The document discusses the importance of storytelling with data, emphasizing how data visualization simplifies complex data into comprehensible information while linking it with narratives to convey deeper insights. It outlines key steps in effective data storytelling, including identifying the story, collecting relevant data, considering the audience, and choosing appropriate visualization types. Additionally, it provides guidelines on various chart types and their best practices for effective data presentation.

1. STORY TELLING WITH
DATA
Frehiwot Mulugeta
June 24, 2021

2. Outline
I. Why story telling with data matters?
II. What is DataVisualization?
III.Chart types & When to use
IV. Summery of charts by their purpose
Please Note: All data presented here are fake

3. I. Why story telling with data matters?
Data visualization is transforming complex data into information which is
easier to understand.
• Data visualizations are created to answer "what" questions, but they
don't explain the "why," or provide other contextual information to find
the reason.
• Data Story telling links the visualization with a narrative to answer the
“why” question, and conveys credible and compelling insights that is
actionable by the decision makers.
• Data Story telling connects the visualization and narration/insights to
the audience to make data-driven decision.
Ryan Fuller, general manager at Microsoft and former CEO
"The narrative is the key vehicle to convey insights, and the
visualizations are important proof points to back up the narrative."

4. Keys to story telling with data

5. Steps of story telling with data
1. Identify your story
• What question are you trying to answer
• Context/the current situation
2. Collect your data.
• What data is most important
• What am I trying to achieve with the data
3. Be aware of your audience
• Who are my audience
• What are the most important points you want to focus
• What level of data detail will they likely expect or appreciate?
4. Transform the data to data visualization
• How you want to present the data: charts, graphs; to answer the question
5. Generate your insights / Narration/
• Answer the “WHY” question of your visualization
• What is the one thing I want my audience to know or do with my data?
• Conclude with actions to taken

6. II. What is DataVisualization
➢ Data visualizations highlights patterns and trends in data and
gives the reader quick insights.
➢ Data visualizations are easier to understand than text-based
narrative or numbers.
• Data visualization is the presentation of data in a graphical
format.
• It is transforming complex data into meaningful information.
Why data visualization is important?

7. Principles of developing effective data visualization
Define your main
message
+20%
Determine the main message
you want to communicate
to your audience
I
II
III
IV
Identify the relationship
Identify the relationship in
your data that you want to
show – composition,
comparison, correlation
Select chart
& make it
Focus on the most important
variables of data, select the
chart, complete the analysis
and create the chart
Highlight insights
Emphasize major insights
through sorting values or
adding averages, trend lines,
growth rates, coloring, bolding, and
other effects

8. 1. Line chart
2. Bar chart
3. Stacked bar chart
4. 100% stacked bar chart
5. Pie chart
6. Areal chart
7. Combinations chart
8. Scatter chart
III. Chart types & when to use

9. 1. Line chart
▪Line charts are used to show time series
data/ trend over time.
▪A single variable time series data / that
changes at regular intervals.
▪Multiple-line chart is to compare and
contrast different data series.
▪ Multiple Lines should be labeled to help
quickly identify lines, should have different
colors
▪ Don’t plot more than 4 lines in one chart
▪ Highlight critical information by using a.
Color or context
When to use it?
Best practice
30
26
42
23
10
0 0 0
Sep-19 Oct-19 Nov-19 Dec-19 Jan-20 Feb-20 Mar-20 Apr-20
Trend of Typhus Cases

10. 2. Bar Chart
▪Used to display nominal variables like
favorite employment settings NGO,
Consulting, Government/
When to use it?
Best practice
▪ Sort usually largest to smallest so that users
can easily compare.
▪ when comparing different categories use
different colors
▪ Avoid grid lines used within the viz
2.1 Horizontal Bar Chart
• Used to show comparison among categories
• Do not use 3-D format to avoid unnecessary noise

11. When to use it?
▪Bar chart is used to shows change over
time, when you have small data set
▪Also to show comparison among ordinal
data / data that have sequential pattern/, age-
group, salary range,
Best practice
▪ Time series data is organized from left to
right, following the natural sequence of
reading.
▪ For columnar bar chart should start at zero
y-axis
▪ Avoid grid lines used within the viz
41%
74%
85%
0%
20%
40%
60%
80%
100%
Jan, 2019 Dec, 2019 Jun, 2020
Average Data Quality Score
+ 46%
2.1Vertical Bar Chart

