Data storytelling
BUS5AP Analytics in Practice
Stu Black
Agenda
• Welcome
• Guest Speaker – Alon Ellis
• Assignment 1 Marks – a Stakeholder Expectations Learning Opportunity
• Data Storytelling
• For Next Week
• Open Q&A
• Close
Agenda
• Welcome
• Guest Speaker – Alon Ellis
• Assignment 1 Marks – a Stakeholder Expectations Learning Opportunity
• Data Storytelling
• For Next Week
• Open Q&A
• Close
Agenda
• Welcome
• Guest Speaker – Alon Ellis
• Assignment 1 Marks – a Stakeholder Expectations Learning Opportunity
• Data Storytelling
• For Next Week
• Open Q&A
• Close
Expectation Mgmt… a Learning Opportunity• All Assignment 1 papers have been marked… hopefully moderation will have been completed and you
should have your marks accessible
• A large part of this subject is about expectation management‐ Setting expectations through articulating a proposal‐ Delivering against that expectation‐ Learning the gotchas so that you can avoid them on your journey
• You have a rare advantage in this course… I tell you my expectations in advance… I publish the marking rubric
• My suggestion… if you got less than 27 / 30 marks… undertake a self-assessment‐ Review the rubric… read it carefully‐ Assess your own paper against that rubric. (Please be sufficiently self-critical)… what would your
mark, if you marked your paper against the published rubric ‐ Review the marks (and the comments) provided in the LMS and granularly compare against your
self-assessment. Identify specific deviations and/or broad trends‐ Develop a view as to what has driven the difference in expectations, and determine what you will do
in future assignments to close that expectation gap
Agenda
• Welcome
• Guest Speaker – Alon Ellis
• Assignment 1 Marks – a Stakeholder Expectations Learning Opportunity
• Data Storytelling
• For Next Week
• Open Q&A
• Close
Why story telling?
• From your selected BadViz…
• How effective was it?
• What would Tufte say about this?
Black’s Analogue to the Theory of Information
If the receiver can determine the
next bit of a bitstream, then the
message carries no information
If you present a visual to a decision maker, and that decision maker
takes no action, did you create any value?
“Insights” or Action-Inducing Insights
If you present a
visual to a decision
maker, and that
decision maker takes
no action, did you
create any value?
• How many of you said (in Assignment 1) that
you would deliver:
‐ “Insights”?
‐ “Visualisations”?
‐ “Descriptive Analytics”?
• Did you articulate the potential actions that
would be taken once the insights were
delivered?
A real-world example – SMSF holdings
What action should I take?
SMSF worked through
Why story telling?
• Humans are wired for story
• A successful story produce and neutralise anxiety
• Not just data scientists, most professions need story telling skills
• Examples
• “Families are doing it tough” but a liberal government will reduce your cost of
living by [you can put anything here literally]
• “Our attrition rate has been increasing YoY and above national average” but
we can [develop an intervention program to improve retention]
What is data storytelling?
• Example: describe a corgi to someone
• Is it easier with numerical raw data, or simply a photograph?
• The same applies to data
• A visual (e.g., graph) is usually better than showing the raw numbers
• Creating a graph is not data storytelling
• Data storytelling requires combination of data, graphs, key observations and
conclusions that are linked through a narrative
Data storytelling
• A good narrative enables one to go beyond the relay of information
• It becomes a powerful mechanism of persuasion
• Gets a point across to the audience quickly with aid of data
• Improves retention of the key information that you want audience to
remember (up to 22x)
• BUS5VA taught you some good practices
Some good tips
• Use the right data
• Is it out of date?
• Is it open to interpretation, or questioning?
• Is the data suitable for the intent?
• Synthesise
• Use combination of data, or focus on individual parts to drive a point
• Example: contrast current wage growth over past wage growth in your
discussion of housing affordability
Some good tips (cont’d)
• Make it personal and real
• How does a lawyer convince the jury?
• Example: discuss housing affordability in Melbourne
• Don’t overload your narrative
• Learn from Hollywood movies
• Never try to say too much
Story structure
• Start with the three “knows”
• Know what you want to say
• Know what your data is saying (and if it supports the first “know”)
• Know what you audience want to hear
• Sketch out your story
• Stick to good data storytelling genres
Story telling genres
• Linear logic
• Start at the beginning and move
linearly to the conclusion
• Reverse of the above
• Change over time
• Linear but time-driven
• Flow diagrams and road maps
Story telling genres
• Linear logic
• Start at the beginning and move
linearly to the conclusion
• Reverse of the above
• Change over time
• Linear but time-driven
• Flow diagrams and road maps
Story telling genres
• Linear logic
• Start at the beginning and move
linearly to the conclusion
• Reverse of the above
• Change over time
• Linear but time-driven
• Flow diagrams and road maps
Story telling genres
• Compare and contrast
• But make sure you are comparing
apples to apples
• Progressive depth
• News are usually written this way
• Personalisation
• People are interested in things
related to them
Story telling genres
• Compare and contrast
• But make sure you are comparing
apples to apples
• Progressive depth
• News are usually written this way
• Personalisation
• People are interested in things
related to them
Story telling genres
• Compare and contrast
• But make sure you are comparing
apples to apples
• Progressive depth
• News are usually written this way
• Personalisation
• People are interested in things
related to them
A good visualisation
Good storytelling doesn’t require
the construction of complex
visualisations
Often, the persuasion we chase
doesn’t warrant the investment
or time.
Better data storytelling with good visuals
• Many large organisations develop their own “best practices” that guide you to
making good visuals
• Most of the time, they are guides rather than hard rules
• Some good starting points
• Create focal points
• Eliminate clutter and confusion
• Have a takeaway message, or call to action
• Think different
Create a focal point
Eliminate clutter and confusion
Have a takeaway message
Click image to open solution templates
Think different
Exercise
Exercise
Exercise
Exercise
• What is the graphic seeking to
communicate?
• What is right with this
visualisation, and what is wrong?
• What should be done differently?
Exercise
And there’s the usual best practices
• Less is more
• More is more (sometimes)
• Keep it simple
• Sometimes, fancy is just that -
fancy
Summary
• Good analytics need good data storytelling to go with it to be successful
• A few good principles to follow
‐ Start with the three “knows”
‐ Think of the structure of your data storytelling
‐ Think of the action that you want the reader to take
‐ Identify relevant best practice
‐ Develop visuals, using those identified best practices
• Keep practicing
Agenda
• Welcome
• Guest Speaker – Alon Ellis
• Assignment 1 Marks – a Stakeholder Expectations Learning Opportunity
• Data Storytelling
• For Next Week
• Open Q&A
• Close
For Next Week…
• Research and Identify at least one Project Management “Fails”
• Identify the following
‐ What Project management Approach did they use?
‐ What were the challenges that project faced?
‐ What could they have done differently to avoid the ‘fail”?
Agenda
• Welcome
• Guest Speaker – Alon Ellis
• Assignment 1 Marks – a Stakeholder Expectations Learning Opportunity
• Data Storytelling
• For Next Week
• Open Q&A
• Close
Agenda
• Welcome
• Guest Speaker – Alon Ellis
• Assignment 1 Marks – a Stakeholder Expectations Learning Opportunity
• Data Storytelling
• For Next Week
• Open Q&A
• Close
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