Data Storytelling 101: Best Practices and Tips to Build Your Data Stories
Humans are natural storytellers. Storytelling has always been a primary means for transmitting knowledge across groups of people, resulting in the formation of cultures and enabling evolutionary success. And now as we enter the digital age where data is the primary currency, we turn to data storytelling yet again to communicate complex information, using compelling narratives customized for specific audiences to increase engagement and user adoption.
Understanding data storytelling
Data in itself cannot resonate with humans as effectively as stories can. With stories, you get a distinct start, middle, and ending. They have a narrative flow and repetition; they tie information together, providing a framework that the audience can easily follow. While data describes what is happening, stories indicate why it is happening.
That said, it takes specific skills and tools to effectively communicate findings to others and convince them to take action.
What are the skills required to be a good data storyteller?
To be an effective data storyteller, you need to have analytical, UI/UX, data visualization and soft skills such as creativity, communication, and the ability to craft a narrative that will be meaningful to your audience.
· Analytical
skills include knowledge of BI tools,
data analysis, information architecture, and development methodology.
· UI/UX
skills encompasses expertise in cognitive
science, design thinking, color theory, user interface design, prototyping, and
information graphics.
· Data visualization skills covers visual storytelling, visual problem framework and knowledge of various charts.
Best practices to create effective data stories:
Here is a summary which talks about six best practices for building effective data stories:
1. Tailor the story to the audience
There are broadly three types of audiences - strategic, analytical, and operational. Data stories change as per the type of audience.
Strategic
Strategic data storytelling is for decision-makers and senior management. They provide high-level performance measures with snapshots of weekly, daily, and monthly data.
The best practices for a strategic dashboard are as follows:
· Avoid
using advanced visualizations and too many details
· Highlight
outliers.
· Focus
on actionable insights instead of making the dashboard visually attractive.
· Use
thresholds and highlight negative and positive values.
Analytical insights and applications are for mid-management and planning teams. They deliver complex data with rich comparisons. Interactive displays and historical data are included.
Here are the best practices for an analytical dashboard:
· Highlight
insights correctly.
· Use
interactive visualizations.
· Focus on actionable insights instead of making an attractive dashboard.
Operational
Meant for operational teams, this type of data stories monitors constantly changing activities and shows near real-time or real-time data.
To create effective operational dashboards, be sure to:
· Keep
simple but actionable visualizations.
· Avoid
placing too many details.
· Make
the most of pre-attentive attributes.
· Focus
on actionable insights instead of the dashboard’s aesthetics.
· Use thresholds and highlight the positive and negative values.[SS1] [..2] [AG3]
2. Understand the anatomy of a dashboard
The dashboard is crucial in data storytelling as it visually displays critical information needed to attain one or more objectives. Ideally, it should fit on a single computer screen to make it viewable at a glance. InfoCepts recommends including these elements on your dashboard:
·
Corporate branding
·
Dashboard header
·
Date and currency
·
Report area
·
Report header
·
Footer
·
Summary section
· Sequential
(ordered from low to high)
· Diverging
(two sequential colors with a neutral midpoint)
· Categorical
(contrasting colors for individual comparison)
· Alert
(a color to convey a warning), or
· Highlight
(a color for showcasing a particular value).
· Color code: Avoid using a combination of green and red in the same display as color-blind people cannot distinguish color-coded groups of data.
· Use of thresholds: Use red and green to represent negative and positive values respectively.
· Printing: Distinguish the colors for printing. Most printers print in black and white, so consider using contrasting colors.
·
Avoid flashy colors and a
bright background: Use a background color that sufficiently contrasts with the
objects in the graph or table for optimum visibility.
4.
Implement visual design best practices
5.
Pick the suitable chart
·
Use a bar chart, deviation bar
chart, or dual axis bar chart , especially when showing comparisons, patterns,
or relationships.
·
Use a stacked bar chart for
patterns, part-to-a-whole, relationships, proportions, and comparisons.
·
Use a line chart for displaying
quantitative values over a continuous period or intervals, such as when showing relationships or trends.
·
Use a pie chart to show
percentages and proportions between categories.
·
Use a bubble chart to compare
and show relationships between categorized or labeled circles with proportions
and positioning. It can provide an overall picture for analyzing correlations
and patterns.
·
Use a scatter plot to identify
relationships existing between different values.
·
Use an area chart to show the
development of quantitative values over a period or an interval, making it ideal
for displaying relationships and trends.
·
Use a box plot to display
numerical data groups through their quartiles. It can show the data
distribution based on percentiles, minimum, median, and maximum, making it
ideal for descriptive statistics and comparing distributions.
·
Use a Gantt chart or project
timelines for planning and monitoring resource allocation or project
development on a horizontal time scale.
·
Use a HiLow stock or
candlestick chart for financial data to represent high, low, closing, and
opening values.
·
Use a histogram to visualize
data distribution over a given time or continuous interval.
·
Use a Pareto chart to identify
the cause of a loss or problem. A histogram shows the frequency of a problem or
the various problems occurring.
·
Use a polar or radar chart to
compare multiple quantitative variables. It is effective for viewing variables
with similar values or showing if there are some outliers. It is also practical
for highlighting high- or low-scoring variables in a dataset, making it
suitable for showing performance.
6. Use advanced visualizations where needed
Here
is a quick overview of a few advanced visualizations and when to use them in
your data
storytelling:
·
Funnel: Use this to analyze
various trends quickly over several metric values.
·
Micro charts - Use a micro
chart for data
storytelling that needs to show the metric's trend at a
glance without additional details. It can be a bullet, sparkline, or bar micro
chart.
·
Network visualization - Use
this to easily and quickly identify relationships between related clusters and
items. It is best for market basket analysis or visualizing a social network.
·
Media - Use this when you need to present
information through channels like images, video, website content, or audio on
the dashboard. Media can also enhance a dashboard’s look and feel.
With
so much data available within an organization, data storytelling offers the
best way to put a human perspective into the increasingly complex and rapidly
changing world of digital disruption. Learn more tips on becoming an effective
data storyteller from the full version of the InfoCepts
Guide to Becoming a Data Storyteller.
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