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False Narratives and Data Storytelling

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Creating Dashboards and Storytelling with Tableau

Université de Californie à Davis

4.6 (415 notes) | 15K étudiants inscrits

Cours 4 de 5 dans Data Visualization with Tableau Spécialisation

Visualiser le programme de cours

Leveraging the visualizations you created in the previous course, Visual Analytics with Tableau, you will create dashboards that help you identify the story within your data, and you will discover how to use Storypoints to create a powerful story to leave a lasting impression with your audience. You will balance the goals of your stakeholders with the needs of your end-users, and be able to structure and organize your story for maximum impact. Throughout the course you will apply more advanced functions within Tableau, such as hierarchies, actions and parameters to guide user interactions. For your final project, you will create a compelling narrative to be delivered in a meeting, as a static report, or in an interactive display online.

Compétences que vous apprendrez

Storyboarding, Tableau Software, Data Virtualization, Data Visualization (DataViz)

Avis

4.6 (415 notes)

5 stars
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RM

Apr 23, 2018

Its a good Course to get understanding regarding Tableau Story telling ,This course covers start to end aspects of story telling with Tableau.Its a very good course to move forward in Tableau

MM

May 24, 2018

Great course with amazing teachers and material! I especially liked the amount of technical tutorials that are given in this course. I would recommend this to anyone starting out in Tableau.

À partir de la leçon

Tell the Story of Your Data

Welcome to Module 4. Although this course focuses on Tableau, we will look at a wider range of examples and techniques to help you become a better data storyteller. By the end of this module, you should be able to leverage concepts and techniques designed to help you become a more focused and compelling storyteller with data as the foundation. We will discuss ways to avoid unintentionally creating false narratives with good data. You will also learn about what neuroscience research tells us about stories, audience engagement, and decision drivers. You will use structural story elements to help improve the relatability of the story and explore ways design and textual elements can affect the emotional tone of a story. Lastly, you will learn how to frame and format the data story based on your design checklist. Let's get started!

Tell the Story of Your Data Introduction1:37

The Art of Storytelling (Past, Present, and Future)3:27

Storytelling and the Human Brain5:28

Bringing Data to Life: Emotions and Data Storytelling7:14

Emotion Modulators: Color, Language, and Other Design Elements8:24

Framing and Format4:11

Framing, Conventions, and Priming8:12

False Narratives and Data Storytelling6:51

Telling the Stories within the Story (Part 1)5:29

Telling the Stories within the Story (Part 2)4:45

Story Considerations6:27

Preparation of the Story Points11:07

Setting Up the Story14:24

Creating Points in the Story16:18

Interactive Story6:36

Create a Data Story in a Static Presentation20:20

Enseigné par

Govind Acharya
Principal Analyst
Hunter Whitney
Sr. Consultant, Author, Instructor

[MUSIC]

Welcome back.

The human brain loves stories, but this is a double edged sword.

Sometimes we see a story where there isn't one.

As a data storyteller, you have a lot of potential power and responsibility.

How you select, arrange and

present the facts can change the way people think or how they view the world.

You should strive to make your data stories reflect reality, however,

even if you have the best intentions that's not always enough.

In the process of assembling your data stories,

we have to be diligent to avoid creating false narratives.

You need to check your own unconscious cognitive biases and

consider potential biases in your audience.

That's what we'll be examining next.

After this lesson, you will be able to recognize cognitive biases and

enact counter-measures to guard against them.

You will also be able to take steps to avoid presenting false narratives.

Let's get started.

Hi, and welcome back.

Data visualizations reinforce our innate tendency to find patterns and

convert them into stories.

If we see what appears to be a close correlation between two things,

there can be an almost automatic attempt to find or

create a story to explain that apparent connection.

For example, look at these charts, which I'm partially reviewing,

so you can focus on the lines.

There seem to be some pretty close relationships, and

some interesting details like the inflection points in the second chart.

What's the story within that?

Well, nothing really.

These examples come from the amusing site, Spurious Correlations.

Here's a full view of that first chart.

The relationship being displayed is between worldwide non-commercial

space launches and awarded sociology doctorates.

And here's the second chart.

We see a pattern of the two things being measured.

In this case, the age of Miss America and murders by steam, hot vapors and

hot objects.

They seem to track to each other very closely, and

then we have what might be perceived as interesting inflection points,

all of which are ultimately meaningless.

What makes these ridiculous combinations so great is precisely that,

well, they're so ridiculous.

We don't need to be subject matter experts to know intuitively that these

relationships are false.

However, there are many instances where it may not be so

obvious at all whether a correlation is meaningful.

Visualizations can make these correlations seem more intriguing and

tangible, and so the patterns get turned into false narratives.

This may be an even greater danger and challenge if you are not a subject

matter expert in the domain from which your story is being created.

I found myself in that situation, as perhaps some of you will as well.

Part of the process of guarding against false narratives involves talking to

subject matter experts or SMEs as we touched on in Module One of this course.

It's also a good idea to do some outside research whenever possible

as well as consider other data sources to help you verify that you're not

unintentionally creating fiction.

It's all too easy to impose a story on a disconnected set of facts that

appear to relate to each other.

These are sometimes called, just so stories, or

in some contexts, ad hoc fallacies.

The term, just so stories,

comes from the title of a children's book by the author, Rudyard Kipling,

with whimsical tales of how some animals developed certain physical attributes.

But these kinds of observations can be quite useful, and

it can sometimes be used as hypotheses for further testing and investigation.

However, stories that oppose an unverifiable explanation can be

very problematic.

The bottom line is that people can create all kinds of origin or

causative tales about patterns that they see.

But huge expansion of data to analyse and visualizations like Tableau to work

with data, increases the opportunity for creating both true and false data stories.

Now, along with the tendency to create false narratives from unconnected

patterns and data, there are a number of ways that data storage can be

unintentionally twisted by their creator's cognitive biases.

Basically, cognitive biases are tendencies to think and make judgements based on our

own personal set of perspectives, experiences and filters.

And that may not be reflective of the true situation.

In general, there are a number of counter measures against forming cognitive biases.

The first step is cultivating an awareness of the potential for

cognitive bias in yourself and others.

After that, for

any data story that you create, be sure to consider other possibilities.

Even stories that seem diametrically opposed to your own ideas and conclusions.

Additionally, whenever possible, it can be very helpful to test early versions of

your data stories on people who are in, or are similar to, your target audiences.

You may find that the stories evoke common responses

that may not have been your original intention or anticipation.

If possible, it can also be a useful exercise to test

out the story in a variety of people outside of the target audience.

There are many potential biases and

logical errors that most of us can fall into.

The following info graphic catalogs and categorizes quite a few of them.

Now, do I think you need to memorize all of these to become a good data

story teller?

No, however, it is important to develop an appreciation for

the many ways that we can fool ourselves and others with false stories.

That way you can be alert throughout your story development process.

Why am I putting so much emphasis on bias in this lesson?

Well, data stories are the place where either truth or

fiction can be vividly brought to life and disseminated to many others.

I hope all of your data stories are engaging and accurate ones.

Well, this wraps up our view of being alert to the dangers of

creating false narratives.

In our next lesson, we will revisit the tale of 100 entrepreneurs, and

walk through it to learn how to tell a specific story

from a data set that contains many potential stories.

Thanks, and see you again soon.

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