12. 3.Stacked Bar Chart
▪ Stacked bar charts are used to compare
totals and be able to see their
breakdowns.
▪ Can be oriented either vertically or
horizontally as of the bar charts
▪ Each component can be identified by
different colors, patterns or shading.
▪ Sort the data with the total
▪ Vertical stack chart should start from
zero y-axis
When to use it?
Best practice

13. 4. 100% Stacked bar chart
▪100% Stacked Bar Chart ccompares the
contribution of each of individual items value
to a total across categories
▪Can be oriented either vertically or
horizontally
▪ Use colors to differentiate to clearly
differentiate separate categorical values.
▪ Use horizontal or diagonal labels, avoid
vertical labels
When to use it?
Best practice

14. 5. Pie & Doughnut chart
▪Pie chart shows the composition of
something.
▪Doughnut chart is a pie chart with a
hole inside used for the same purpose
▪ Make sure all data adds up to 100%.
▪ Label the chart clearly
▪ Don’t compare more than 6
components in one pie chart, Better
to plot the 5 important ones and
group the remaining into an “other"
category” Do not use multiple pie
chart for comparison.
When to use it?
Best practice
Covid-19 death disaggregation

15. 6. Areal Chart
▪Always label the axis and fields clearly.
▪ Avoid using dimensions with more
categories in an area chart as it can lead
to clutter.
▪The X-axis must start from zero
▪ The area chart is like the line chart, the
area below the plotted line is filled in
with color to indicate volume.
When to use it?
Best practice
COVID-19 Lab Test, Confirmed cases & recovered cases

16. 7. Heat Map
▪ A Heat Map in a visual representation of
numerical data in a tabular format, tables
where color intensity is used represent
relative values.
▪ Heatmap shows you a comparative view
of a dataset and gives insight which
dataset need more attention
When to use it?
Best practice
▪ color range high, average and low ( uses a
warm-to-cool color spectrum)
Sub city EFY 2012 EFY 2013
Addis Ketema 343,228 351,095
Akaki Kality 246,731 252,387
Arada 285,556 292,101
Bole 415,572 425,097
Lideta 271,399 277,620
Yeka 465,505 476,174
Kolfe Keranio 576,443 589,655
Nifas Silk Lafto 424,215 433,939
Kirkos 297,485 304,303
Gulelle 359,933 368,183
Addis Ababa population by sub city
Large population…green…

17. 8. Combination/Combo Chart
▪ Limit the number of series and keep each chart
simple
▪ Use similar scale to improve the graph readability,
similar scale for both axis
▪ Use multipleY-axis to show different scale
▪ Combo Charts are used to
visualize a correlation between
two data sets.
When to use it? Best practice

18. 9. Scatter Plot
▪A scatterplot shows the relationship
between two numerical variables plotted
along both the horizontal and vertical
axis.
▪It is used when you want to see a
relationship analyze the correlation
between two variables
When to use it?
Best practice
▪ Label key data points directly
▪ Only used for numerical data.
▪ Use when there are more than 10
data points on the horizontal axis

19. III. Summary of Quantitative data visualization
Line, Area,
Distribution
Scatter,
bubble, trend
Bar, Stacked
(%),
Pie chart
Composition to show
how individuals parts
make up the whole
Distribution: A whole
decomposed in
quantitative ranges
to understand
patterns
Correlation: To
understand how two
or more variables
relate on specific
dimensions
Relationship
Comparison: To
compare
performance of
several items at
specific points in time

20. Reference
• Data Visualization: How To Tell A Story With Data
https://www.forbes.com/sites/nicolemartin1/2018/11/01/data-
visualization-how-to-tell-a-story-with-data/?sh=4dea01e54368
• Telling a story with data
https://www2.deloitte.com/us/en/insights/deloitte-
review/issue-12/telling-a-story-with-data.html
• https://www.kdnuggets.com/2021/02/telling-great-data-story-
visualization-decision-tree.html
• Different websites and books