, sizes, or colors.
Scenarios:
Suitable for describing key words (tags) or visualized free-form texts on a
web page; can compare the importance of words. Essentially a scatter plot,
it is the result of drafting words of specific forms at corresponding
coordinate points
Advantages:
 Quickly perceives most highlighted words, or differentiates words of
different weights
 Displays massive texts
Disadvantages:
- Inefficient at displaying datasets of too low amounts of data
- Inefficient at displaying data of low differentiation, or without important
keywords
Similar charts: Scatter plot, column chart
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51. Word Cloud Example
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52. Chart Types and Scenarios
Comparison
Compares
differences
between values
Column
chart
Radar Funnel
Polar
coordi-
nates
Tornado
Funnel
Word
cloud
Percentage
Percentage of a
partto a total
Pie
chart
Funnel
Dashb-
oard
Tree
map
Correlation
Displays
correlations
between values
Scatter
plot
Tree
map
Card Tree
chart
Conver-
sion
chart
Trend
How values
change along
dimensions
Line
chart
Column
chart
Geographic
map
Mapping of values
and geographic
information
Bubble
geo
chart
Color
geo
chart
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53. Introduction to Graphical Reports
Introduction to Quick BI
Resolve and Prepare Common Charts
Task: Build a Business Report Portal
Content
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2
3
4

54. Task Description
ABC is a sales company, and its website allows consumers to place and pay for
orders for their products. Its operating department has accumulated some static
offline data, and wants to create a business report portal to support acquiring and
sharing the data inside the enterprise.
Sign up
for Quick
BI
Step 1
Upload
data
Step 2
Design
reports
Step 3
Create
portal
Step 4
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55. Q&A

56. 56 / 54