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